using adams/insight - md adams 2010

About Adams/InsightAdams/Insight, part of the MD Adams 2010® suite of software, is a powerful design-of-experiments software. Adams/Insight is a stand-alone product that also works with many other Adams products. Adams/Insight lets you design sophisticated experiments for measuring the performance of your mechanical system. It also provides a collection of statistical tools for analyzing the results of your experiments so that you can better understand how to refine and improve your system.

Within the Adams analysis environment, there are conduits between Adams/Insight and the other Adams products (for example: Adams/Car and Adams/Chassis). These conduits streamline the process by taking advantage of the inherent parametric strengths of the vertical application.

When these parametric applications are not accessible, you can use the Adams/Insight ASCII Conduit (Adams/Insight ASC). It provides the power of a streamlined parametric investigation process for systems that are defined by text files. For example, if you only have an .adm and .acf file of an analytical Adams/Solver system, you could use Adams/Insight ASC to easily execute various Adams/Insight investigation strategies. Adams/Insight ASC has an editor that enables you to import the ASCII files (.adm, .acf) and turn them into ASC templates, which together define an ASC system.

Adams/Insight ASC is a general-purpose tool that helps you work with various analysis environments, from your own or inhouse-developed applications to commercial applications which accept an ASCII input deck

What Is Experimental Design?

This section covers the following topics:

• Overview

• Process

• Analysis

• Example

OverviewExperimental design (also called Design of Experiment (DOE)) is a collection of procedures and statistical tools for planning experiments and analyzing the results. In general, the experiments measure the performance of a physical prototype, the yield of a manufacturing process, or the quality of a finished product.

Although experimental design techniques were originally developed for physical experiments, they also work very well with virtual experiments. In the case of Adams/Insight, the experiments help increase the reliability of your conclusions, get you answers faster than trial-and-error or testing factors one at a time, and help you better understand and refine the performance of your mechanical system.

For simple design problems, you can explore and optimize the behavior of your mechanical system using a combination of intuition, trial-and-error, and brute force. As the number of design options increase,

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however, these methods become ineffective in formulating answers quickly and systematically. Varying just one factor at a time does not give you information about the interactions between factors, and trying many different factor combinations can require multiple simulations that leave you with a great deal of output data to evaluate. To help remedy these time-consuming tasks, Adams/Insight provides you with the planning and analysis tools for running a series of experiments. Adams/Insight also helps you to determine relevant data to analyze, and automates the entire experimental design process.

ProcessThe experimental design process includes five basic steps:

• Determine the purpose of the experiment. For example, you might want to identify which variations most affect your system.

• Choose a set of factors for the system that you are investigating and develop a way to measure the appropriate system responses.

• Determine the values for each factor (called Levels), and plan a set of experiments (called runs or trials) in which you vary the factor values from one trial to another. The combination of actual runs to perform is called the design.

• Execute the runs, recording the performance of the system at each run.

• Analyze the changes in performance across the runs, and determine what factors most affect your model.

An experiment configured using this process is called a designed experiment, or matrix experiment. The runs are described by the design matrix, which has a column for each factor and a row for each run. The matrix entries are the levels for each factor per run.

Experiments with two or three factors might only require five or ten runs. As the number of factors and levels grows, however, the number of runs can quickly escalate to dozens, even hundreds. As a result, a good design is critical to the success of the experiment. It should contain as few runs as possible, yet give enough information to accurately depict the behavior of your system. The best design depends on the number of factors and levels, the nature of the factors, assumptions about the behavior of the product or process, and the overall purpose of the experiment. Adams/Insight lets you combine all of these requirements into an efficient, effective design for your problem, and help you make accurate analyses of the results.

AnalysisThe type of analysis you’ll run depends on the purpose of the experiment. Common analyses include Analysis of Variance (ANOVA), which determines the relative importance of the factors, and Linear Regression, which fits an assumed mathematical model to the results.

3Adams/Insight ASCII Conduit

ExampleIf a simple experiment includes two factors, each with three Levels and four runs, the design matrix for the experiment might look like this:

Each row of the matrix represents a run, and each column represents a factor. A -1 indicates the first level for the factor, a 0 the second, and a +1 the third.

If the levels for the first factor are 9, 10, and 11, and the levels for the second factor are 85, 90, and 95, then the matrix would give the following runs:

Adams/Insight ASCII Conduit

Within the Adams analysis environment, there are conduits between Adams/Insight and the other Adams products (for example: Adams/Car and Adams/Chassis). These conduits streamline the process by taking advantage of the inherent parametric strengths of the vertical application.


Adams/Insight ASC is a general-purpose tool that helps you work with various analysis environments, from your own or inhouse-developed applications to commercial applications which accept an ASCII input deck.

Run Factor 1 Factor 2

1 10 95

2 9 90

3 11 85

4 11 95

0 1

1– 0

1 1–

1 1

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References

DOE• Grove, D. M., and T. P. Davis. Engineering Quality Experimental Design. ISBN 0-582-06687-5.

• Cornell, John A. How to apply Response Surface Methodology. American Society for Quality Control Statistics Division, Volume 8. ISBN 0-87389-066-3.

• Myers, Raymond H., and Douglas C. Montgomery. Response Surface Methodology. Wiley Inter Science. ISBN 0-471-58100-3.

• Box, G. E. P., and D. W. Behnken. Some New Three Level Designs for the Study of Quantitative Variables. Technometrics, Vol. 2, No. 4, November 1960.

• Gentle, James E., Random Number Generation and Monte Carlo Methods. Springer-Verlag, 1998.

• Introduction to Monte Carlo Methods. http://www.phy.ornl.gov/csep/CSEP/MC/MC.html.

• Numerical Recipes. http://www.nr.com.

• Greenwood, W.H. and Chase, K.W., A New Tolerance Analysis Method for Designers and Manufacturers, ASME Journal of Engineering for Industry, Vol. 109, pp. 112-116, May 1987.

Python• http://www.python.org/

• Beazley, David M., Python Essential Reference. ISBN 0-735-70901-7.

• Lutz, Mark. Python Pocket Reference. ISBN 0-596-00189-4.

• To run Python you can run from mdadams2010 environment 'mdadams2010 -c python'

Regression/RSM• Draper, Norman R., and Harry Smith. Applied Regression Analysis. John Wiley & Sons, 1998.

ISBN 0-471-17082-8.

• Box, George E. P., and Norman Richard Draper. Empirical Model-Building and Response Surfaces. John Wiley & Sons, 1987. ISBN 0-471-81033-9.

• Montgomery, Douglas C., and Raymond H. Myers. Response Surface Methodology: Process and Product in Optimization Using Designed Experiments. John Wiley & Sons, 1995. ISBN 0-471-58100-3.

Statistics/Distributions• NIST/SEMATECH e-Handbook of Statistical Methods,

http://www.itl.nist.gov/div898/handbook/index.htm

http://www.phy.ornl.gov/csep/CSEP/MC/MC.html

http://www.nr.com/

http://www.python.org/

http://www.itl.nist.gov/div898/handbook/index.htm

5References

XML• Eckstein, Robert, XML Pocket reference. ISBN 1-56592-709-5.

• Harold, Elliotte Rusty, and W. Scott Means, XML in a Nutshell. ISBN 0-596-00058-8.

• http://www.w3schools.com/

http://www.w3schools.com/

Adams/Insight6

Learning the Basics

Adams/InsightStarting Adams/Insight

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Starting Adams/InsightThere are two ways to start Adams/Insight:

• Start Adams/Insight and open an existing experiment.

• Start Adams/View, run a simulation, and then export the results to Adams/Insight.

Start Adams/Insight and open an existing experimentIf you've already created an experiment, you can open Adams/Insight directly.

To start Adams/Insight:

1. Do one of the following:

• On UNIX, type the command to start the Adams Toolbar at the command prompt, and then press Enter. Select the Adams/Insight tool.

• On Windows, from the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to AInsight, and then select Adams - Insight.

The Adams/Insight main window appears.

2. Open your existing experiment.

Start Adams/View, run a simulation, and then export the results to Adams/InsightThis section will explain how to start Adams/View, create a modeling database, run a simulation, and export the results to Adams/Insight.

To start Adams/View:

• Do one of the following:

• On UNIX, type the command to start the Adams Toolbar at the command prompt, and then press Enter. Select the Adams/View tool.

• On Windows, from the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to Aview, and then select Adams - View.

The Adams/View main window appears.

To create a modeling database:

1. In the Welcome dialog box, select Import a file.

If the Start in text box doesn’t show the path to your working directory, select the Browse button. Use the Select File dialog box to navigate to your working directory, and then select OK.

2. Select OK in the Welcome dialog box.

The File Import dialog box appears.

3Learning the BasicsStarting Adams/Insight

3. In the File to Read text box, enter the name of your command file, or right-click and select Browse. You can then use the Select File dialog box to open the file.

4. Select OK.

Adams/View imports the file, and then displays the model.

To simulate the model:

1. From the Main toolbox, select the Simulate tool. The options for running a simulation appear in the Main toolbox.

2. Specify the simulation options, as needed.

3. Select the Start tool, and wait for the simulation to finish.

Adams/View runs the simulation.

To start Adams/Insight from Adams/View:

1. From the Simulate menu in Adams/View, point to Adams/Insight, and then select Export.

The Adams/Insight Export dialog box appears.

2. In the Experiment text box, enter a name for your experiment or use the default.

3. In the Model text field, enter the name of your model, or use the default.

You can also browse for the model name in the Database Navigator. Right-click in the text box, point to Model, and then select Browse. Select the model in the Database Navigator, and then select OK.

4. In the Simulation Script text box, enter the name of your simulation script or use the default.

You can also browse for the script name in the Database Navigator. Right-click in the text box, point to Simulation_Script, and then select Browse. Select the model in the Database Navigator, and then select OK.

5. Optionally, enter the name of an existing experiment in the Reuse Experiment text box.

You can also browse for the experiment in the Database Navigator. Right-click in the text box, point to Experiment, and then select Browse. Select the model in the Database Navigator, and then select OK.

If you enter an experiment to reuse, Adams/Insight reuses as many components of the old experiment as possible in the new experiment. More about the Reuse Tool.

Note: You can also display the Database Navigator by double-clicking in the Model or Simulation Script text box.

Note: The new experiment cannot have the same name as the old experiment. Adams/View will rewrite the new experiment file, erasing any old information in the file. To reuse an old experiment, use a different name for the new experiment.

Adams/InsightStarting Adams/Insight

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6. Select OK.

Adams/View writes your model information to an experiment file and launches Adams/Insight. The experiment file will have the same name as the experiment, and will be written in the current Adams/View working directory.

If you reused an old experiment and Adams/Insight is able to use it to create a work space for the new experiment, Adams/Insight immediately adds the work space to the new experiment file and returns to Adams/View. Adams/View will then run the experiment.

If you did not reuse an old experiment, or if Adams/Insight is unable to automatically create a work space using the old experiment, the Adams/Insight main window appears.

On Windows, Adams/View opens a command prompt window to launch Adams/Insight. This window stays open until you exit Adams/Insight and return to Adams/View. Do not manually close the command prompt window.

Opening Recently Used FilesAdams/Insight remembers the most recently used experiments in your system. You can use this feature to open an active experiment without having to remember its specific directory path and filename.

To open a recently used experiment:

• From the File menu, point to Recent Files, and then select the experiment you want to open.

Adams/Insight opens the experiment.

Setting PreferencesYou can set preferences that will be used for all Adams/Insight experiments. You can define properties for design, fit, optimization, and thresholds.

To set preferences:

1. From the Edit, menu select Preferences.

2. Complete the dialog box as described in Preferences.

3. Select OK.

Setting the Working DirectoryBy default, Adams/Insight saves all files in the directory from which you ran Adams/Insight. You can change the working directory.

To change the working directory for the current session:

1. From the File menu, select Select Directory.

2. Select the directory in which Adams/Insight should save files.

5Learning the BasicsStarting Adams/Insight

3. Select OK.

Adams/InsightAdams/Insight Main Window

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Adams/Insight Main Window This is the window that appears when you first launch Adams/Insight. Learn more about toolbars and Treeview.

7Learning the BasicsAdams/Insight Main Window

• Menu Bar: The Menu bar contains pull-down menus for File, Edit, Define, Simulation, Tools, and Help.

• Toolbars: The toolbars contain commonly-used tools for accessing files, creating experiments, and generating reports. The tools in the toolbars are arranged in the order that you use them in the process of creating and executing your designed experiment. Depending on where you are in the process of creating an experiment, Adams/Insight enables or disables the tools. This feature alerts you to the correct order of procedures to follow.

• Treeview: The treeview displays a hierarchical list of objects that you can include in an experiment. The tree is especially useful in selecting and identifying objects when you are creating a design matrix.

• Viewport: The Viewport is the area of the window that displays fields for modifying the objects you select from the treeview.

• Status bar: The Status bar displays messages and issues prompts during your Adams/Insight session.

Adams/InsightAbout the Toolbars

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About the Toolbars The Adams/Insight toolbars appear at the top of the window by default. (Learn how to move the toolbars to other parts of the window.) The tools in the toolbars are arranged in the order that you use them in the process of creating and executing your experiment. Depending on where you are in the process of creating an experiment, Adams/Insight enables or disables the tools. This feature alerts you to the correct order of procedures to follow. For example, the Run simulations tool is disabled until you define the required elements for a design matrix.

The Adams/Insight main window has the following toolbars:

• Experiments

• Experiment contents

• Work Space

• Report

Experiments ToolbarThe Experiments toolbar lets you execute basic commands. It includes the following tools:

Experiment Contents ToolbarThe experiment contents toolbar helps you build and execute your experiment. It includes the following tools:

Tool: Purpose: Description:

New Creates a new experiment.

Open Opens an existing experiment.

Save Saves your current experiment.

Delete Deletes the selected item.


Add FactorCreates a new factor in your experiment and displays the Factor Form for you to complete.

Add ResponseCreates a new response in your experiment, and displays the Response form for you to complete.

Promote

Promotes items to the inclusion list. From the Candidates section of the treeview, select the factor(s) and/or response(s) that you want included, and select the Promote tool. The items are moved from Candidates to Inclusions, and are now part of your design matrix.

9Learning the BasicsAbout the Toolbars

Work Space ToolbarThe work space toolbar lets you execute commands on the work space. It includes the following tools:

Demote

Demotes items to the candidates list. From the Inclusions section of the treeview, select the factor(s) and/or response(s) that you want excluded, and select the Demote tool. The items are moved from Inclusions to Candidates, and are no longer part of your design matrix.

Tie Factors Ties factors together. Learn about on tying factors.

Untie Factors Unties factors. Learn about Tying Factors.

Create Design Specification

Sets the design specifications for your experiment. Learn about the Design Specification form.

Create Work Space

Creates the work space for your experiment. Learn about the Design Work Space form.

Run Simulation Runs the simulation for each trial in your work space matrix.

Fit Results Fits the results of your experiment. Learn about fitting results.

Export

Exports the results from your experiment to a file using these file formats:

• HTML - Creates an HTML-format Web page

• SYLK - Creates a symbolic Link (SYLK) format spreadsheet file

• Visual Basic - Visual Basic subroutines

• MATLAB - MATLAB M-File

Learn more about Exporting Results.

Optimize Optimizes your experiment. Learn about Optimize Model or Experiment.


Work Space Display

The Work Space is a matrix with the rows indicating the runs and the columns identifying the factor settings and resulting response values in engineering units. It is sometimes referred to as the run matrix. Learn more about Design Work Space

Work Space Review

The Work Space Review allows you to perform preliminary investigations of the raw data from the work space. This can be achieved by graphically reviewing the histograms which depict the distribution of the column values. Learn more about Design Work Space Review.



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Report ToolbarThe report toolbar lets you generate and export a report. It includes the following tools:

Setting Up Toolbars You can turn the display of the toolbars on or off and set where they appear. By default, all toolbars are displayed at the top of the window. To set the placement of these objects, see Moving Toolbars.

Work Space - Scatter Plots

Allows you to view the raw data plotted against another variable. You can also plot the raw values against the trial count.

Work Space Correlation Matrix

Correlation can be used to measure the potential strength of relationship or lack of relationship between two variables. Learn more about: Work Space Correlations.

Work Space Column Calculator

Enables you to perform mathematical operations on the columns of an existing workspace. Learn more about: Work Space Column Calculator.


Create snapshotTake a picture of the active application window and save it to an image file.

Display last exported item

Display the last saved or exported file. For example, if you just saved the experiment, using this tool displays the .xml file in a browser. If you just exported a work space, use this tool to display the work space .csv file in your default spreadsheet program.


11Learning the BasicsAbout the Toolbars

To turn a toolbar on or off:

1. Right click one of the toolbars in the top of the window to display the toolbar selection menu.

2. Select the item from the toolbar selection menu:

• If the item is not currently displayed (no checkmark in the menu), it will now display in the window.

• If the item is currently displayed (with a checkmark in the menu), it will be turned off.

Moving ToolbarsYou can move the Adams/Insight toolbars to other areas of the screen.

To move a toolbar:

1. Put your cursor on the divider for the toolbar you want to move.

This item in the selection menu: Controls this toolbar:

Experiments

Experiments Contents

Work Space

Report

Line upIf you select Line up from the toolbar selection menu, Adams/Insight takes all toolbars and aligns them in the top left corner of the window.


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2. Drag the cursor until the toolbar is at the desired location.

Running Experiments

Adams/InsightAnalysis

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Analysis You can view the properties of each model in your experiment using the Model Properties form. You can view statistic categories on the following:

• Regression Summary

• Response Summary

Regression Summary The regression summary displays a summary of statistics for the entire model. You can view the following statistics for your model:

• Properties

• Rules Summary

• Goodness of Fit

• Term Significance

• Studentized Residuals

• Cook’s Statistics

• Term coefficients

• Beta (standardized coefficient)

• Residuals

• Estimates

• Minimum and maximum estimates

Response Summary The response summary displays a summary of statistics for a specific response in your experiment. You can view the following statistics for the response:

• Fit Table

• Term

• Residuals

• Condition

• Minimum and maximum

• Plot - Raw residuals vs. Estimates

• Plot - Responses vs. Trials

• Plot - Studentized residuals vs. Trials

• Histogram - Raw residuals

3Running ExperimentsAnalysis

• Histogram - Studentized residuals

Adams/InsightDesigns

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Designs

Design Specification

The Design Specification form is where you define the details of your experiment. Some details are:

• DOE Design Types

• Investigation Strategy

• Model

DOE Design Types Using Adams/Insight, you can import your own design matrix, or use a selection of built-in design types to help you create a design matrix. These options allow you the freedom to create the most effective experiment for your system.

When you use built-in designs, Adams/Insight generates a design matrix according to specifications of the design type. Design types include:

• Full Factorial

• Fractional Factorial

• Plackett-Burman

• Box-Behnken

• Central Composite Faced (CCF)

• D-Optimal

• Latin Hypercube

Full Factorial

Full Factorial is the most comprehensive of the design types and uses all of the possible combinations of

Levels for your factors. The total number of runs is mn, where m is the number of levels and n is the

number of factors. Since the values for mn increase very quickly, Full Factorial is only practical for an experiment with few factors.

The Full Factorial algorithm can produce mixed-level designs that have a different number of values for each factor. Mixed-level designs can occur when you have discrete variables, which take on values from a fixed list. This contrasts with continuous variables, which take on arbitrary values that are usually constrained to a range. For example, a mixed-level design might have two Design Variables one with two levels and one with three levels. The number of runs for such a design is 2 * 3 = 6. In general, to compute the number of rows in a Full Factorial design, just multiply the number of levels of each design variable.

Fractional Factorial

Fractional Factorial and Plackett-Burman designs are referred to as reduced factorial designs. They are popular for screening important variables and are used principally with two-level factors. They enable

5Design SpecificationDesigns

you to estimate the effects on your system and, depending on the number of factors and the number of runs, estimate either none, some, or all of the two-factor interactions.

They are appropriate for two-level screening experiments when you are primarily interested in identifying the most significant factors (main effects) affecting the responses under investigation. As a subset of Full Factorial, these designs require fewer Trials, but may result in confounding of factor interactions with main effects. You should use these designs with the Screening method of experimental design, not RSM. Learn more about Screening and RSM.

These design types let you specify the number of trials in certain conditions. For example, for four factors and a linear model the only possible number of trials is 8. For five factors and a linear model one would have 8 or 16 trials.

The number of runs for a Fractional Factorial design must be a power of two (4, 8, 16...).

Plackett-Burman

Plackett-Burman designs are useful for screening a large number of factors to find the most important ones. These designs require the fewest runs of any classical design type, but do not allow you to estimate the interactions between factors.

The number of runs for a Plackett-Burman design must be a multiple of four (4, 8, 12, ..., 48).

Box-Behnken

Box-Behnken designs use points on planes of the design space as shown in the diagram below. A Box-Behnken design requires relatively few trials. For example, a 12-factor design has 192 rows with 12 center points, for a total of 204 trials. Even though the number of trials is low, the results yield information on factor interaction, which makes these designs appropriate for RSM experiments in which the model type is quadratic. Box-Behnken designs require using each factor at three levels, and are available for designs with 3, 4, 5, 6, 7, 9, 10, 11, 12, or 16 factors.

Box-Behnken Design with Three Factors and Three Levels:


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Central Composite Faced (CCF)

CCF designs use points on each factor axis (star points) in addition to points at the corners of the design space (cube points) and one or more center points, as shown in the diagram below. The CCF design produces a relatively greater number of runs than a Box-Behnken design, and is applicable to the same type of problems.

You can use CCF designs for RSM experiments in which the model type is quadratic. Standard CCF designs use the Fractional Factorial or Full Factorial design for a subset of factors in the experiment (in Adams/Insight, the subset is always Full Factorial). For remaining factors outside of the subset, CCF designs use additional points that estimate quadratic effects. These designs allow high-quality prediction over the entire factor space.


CCF Design with Three Factors and Three Levels:

D-Optimal

The D-Optimal design produces a model that minimizes the uncertainty of coefficients. This design consists of a random collection of rows from a larger pool of Candidates that are selected using minimization criteria. D-Optimal designs let you specify the total number of runs in an experiment, supply existing rows from a previous experiment into a new experiment, and specify a different level for each factor. These features make D-Optimal designs the best choice in many situations, especially when experiment cost is a significant consideration.

D-Optimal designs extend to larger design matrices. For example, the more redundant the vectors (columns) of the design matrix, the closer to zero the determinant of the correlation matrix is for those vectors; the more independent the columns, the larger the determinant of that matrix is. Therefore, finding a design that maximizes the determinant D of this matrix means finding a design where the factor effects are maximally independent of each other.


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Latin Hypercube

A Latin Hypercube design uses as many values as possible for each factor. Each factor's values are randomly ordered so that each run has a random combination of factor values.

Continuous factors have a different value for each run. The values are equally spaced, running from the minimum value to maximum factor value. Discrete factors have a fixed number of values. If there are more runs than discrete values, there will be runs with duplicate factor values. If there are fewer runs than discrete values, then not all values will be used.

The Latin Hypercube design is similar to a Sweep Study design, except that the factor values in each column are randomly ordered instead of uniformly sweeping from the minimum value to maximum value.

Investigation Strategies The investigation strategies (methods) for creating a design matrix in Adams/Insight include:

• Study - Perimeter

• Study - Sweep

• DOE Screening (2 Level)

• DOE Response Surface

• Variation - Monte Carlo

• Variation - Latin Hypercube

The first four strategies in the list reference attributes specified in the Settings tab of the Factor form. The two Variation methods reference attributes in the Variation tab of the Factor form.

Study - Perimeter

This method is used to evaluate the relative robustness of an analytical model. This method is often called Processes Health Check. The system under investigation is exercised at three different configurations:

• In the first trial, all the factors are set to their respective minimum values.

• In the second trial, the factors are set to their intermediate value.

• In the third trial, the factors are set to their respective maximum values.

When first investigating a system, it is good practice to determine the relative robustness of the nominal simulation. The first step in this process is to make sure that the nominal configuration runs well. The next step is to determine the likelihood that variants, of the nominal configuration, will run well. You can use the perimeter study to run three different configurations, which span the design space. The successful running of these three configurations will build confidence that you are working with a robust simulation. Before submitting a series of simulations which you may expect to run overnight, it is important to run a perimeter study to verify that the basic mechanics of building, running, and postprocessing the analytical system performs as expected.


If you choose the Perimeter Study as the Investigation Strategy, the model type will be automatically set to None and there will be no option for fitting the results or subsequently publishing or optimization. This Investigation Strategy is used to determine the relative robustness of the simulation and the simulation process.

Study - Sweep

This method alters the respective inputs over a range. For example, let's say you wanted to alter the initial velocity of a vehicle from 50 to 100 KPH. You would define the initial velocity as a factor with settings 50 and 100. If this is the only factor in the investigation, you could select the sweep study investigation strategy in the Design Specifications form. The Number of Runs specifies how the factor interval will be divided. If you specified six trials, then the simulation would be run at 50, 60, 70, 80, 90, and 100 KPH. If only one factor is in the Factor Candidates List for a sweep study, then you can potentially fit the regression model. If more than one factor has been promoted than the Sweep Study permits, the None option for a regression model type and no subsequent model fitting or publishing of a fit model will be available. Sweep Studies are sometimes referred to as design studies.

DOE Screening (2 Level)

This method identifies the factors and combinations of factors that most affect the behavior of a system. You consider every factor that may potentially affect the response, and use a screening analysis to determine how much each contributes to the response.

A screening Design of Experiment (DOE) only picks high and low values of a setting range, and therefore is often called a two-level analysis. Screening helps narrow down experimentation to important factors and ensures that you do not omit significant factors or effects. Screening is usually followed by a more in-depth experiment, which is typically RSM, on the most important factors.

DOE Response Surface

This method fits polynomials to the results of the Trials in your experiment. The fitting functionality gives you an easy-to-use approximation of your system's behavior and performance. You can use this method for plotting and evaluating, for quick what-if studies, as input for an optimization algorithm, or as a subsystem model for a larger system.

RSM experiments require a greater number of runs than a screening experiment for the same number of factors. Therefore, it is advisable to first run a screening experiment, determine which factors are important, and then run an RSM experiment with this new subset of factors. Some common RSM designs are Box-Behnken, CCF, and D-Optimal.

Quadratic RSM provides three-level analysis because it uses high, low, and average values in the setting range. Cubic RSM uses high, low, 1/3, and 2/3 range values providing a four-level analysis.

Note: If you select one factor for a Sweep or Perimeter Study, you can fit a model to the results. If you have more than one factor, you cannot fit a model, so the Model option in the Design Specifications form is set to None


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Variation - Monte Carlo

This class of methods randomly sets values of the factors for each Trial. The goal of the investigation is to determine the effect of real-world variations upon the performance of the design. With a large number of trials, you can develop statistical predictions of design response.

The foundation of the method involves characterizing parameters with a Probability Density Function (PDF). This function must be specified for each parameter that will be varied in the analysis. Examples of parameters include spring stiffnesses, damping rates, and initial rotation rates.

Variation - Latin Hypercube

This investigation strategy is similar to the Variation - Monte Carlo strategy. The difference is in the sampling logic that generates the factor settings for each trial. The sampling logic for the Variation - Latin Hypercube method uses the modified Latin Hypercube algorithm.

Model In performing a regression analysis, the objective is to fit an equation (referred to as the model) to the data such that the error between the values predicted by the equation and the actual observed values is minimized.

The model can have a constant term, linear terms, quadratic terms, and cubic terms. For example, if there are two factors, the forms are as shown below:

where:

• F1: Value of the first factor.

• F2: Value of the second factor.

• a1-a3: Coefficients computed by the regression analysis.

• e: The remaining error, minimized by the regression analysis.

• R: Response value.

Note: This investigation strategy creates a collection of points, which approximates the specified distribution with fewer trials than the Variation - Monte Carlo method.

Type: Form:

Linear R = a1 + a2*F1 + a3*F2 + e

Interactions R = a1 + a2*F1 + a3*F2 + a4*F1*F2 + e

Quadratic R = a1 + a2*F1 + a3*F2 + a4*F1*F2 + a5*F1^2 + a6*F2^2 + e

CubicR = a1 + a2*F1 + a3*F2 + a4*F1*F2 + a5*F1^2 + a6*F2^2 + a7*F1*F2^2 + a8*F1^2*F2 + a9*F1^3 + a10*F2^3 + e

11Design SpaceDesigns

Design Space

The design space is a matrix with the rows representing the run and the columns representing the factor settings. The settings are in a normalized representation. Learn more about the Design Space form.

Inclusions

Adams/Insight enables you to import a full or partial design matrix whose factor settings will be included when the complete workspace is generated. This is only applicable for D-Optimal design types.

Adams/InsightFactors

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Factors A factor is a variable that you want to vary in your experiment. Factors are the inputs to a system, such as geometric point location user-defined element parameter or a Design Variable. You define your factors using the Factor Form.

Tying Factors• Overview

• Procedure

OverviewWhen you create a tie, you specify the type of the tie, and the corresponding scale or offset value for each tied factor. Adams/Insight then computes the value of the tied factors from the value of the tie.

If the tie type is Scale, Adams/Insight uses the following formula:

current factor value = current tie value * component factor scale value

where:

• Current factor value is the value the factor becomes during the current trial.

• Current tie value is the value the tie assumes for the current trial.

• Component factor scale value is the value entered in the Tied Factors table in the tie Factor Form.

If the tie type is Offset, Adams/Insight uses the following formula:

current factor value = current tie value + component factor offset value

For example, to symmetrically control two factors:

1. Create a Scale tie.

2. Set the nominal value of the tie to the desired positive value.

3. Set the scale of one tied factor to +1 and the other to -1.

If the tie value is 300, Adams/Insight computes the value of the tied factors as +300 and -300.

How to tie factors

To tie factors:

1. Select two factors by holding down the Ctrl key while you select each factor with the left mouse button.

2. Select the Tie factors tool.

3. In the Factor Name text box, change the name to something meaningful.

13ExamplesFactors

4. Review the other factor properties, such as Nominal Values and Settings. If necessary, change them to the desired values.

5. Select the Tie tab.

6. Select Scale or Offset for Tie Type.

7. Enter a scale or offset for each tied factor, then select Apply.

Adams/Insight updates the computed values for the tied factors.

When you create the tie, Adams/Insight sets default values for the tie value, tie settings, and scale values for the tied factors. If the tied factors have the same value, Adams/Insight automatically moves the value and settings to the tie and sets all scales to 1. If the tied factors have the same absolute values, Adams/Insight automatically makes the tie the positive value, and sets all scales to +1/-1. Otherwise, Adams/Insight sets the tie value to 1 and the tied factor scales to the tied factor nominal values.

To untie factors:

1. Select the tie in the treeview.

2. Select the Untie factors tool.

3. Adams/Insight displays an alert asking whether you want to restore the original factor values.

4. Select Yes or No.

Adams/Insight deletes the tie and moves the tied factors back to the top level of the treeview. If you asked to restore the original factor values, Adams/Insight resets the factor nominal values to their original values (before they were tied). Otherwise, the factors retain the nominal values computed from the tie.

See examples.

Examples

This section contains the following examples for tying factors:

• Example 1: Two Symmetric Factors

• Example 2: Three Scaled Factors

• Example 3: Symmetric Points

Example 1: Two Symmetric FactorsHere, you will use a tie to vary two factors together, but keep them symmetric.

Create two factors:

factor_01 Continuous, Relative, Settings=(-10, 10), Nominal= 200, tol=2factor_02 Continuous, Relative, Settings=(-10, 10), Nominal=-200, tol=2

Tie factor_01 and factor_02 together.


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In this case, Adams/Insight creates Tie_01 as a scalar tie with the attributes of the first factor, factor_01. factor_01 and factor_02 are now children of Tie_01. Their scales are +1 and -1, respectively. Their value is dictated by:

f_01.value = Tie_01.currentValue * f_01.scalef_02.value = Tie_01.currentValue * f_02.scale

The scales are the only attributes of the tied factors that Adams/Insight now uses in the experiment. The nominal values, settings, and tolerances of the tied factors are now ignored.

When Tie_01 is set to the low value, then:

f_01.value = (Tie_01.nominalValue + Tie_01.settings.low) * f_01.scalef_01.value = (200 + (-10) ) * 1f_01.value = 190

f_02.value = (Tie_01.nominalValue + Tie_01.settings.low) * f_02.scalef_02.value = (200 + (-10) ) * -1f_02.value = -190

When Tie_01 is set to the mid value, then:

f_01.value = (Tie_01.nominalValue + Tie_01.settings.mid) * f_01.scalef_01.value = (200 + (0) ) * 1f_01.value = 200 f_02.value = (Tie_01.nominalValue + Tie_01.settings.mid) * f_02.scalef_02.value = (200 + (0) ) * -1f_02.value = -200

When Tie_01 is set to the high value, then:

f_01.value = (Tie_01.nominalValue + Tie_01.settings.high)* f_01.scalef_01.value = (200 + (+10) ) * 1f_01.value = 210

f_02.value = (Tie_01.nominalValue + Tie_01.settings.high)* f_02.scalef_02.value = (200 + (+10) ) * -1f_02.value = -210

Example 2: Three Scaled FactorsHere, you will use a tie to vary three factors together, by the same percentage.

Create three factors:

Note: The above example assume a Relative setting of the Tie; however, the same expression is valid for Absolute or Relative_Percent.

15ExamplesFactors

factor_01 Continuous, Relative Percent, Settings=(-10, 10), Nominal=1000, tol=5factor_02 Continuous, Relative Percent, Settings=(-10, 10), Nominal=2000, tol=10factor_03 Continuous, Relative Percent, Settings=(-10, 10), Nominal=3000, tol=15

Tie factor_01, factor_02, and factor_03 together.

In this case, Adams/Insight creates Tie_01 as a scalar tie with a value of 1 and the same delta type and settings of the first factor, factor_01. factor_01, factor_2, and factor_03 are now children of Tie_01. Their scales are 1000, 2000, and 3000, respectively. Their value is dictated by:

f_01.value = Tie_01.currentValue * f_01.scalef_02.value = Tie_01.currentValue * f_02.scalef_03.value = Tie_01.currentValue * f_03.scale

When Tie_01 is at the low value, then:

f_01.value = (Tie_01.nominalValue + Tie_01.settings.low) * f_01.scalef_01.value = (1 + (-10%) ) * 1000f_01.value = 900



Note that Adams/Insight set the tolerance of Tie_01 to .005, giving the same effective tolerance for factor_01 as it originally had.

Example 3: Symmetric PointsHere, you will use three ties to vary two three-dimensional points together, keeping them symmetric.

Start with factors left_pt.x, left_pt.y left_pt.z right_pt.x, right_pt.y right_pt.z. These could come from Adams/Car or Adams/Chassis, for example.

Tie the left and right together, creating three new ties:

Tie_01: left_pt.x right_pt.xTie_02: left_pt.y right_pt.yTie_03: left_pt.z right_pt.z

If the points are already symmetric, Adams/Insight automatically determines that two of the coordinates are equal and one is symmetric. The default tie attributes and tied factor scales will be correct.

If the points are symmetric about the xz (y=0) plane, for example, the default scales are:

Tie_01.left_pt.x.scale = 1Tie_01.right_pt.x.scale = 1Tie_02.left_pt.y.scale = -1Tie_02.right_pt.y.scale = 1Tie_03.left_pt.z.scale = 1


16

Tie_03.right_pt.z.scale = 1

17Response TypesResponses

ResponsesA response can be considered the output, design objective, or measurement of interest. In a Design of Experiment (DOE), you monitor or measure the response after each Trial evaluation. After adequate trials have been completed, you attempt to numerically establish a function relationship between the inputs (factors) of the system and the outputs (responses) of the system.

If successful, a response evaluates to some function and the independent variables are the factors or inputs to the system. A scalar response is a type of response which returns a single value of interest.

Response 01 = R_01 (f1, f2, f3, ... fn)

This function could be a linear function or a higher-order function. The following example demonstrates a quadratic response with three factors. The Adams/Insight fit utility computes the constant and coefficients

where a = Constant and b ... j are the coefficients.

Response Types

There are two types of responses in Adams/Insight:

• Scalar

• Composite

Scalar ResponseA scalar response is a type of response which returns a single value of interest.

Response 01 = R_01 (f1, f2, f3, ... fn)

This function could be a linear or higher order function. The following example demonstrates a quadratic response with three factors. The Adams/Insight Fit utility computes the constant and coefficients as follows:

where a is constant and b ... j are the coefficients.

R_01 (f1, f2, f3) = a + (b * f1) + (c * f2) + (d * f3)

+ (e * f1 * f2) + (f * f1 * f3) + (g * f2 * f3)

+ (h * f1^2) + (i * f2^2) + (j * f3^2)

R_01 (f1, f2, f3) = a + (b * f1) + (c * f2) + (d * f3)

+ (e * f1 * f2) + (f * f1 * f3) + (g * f2 * f3)

+(h * f1^2) + (i * f2^2) + (j * f3^2)

Adams/InsightResponses

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Composite ResponseA composite response consists of N number of scalar responses. When evaluated together, this group of scalar responses can produce a continuous representation of a measurement. A composite response enables you to reserve more than one column per response in the work space matrix. Traditionally you would expect one column per response in the work space matrix when responses represent a scalar value for each Trial. By altering the Columns field in the response attribute form you can reserve any number of additional columns. These Columns are then named <response abbr> (0), <response abbr> (1), <response abbr> (2), ... <response abbr> (n). Composite response member elements could be used to store polynomial representation of a curve by putting the constant and subsequent coefficients in the respective columns.

For example, the following composite response represents a cubic polynomial. A cubic polynomial consists of a constant and three coefficients; therefore, the four scalar responses. In this example, the four scalar responses are a function of three factors:

• curve(m, n, o, p) = m + (n*x) + (o*x2) + (p*x3)

• Response 10 (0) = R_10 (0) (f1, f2, f3) = m

• Response 10 (1) = R_10 (1) (f1, f2, f3) = n

• Response 10 (2) = R_10 (2) (f1, f2, f3) = o

• Response 10 (3) = R_10 (3) (f1, f2, f3) = p

Now, if you vary x over a range, you can visualize the resulting curve.

This next example is a Composite Response representation of a quadratic polynomial. A quadratic polynomial consists of a constant and two coefficients; therefore, the need for three scalar responses. In this particular case, the composite response elements are dependent on six factors.

• curve(m, n, o) = m + (n*x) + (o*x^2)

• Response 11 (0) = R_11 (0) (f1, f2, f3, f4, f5, f6) = m

• Response 11 (1) = R_11 (1) (f1, f2, f3, f4, f5, f6) = n

• Response 11 (2) = R_11 (2) (f1, f2, f3, f4, f5, f6) = o

19Response TypesExperiments in Adams/Insight

Experiments in Adams/InsightIn Adams/Insight, a designed experiment helps you gain understanding and improve a system or subsystem model. The components of the experiment include:

• System model - such as a multi-dynamic simulation

• Factors - inputs to the system

• Responses - outputs or performance metrics of the system under investigation

In the experimental design process, you systematically modify factors in your model and monitor responses after each Trial.

The design matrix does not directly specify factor values. Instead, it specifies indexes to the Levels for each factor. The indexes center on zero. This means that for a two-level factor, the only possible values are -1 and +1; for three-levels, -1, 0 and +1; for four-levels, -2, -1, +1, +2; and so on.

This convention implies that the levels (allowed values or range of values) are ordered from smallest to largest, and cover a range above and below a baseline value. For example, if a factor has three levels, you can think of the -1 index as the low value, the 0 index as the middle or baseline value, and the +1 index as the high value. In Adams/Insight, we recommend that you list factor values from smallest to largest.

Adams/InsightExporting Data

20

Exporting DataIn Adams/Insight, you can export various components to other experiments. You can export the following:

• Design Space

• Work Space

• Full Work Space

• Model

To export a component:

1. From the File menu, point to Export, and then select the menu item for the component you want to export.

A file selection dialog box displays.

2. Select the file to which you want to export the components.

3. Select OK.

21Response TypesImporting Data

Importing DataIn Adams/Insight, you can import various components of other experiments. You can import the following:

• Full Work Space

• Inclusion Design

• Results

• Work Space

To import a component:

1. From the File menu, point to Import, and then select the menu item for the component you want to import.


2. Select the file that contains the components you want to import.

3. Select OK.

Adams/InsightReusing Components

22

Reusing ComponentsIn Adams/Insight, you can reuse many components of existing experiments. When you reuse components, Adams/Insight updates your current experiment to use as many of the attributes and settings of the old experiment as possible. As a result, you can efficiently rerun an old experiment if the new experiment has few changes. Learn how to reuse components.

You can reuse the following:

• Factors

• Responses

• Design specifications

You can also reuse all factors, responses, and design specifications from an experiment.

When you run Adams/Insight with the reuse option, either from the command line with -reuse or from Adams/View using the Reuse Experiment option, Adams/Insight loads the experiment and reuses the specified old experiment. If Adams/Insight can recreate a valid workspace, it does so and then immediately exits without displaying the Adams/Insight window. This allows you to use Adams/Insight in a batch mode, either from an Adams/View .cmd or from the command line. As long as the experiment has the same number of factors and the factors have the same general characteristics, you can use Adams/Insight to regenerate the workspace and immediately exit. Some common uses of this feature are changing factor nominal values or limits, or adding or removing responses.

Reusing FactorsWhen you reuse factors, Adams/Insight:

• Promotes any factors that match old inclusion factors by name.

• Creates ties in the inclusion factors (if there are factors matching old tied factors by name and type).

• Updates most attributes for inclusion factors that match old inclusion factors by name and type.

Reusing ResponsesWhen you reuse responses, Adams/Insight:

• Promotes any responses that match old inclusion responses by name.

• Updates most attributes for inclusion responses that match old inclusion responses by name and type.

Note: Reuse only updates the information in the target experiment with matching information from the referenced experiment. For example, if the referenced experiment has additional or unique responses that do not exist in the target experiment, they are not brought in.

23Response TypesReusing Components

Reusing Design SpecificationsWhen you reuse design specifications, Adams/Insight does one of the following:

• Copies the design and regenerates the Work Space (if the new inclusion factors are identical to the old inclusion factors).

• Regenerates the design from the old specification (if the factors are not identical, but there are the same number of inclusions as the old experiment and the new inclusions have the same numbers of levels as the old inclusions).

• Copies only the design specification (if there is a different number of inclusion factors or different numbers of levels).

To reuse a component:

1. From the File menu, point to Reuse, and then select the menu item for the component you want to import.


2. Select the experiment file that contains the components you want to reuse.

3. Select OK.

Adams/InsightSimulation Properties

24

Simulation PropertiesDisplays information on the simulation used for your experiment.

Working with Results

Response Surfaces and Fitting ResultsA response surface is a mathematical surface represented by a series of polynomials. It gives an approximate value of the response (dependent variable or objective) as a function of the factors (independent variables or Design Variables). The techniques you use to create and analyze response surfaces are collectively called Response Surface Methodology (RSM). RSM is widely used for developing and optimizing processes and products of all kinds (see References).

Adams/Insight computes the least-squares fit of the polynomial when you use the Fit results tool. In statistical terms, Adams/Insight performs a multiple linear regression of polynomial models. It computes standard analysis of variance (ANOVA) statistics for the fit, and provides a large set of ANOVA statistics, like R2 and R2adj, to help you assess quality of fit.

Adams/Insight can export the response surface polynomial as an HTML Web page or SYLK format spreadsheet file.

You can use response surfaces as a simplified model of a system. For example, you can use the HTML page or SYLK file to quickly predict the effects of changing factors in your design matrix. Load the HTML page in a browser or the SYLK file in a spreadsheet program, and modify values for factors to the change in estimated response.

You can also use the response surface to estimate an optimal design. Because it is much quicker to evaluate a polynomial than run a full series of simulations, optimizing estimated response is a quick way to get an approximate optimum. You can use data in the SYLK file to do this in a spreadsheet application, such as Microsoft Excel™.

Evaluating the FitThis section provides a brief explanation of fit results. Consult a statistics, regression, or RSM reference for more information on evaluating regression results.

R2 (R-Squared) indicates how well the response surface represents the results. It is the square of the

multiple correlation coefficient (R). R2 is the fraction of variability in the data for which the model

accounts. The larger it is, the better the fitted equation explains variations in the data. R2 is between 0

and 1. If R2 = 1, the equation exactly matches the data. A high R2 (.9 for example) indicates a good, but

not exact, fit. A low R2 (.3 for example) indicates a poor fit.

High R2 values can be deceiving. Adding more terms to the equation almost always increases R2. If you add enough terms, you can always achieve an exact fit. However, you usually want the most efficient fit: the fit that gives the best results with the fewest terms.

Adams/Insight2

Because of this, it's useful to look at R2adj (Adjusted R-Squared), which is similar to R2 but is adjusted

to account for the number of terms. Adding terms does not always increase R2adj. If you add unnecessary

terms, R2adj often decreases. If R2 is much higher than R2adj, it indicates that at least one of the terms is not as useful as the others and could probably be removed without hurting the fit. You find which term to remove by further examining the results, or by trial and error.

Even after checking R2adj, a high R2 does not always mean you have a suitable response surface. At a minimum, you should review Residuals. Residuals are differences between original response values and estimated values. In other words, a residual is the amount by which a fitted surface misses an original value. Adams/Insight provides residuals for each Trial.

If a trial has an unexpectedly large residual, it could indicate that the trial is an outlier, meaning that it might not be consistent with the other runs. Perhaps something unexpected happened or there was a simulation error during the run. Review the results of that run, looking for unusual behavior or results. If necessary, correct the model so that all runs complete successfully and consistently.

Large residuals can also mean that data are irregular and difficult to fit. Review your objective function and values, looking for sudden changes in value or the slope of values. Gaps or cusps in objective values cause poor fits. If necessary, adjust your objective function to produce smoother values.

If the runs seem consistent and objective values vary smoothly, then large residuals probably mean the polynomial is just not a good fit and you should add more terms or fit across a smaller range of values.

When evaluating the fit, if the R2 and R2adj are red, review the workspace matrix as follows:

• Verify that all of the runs completed successfully.

• Review the residuals and determine if there is a pattern in actual versus estimate.

• Refine the model to improve the fit.

• See if the factors you selected have any impact on the response (review Term Significance in the Terms form).

• Check the Error DOF in the fit summary.

Refinement of a FitFollowing are the typical steps of refining a model:

1. Fit regression model

2. Check R2 and interpret ANOVA table

3. Verify residuals plots

4. Remove outliers, if needed

5. Remove terms, if needed


7. Transform response, if needed

3Working with Results

8. Change model order, if needed


10. Monitor error Degrees of Freedom (DOF)

Exporting ResultsYou can export your results to a file using these file formats:

• HTML - HTML-format Web page

• SYLK - Symbolic Link (SYLK) format spreadsheet file

• Visual Basic - Visual Basic subroutines

• MATLAB - MATLAB M-File

The SYLK and HTML formats show you a table of the responses and factors where you can change variable values, and automatically compute new estimates. To do this, display the HTML page in a Web browser enabled to read JavaScript, or load the SYLK file into a spreadsheet program, such as Microsoft Excel. The SYLK format is a convenient way to transfer response surface equations to a spreadsheet program for further study.

The Visual Basic format file contains Visual Basic subroutines to compute the responses.

The MATLAB format file contains MATLAB matrices that can be used to compute the responses.

Using HTML FilesWhen you open an HTML results file that you exported from Adams/Insight, you see something similar to what’s shown in the figure below.

Put your mouse over a section of the following example to get more information.

Adams/Insight4

Adams/Insight6

HTML: Factor

The factor section lists each factor in the design matrix. Each row shows information about one factor including the factor name, units, current value, tolerance (optional), minimum, nominal, and maximum value.

You can modify the current value by typing a number in the text box or by selecting the increment/decrement buttons. After entering a value in a factor current value text box, you must select the Update button or press Enter to see the response effect.

HTML: Plot

The plot section lists the composite responses only. You can make changes to the values in this section and press Update Plots to redraw the plots in the separate window. You can also check the Swap XY check box to invert the x and y axes.


HTML: Response

The response section lists each response in the design matrix. Each row shows information about one response including the response name, units, and current value.

HTML: Tolerance Contributions

The Tolerance Contributions table provides the percent contribution of each factor to the tolerance of each response. A high value means the factor tolerance greatly contributes to the response tolerance. The response tolerance and tolerance contributions vary with both factor values and factor tolerance values. For more information, click on Tolerance Contributions in the left pane of this window.

HTML: Goodness-of-Fit Statistics

Displays response statistics for each response. These statistics help you evaluate the goodness of the fit.

HTML: Design Matrix Table

This section has a table for each response in the design matrix. Each row of each table shows the effect and percentage effect of varying a factor from its minimum to its maximum value. There is also a bar graph that shows the relative impact each factor has on the response.

At the end the form displays the date and time of the run, and the version of Adams/Insight that created the HTML file.

Main EffectsMain effect refers to the primary effect of a factor. A good way to examine the main effects is through a Pareto chart.

The Adams/Insight .htm file computes main effects on the fly using JavaScript.

The displayed main effect of a factor is the difference between the response at the factor maximum value and the response at the factor minimum value, while all other factors are at their average values. Effects may be positive (response increases with larger factor value) or negative (response decreases with larger response value).

Note that the minimum and maximum factors' values do not necessarily produce the minimum and maximum response values. If a response is highly nonlinear over the factor value range, the minimum and/or maximum response values may be in the middle of the curve. In this case, the main effects values are meaningless.

The effect % is the ratio of the effect value to the response value with all factors at their average values. An effect % greater than 100% means that the variation in the response value is larger than the average response value.

The effects are sorted largest to least absolute value. The longest bar is always the same length. The other bars are proportional to the largest based on the effect value relative to the largest value. Positive effects have a dark blue bar, negative effects have a light blue bar.

Adams/Insight8

Using SYLK FilesYou can export an SYLK file from Adams/Insight. When you open an SYLK results file in Microsoft Excel, it appears similar to the image shown below. If you change factor values, the spreadsheet program automatically recomputes estimated response values.

Adams/Insight10

SYLK: Factor

The factor section lists each factor in the design matrix. Each row shows information about one factor including the factor name, units, current value, tolerance, minimum, and maximum value.

You can modify the current value by typing a number in the text box. After entering a value in a factor text box and pressing Return or Tab, you can see the response effect.

SYLK: Response

The response section lists each response in the design matrix. Each row shows information about one response including the response name, units, current value estimate, and tolerance.

SYLK: Tolerance

The Tolerance Contributions table provides the percent contribution of each factor to the tolerance of each response. A high value means the factor tolerance greatly contributes to the response tolerance. The response tolerance and tolerance contributions vary in both factor values and factor tolerance values. For more information, click on Tolerance Contributions in the left pane of this window.

SYLK: Goodness-of-Fit Statistics

Displays response statistics for each response. These statistics help you evaluate the goodness of the fit.

SYLK: Design Matrix Table

This section has a table for each response in the design matrix. Each row of each table shows the polynomial terms, coefficients, and factors used in the fit. There is a separate table that shows the tolerance estimate for each factor along with sensitivity and variation.

ToleranceThe tolerance value can be initially specified as one of the factor attributes. If any of the factors have a nonzero tolerance attribute, the published Web page will present this value and the responses will have a tolerance computed for each time a factor value is modified.

The computed tolerance reflects the same amount of variation as the factor tolerance values. For example, if you enter factor tolerances that are three times the standard deviation, then the computed response tolerance will be three times the standard deviation of the response.

Note that the tolerance calculation always assumes a normal distribution for factor variations. This is true even if you have selected None or Uniform for Monte Carlo Distribution in the Factor form. Adams/Insight only uses the Monte Carlo Distribution setting for Monte Carlo experiments, not for the tolerance calculations in the exported HTML file.

The method Adams/Insight uses to compute the response tolerance is described in several papers. A specific reference is "A New Tolerance Analysis Method for Designers and Manufacturers" by Greenwood and Chase. See References for more details.

The assumptions of this computation are:


• The variability of the inputs are all normal distributions.

• The mean is at the midpoint.

• They are statistically independent.

• The relationship between the response and the factor is effectively linear over the variation range.

This approach has been successfully used in many real manufacturing problems at various customers, and the results are extremely close to the results obtained through Monte Carlo simulation. This is to be considered an "up-front" manufacturing analysis tool, not a manufacturing plant tolerance analysis tool.

Tolerance ContributionsThe Tolerance Contribution table shows the relative contribution by each factor to the variation (tolerance) in each response. The contributions are rounded to the nearest percent. The values in each row add to 100%, plus or minus a few percent due to the rounding.

A high value indicates that the factor variation greatly contributes to the response variation. A low value indicates that the factor does not contribute much to the response variation. A value of zero indicates either that the factor does not affect the response at all, or that the variation in the factor has only an insignificant effect compared to the other factors.

The contribution values only show relative importance, they do not directly indicate how much the response variation will drop if the factor variation is eliminated. For example, if there are two factors and each contributes 50% to the response variation, eliminating the variation of one factor will not cut the response variation in half. Instead, it will reduce it by about 30%.

This is because the total response variation is the square root of the sum of the individual factor contributions squared. The percentage contribution is calculated as the ratio of the factor contribution squared to the sum of all the contributions squared.

Adams/Insight12

Using Adams/Insight Tools

Adams/InsightWorkspace

2

Workspace

Scatter Plots

Scatter plots allow you to view the raw data plotted against another variable. You can also plot the raw values against the trial count.

Work Space Column CalculatorThe Workspace column calculator enables you to perform mathematical operations on the columns of an existing workspace.

To use the work space column calculator:

1. From the Tools menu, select Work Space Column Calculator.

2. Enter the information as described in the dialog box help for Work Space Column Calculator.

3. Select OK.

See Work Space Column Calculator Example.

Work Space CorrelationsCorrelation can be used to measure the potential strength of relationship or lack of relationship between two variables. By numerically quantifying the amount of scatter of the data, correlations can often provide a greater understanding of the system you're investigating. Correlation values in Adams/Insight will range between -1 and +1. If the scatter of the data forms a straight line, the linear correlation value will have a value of one (negative one if the slope is negative).

• Positive correlations indicate that as one variable increases, the other variable decreases.

• Negative correlations indicate that as one variable increases, the other variable increases.

• Linear correlations (Pearson) indicate that the scatter of the data is "cigar shaped" or elliptical.

• Nonlinear correlations (Spearman or rank) indicate that the scatter is irregular (that is, has clusters).

• Higher absolute correlation values indicate a stronger correlation.

Note: • The following are predefined variables in the Work Space Column Calculator:

• rtod for radians to degrees conversion

• dtor for degrees to radians conversion

• curRow for the integer value of the current row (this is a zero-based index)

• Another class of predefined variables is the factor values. To access these values, use the factor abbreviation.

3Using Adams/Insight ToolsWorkspace

• Correlation values below 0.5 generally indicate a chaotic relationship between the variables.

• Nonlinearities of the system may confuse correlation index.

Learn about the Work Space Correlation Matrix.

Work SpaceThe Work Space is a matrix with the rows indicating the runs and the columns identifying the factor settings and resulting response values in engineering units. It is sometimes referred to as the run matrix.

Learn about the Design Work Space form.

Work Space ReviewThe Work Space Review enables you to do preliminary investigations of the raw data from the work space. This can be achieved by graphically reviewing the histograms which depict the distribution of the column values.

Learn more about the Design Work Space Review Form.

Examples

Following are examples of using the Work Space Column Calculator in Adams/Insight.

Example 1If the values of response r_01 were measured in miles per hour (mph), you could convert the raw data to kilometers per hour (kph) as follows:

1. In the Work Space Column Calculator, set Column to Compute to r_01.

2. Set the Expression area to r_01*1.609344.

3. Select Apply or OK.

4. The updated values display in the r_01 column.

Example 2Synthesize a relationship between three factors and a response

1. Create three factors and one response.

2. Create a workspace matrix.

3. Open the workspace column calculator and set Column to Compute to r_01.

4. Enter the following expression: '2 + 3*f_01 + 4*f_02**2 + 5*f_03**3'

5. Select OK.

Adams/InsightWorkspace

4

Example 3Use of Python functions in the expression

1. Create two factors and one response.



4. Enter the following expression: '2 + 3*f_01 + 4*f_02**2 + 5*f_03*f_02 + random()'

5. Select OK.

5Using Adams/Insight ToolsOptimization

OptimizationUsing Adams/Insight you can optimize your factor values based on a response surface (fitted regression model). If your experiment uses a simulation conduit that supports direct execution, you can also directly optimize your experiment using simulations. During optimization, Adams/Insight automatically adjusts the factor values so that the resulting responses come as closely as possible to the specified target values.

To optimize a response surface, select a model under Analysis in the tree view, then select Tools->Optimize Model or the Optimize tool in the toolbar. Adams/Insight displays the Optimize Model or Experiment dialogbox. Adams/Insight uses the fitted model to estimate the response values.

To directly optimize your experiment, select Simulation->Run->Direct. Adams/Insight displays the Optimization Form. Adams/Insight runs simulations to compute the response values. If Simulation->Run->Direct is disabled, the simulation conduit for your experiment does not support direct execution and you will not be able to directly optimize your experiment. The MSC.Patran, Easy5, and ASCII conduits support direct execution by Adams/Insight.

The Optimization form in Adams/Insight displays your model’s factors (Design Variables) and responses (design objectives). Only scalar responses are shown; composite responses are not displayed.

Using the factor controls, you can:

• Reduce the range of factor values

• Set the factor value

• Fix the factor to a specific value

Using the response controls, you can specify:

• Responses to be minimized, maximized, constrained, or ignored

• Target values or constraint limits

• Relative weights

Using Adams/Insight, you can perform Single-Objective Optimization and Multi-Objective Optimization. Single-objective optimization involves trying to achieve a target for one scalar response; multi-objective optimization involves more than one scalar response. If you choose more than one response as objectives, Adams/Insight will calculate a multi-objective cost based on the objective options, targets, weights, and multi-objective method option. Adams/Insight will then minimize the overall multi-objective cost.

If you want to do a direct optimization using simulations, it is a good idea to first create a response surface, optimize that, then use the Save button in the Optimization Form to save your settings and the optimal point as the new defaults. Then, when you do the direct optimization, the response minimum and maximum values will be good estimates, and the starting point for the direct optimization will be a good starting point to find the true optimum. If you do not do an initial response surface and optimization, be sure to specify appropriate response minimum and maximum values in the Response form, in the Optimization tab. For more information on optimizing in Adams/Insight, refer to Optimizing Results in Using Adams/Insight with Adams/View.

Adams/InsightPreferences

6

PreferencesYou can define the design, fit, and thresholds preferences for your experiment.

To set preferences:

1. From the Edit menu, select Preferences.

2. Select the Design, Fit, and/or Thresholds tab, and enter your preferences as described in the Preferences Dialog Box.

3. Select OK.

Note: Optimization preferences are set in the Optimization Preferences dialog box.

7Using Adams/Insight ToolsRefinement

RefinementAfter fitting a model in Adams/Insight, it is important to evaluate the quality (or "Goodness") of the fit. If the fit does not meet your criteria, you can refine it using Adams/Insight.

You can perform a manual or automatic refinement.

Manual refinement

To manually refine the fit of your model:

1. From the Tools menu, point to Refine Model Manually, and then select one of the following:

• Remove Outliers: Select Outliers to remove from your experiment.

• Remove Terms: Select terms to remove from your experiment.

• Transform Response: Specify the type of transformation for your response.

• Change Order: Specify the model order for your experiment.

2. Enter the information as described in the dialog boxes:

• Refinement - Remove Outliers

• Refinement - Remove Terms

• Refinement - Transform Response

• Refinement - Change Order

3. Select OK.

Automatic (stepwise) refinementAdams/Insight uses stepwise refinement to automatically refine regression polynomials. Stepwise refinement repeatedly adds and removes terms in the regression equation, trying to find the smallest set of significant terms. Stepwise refinement is not guaranteed to find the smallest set, but usually gives good results.

The stepwise refinement will not increase the order of the polynomial. For example, if you have a quadratic order regression, the stepwise refinement might include some or all of the quadratic terms, but will not add any cubic terms.

You can manually review and modify the terms the stepwise refinement has excluded using the Manual refinement method described above.

The basic stepwise refinement process is:

1. Start with the constant term.

2. If possible, add a significant term or group of terms to the polynomial.

3. If possible, remove an insignificant term or group or terms from the polynomial.

Adams/InsightRefinement

8

4. Repeat steps 2 and 3 until no other terms can be added or removed, or until a term combination is repeated.

Adams/Insight repeats the stepwise refinement on each regression in the specified model.

Depending on the hierarchy option you choose, Adams/Insight may add or remove individual terms, or only groups of terms. Hierarchy refers to the relationship between polynomial terms, and whether or not some terms should be added or removed before others.

At each step, the stepwise refinement evaluates the terms or sets of terms that could be added or removed based on hierarchy. It evaluates them using your choice of F or P value, and your specified threshold value to add or remove terms. It first evaluates all possible terms or sets of terms to add. If the best term or set is more significant than your specified threshold, it will add the term or set. If not, no term is added. Then, it evaluates all possible terms or sets of terms to remove. If the worst term or set is less significant than your specified threshold, it will remove it. If not, no term is removed.

The refinement ends when Adams/Insight cannot add or remove any more terms, or when a term combination is repeated.

Many of the term evaluations require a new computed fit. As the number of terms increases, not only does the number of evaluations increase, but the time to compute the fit increases as well. For large models, stepwise refinement may be quite slow, especially with no hierarchy.

To automatically refine the fit of your model:

1. From the Tools menu, point to Refine Model Automatically, and then select one of the following:

• Remove Terms: Automatically removes terms from your experiment.

• Remove Outliers: Automatically removes outliers from your experiment.

2. Enter the information as described in the dialog boxes:

• Refinement - Remove Terms

• Refinement - Remove Outliers

3. Select Start (or OK) to begin the refinement.

Adams/Insight displays the messages in the Monitor tab. If you want to stop the process before it is complete, select Stop or Close. If you stop the refinement, no results are saved.

Tutorials and ExamplesThe following are examples of specific Adams/Insight functionality:

• Tying Factors

• Sample Experiment

• Work Space Column Calculator

The following tutorials teach you how to use Adams/Insight with other products:

Instructions file: Description of example:

install_dir/ainsight/examples/ain_tut_110_asc_mat.txt

Studies the control system of a linearized model using the Adams/Insight ASCII conduit and MATLAB.

install_dir/ainsight/examples/ain_tut_102_asc_aco.txt

Studies the control system using the Adams/Insight ASCII conduit and Adams/Controls.

install_dir/ainsight/examples/ain_tut_101_patran.txt

Uses MSC.Patran, Adams/Insight, and the Adams/Insight Patran conduit to facilitate MSC.Patran multi_runs. Demonstrates how the utility can be used with MSC.Patran session files and the MSC.Patran parametric_modeling_utl class.

install_dir/ainsight/examples/ain_tut_101_easy5.txt

Uses Easy5, Adams/Insight, and the Adams/Insight Easy5 conduit to facilitate Easy5 multi_run investigations. Demonstrates the Easy5 EMX capabilities and how the utility can be used with Easy5 model (.ezmf) files.

Using Adams/Insight with the ASCII Conduit (ASC)

Demonstrates how Adams/Insight and the ASCII conduit can be used with Adams/Solver simulation files.

Using Adams/Insight with Adams/Car Uses Adams/Insight with Adams/Car to investigate transient dynamic response of a vehicle front-suspension model.

Using Adams/Insight with Adams/Chassis Demonstrates how to use Adams/Insight with Adams/Chassis to perform a design of experiments (DOE) analysis.

Using Adams/Insight with Adams/View Uses a simple automotive example to illustrate the basics of Adams/Insight.

Adams/InsightUsing Adams/Insight with the ASCII Conduit (ASC)

2

Using Adams/Insight with the ASCII Conduit (ASC)

Overview

This tutorial guides you through the process of using Adams/Insight ASCII Conduit to interface between

text files and Adams/Insight, part of the Adams suite of software.

This guide assumes basic working knowledge of Adams/Insight, Adams/Solver .adm and .acf files, and Adams/Solver running in command-line mode. It also assumes you know how to negotiate a file structure.

• Introducing the Tutorial

• Creating and Running an Experiment

Introducing the Tutorial

OverviewThis chapter introduces you to the tutorial and gets you started. The tutorial demonstrates how the Adams/Insight and ASCII conduit can be used in conjunction with Adams/Solver simulation files.

The sections in this tutorial are:

• About this Tutorial

• About Adams/Insight with ASCII Conduit

• Getting Started

• Parameterizing the System

This tutorial takes about one hour to complete.

About this TutorialIn this tutorial, you will use Adams/Insight ASC with Adams/Insight. You will start with two text files that describe a dynamics system. These text files are Adams/Solver input files that represent a bungy-jump event. An Adams model file (.adm) describes the dynamics system and the Adams control file (.acf) provides instructions to Adams/Solver of how to run the particular job. You will import the two text files into Adams/Insight ASC, making them ASC templates. Then, you will parameterize the two ASC templates (referencing attributes such as, numerical values of mass, free length, and stiffness). You will also parameterize text fields, such as simulation titles and simulation names. Finally, you will use Adams/Insight to create a Web page, where you can interactively adjust the free length of a bungy cord to limit the travel distance of a jumper when he or she jumps.

3Introducing the TutorialUsing Adams/Insight with the ASCII Conduit (ASC)

About Adams/Insight with ASCII ConduitAdams/Insight is a stand-alone product that also works with Adams/Car, Adams/Chassis, Adams/Controls, Adams/Durability, and Adams/View. Adams/Insight lets you design sophisticated experiments for measuring the performance of your mechanical system. It also provides a collection of statistical tools for analyzing the results of your experiments so that you can better understand how to refine and improve your system.

Within the Adams analysis environment, there are conduits between Adams/Insight and the other Adams products (for example, Adams/Car, and Adams/Chassis). These conduits streamline the process by taking advantage of the inherent parametric strengths of the vertical application.


Adams/Insight ASC is a general-purpose tool that helps you work with various analysis environments, from your own or inhouse-developed applications to commercial applications which accept an ASCII input deck.

Getting StartedHere you will create your working directory and copy over the necessary files.

To get started:

1. Create a working directory called ain_examples/asc. This directory will contain all of the files for this tutorial.

2. Copy the following files from <install_dir>/ainsight/examples to the newly created working directory:

• ain_tut_101_asc_adm.acf

• ain_tut_101_asc_adm.adm

3. Copy the two Adams simulation files by performing one of the following from a command prompt in the ain_examples/asc directory:

• On Windows:

Note: On Windows, you may need to set the permissions to Full Control to edit the tutorial files.

Note: You can skip steps 1-3 below if you previously used the Help ‡ Copy Examples to feature to copy all of the tutorials for Adams/Insight.


4

• copy ain_tut_101_asc_adm.acf bungy.acf

• copy ain_tut_101_asc_adm.adm bungy.adm

• On UNIX:

• cp ain_tut_101_asc_adm.acf bungy.acf

• cp ain_tut_101_asc_adm.adm bungy.adm

4. Ensure that the simulation runs by submitting bungy.acf to Adams/Solver by entering the following from your working directory in the command window:

• On Windows: mdadams2010 ru-s bungy.acf

• On UNIX: mdadams2010 -c ru-standard i bungy.acf exit

Parameterizing the SystemHere, you will create an Adams/Insight ASC system. You will first start theAdams/ASC editor and import two text files, bungy.adm and bungy.acf. You create an ASC template by importing a text file(s) into an ASC system. In this tutorial, you import two text files, which results in two ASC templates. (An Adams/Insight ASC system can have many ASC templates.)

Once you’ve created these ASC templates, you will use the Adams/Insight ASC editor to parameterize the ASC templates by annotating them. This process involves delineating regions of text in the templates. In this way, you can identify parts of the template that will subsequently be replaced. This substitution process occurs when variants of the nominal system are automatically generated as part of an investigation strategy.

To parameterize the system:

1. From the command prompt, start the Adams/Insight ASC Editor from the working directory as follows:

• On Windows: mdadams2010 ainsight -ascg

• On UNIX: mdadams2010 -c ainsight -ascg exit


• From the File menu, select Import.

• Select the Import text file tool .

3. Select the file bungy.acf.

4. Select Open.

5. Using the same process, import the file bungy.adm.

Next, you will annotate the two ASCII templates by identifying the regions that will be substituted, as follows:

• Highlight the text that is to be substituted.

• Associate a variable with the highlighted text.

5Introducing the TutorialUsing Adams/Insight with the ASCII Conduit (ASC)

For example, if you have a string such as, 'PART/02 , MASS = 160.0, CM = 0203' and you want to alter the mass of the part, you would first highlight the numeric value of '160.0', and then right-click and assign the highlighted text to a variable (by either creating a new variable, or referencing an existing variable displayed in the shortcut menu). When creating a new variable, you can define the following:

• Name: Descriptive name of the variable.

• Format: Controls how the value will be printed. The convention follows the C printf() convention of %d for integer, %f for float, %e scientific, and %s for string. On UNIX, use the man printf command to get more information on this numeric formatting convention. In most cases, you don’t need to modify the default value.

• Value: Default value that was originally highlighted.

• Description: Optional supplemental information regarding the particular variable.

6. From the Template menu, select bungy.adm.

7. Highlight the numeric value of the mass of the jumper (160.0).

8. Right-click and select Create.

The Create Variable dialog box appears.

9. Change the value of the Name text box from F_05 to mass.

10. Leave the default values in the remaining text boxes.

11. Select OK.

The corresponding text is modified as follows:

From: PART/02 , MASS = 160.0, CM = 0203

To: PART/02 , MASS = {{mass=160.000000}}, CM = 0203

12. Highlight the numeric value of the function of the free-length variable.

13. Right-click and select Create.

14. In the Name text box, enter free_length.

15. Select OK.

16. Select the IC value of the free-length variable.

17. Right-click, point to Replace, and then select the variable free_length.

The following shows how the corresponding text was modified:

From: VARIABLE/01, IC=100.0, FUNCTION=100.0

To: VARIABLE/01, IC={{free_length}}, FUNCTION={{free_length=100.000000}}

18. Create another variable for the stiffness by repeating the steps above, using the following parameters:

Note: The double curly brace delimiters '{{' '}}' that appear in the text file are the default delimiters. You can change them on a template-by-template basis in the template properties.


6

From: VARIABLE/02, IC=4.0, FUNCTION=4.0

To: VARIABLE/02, IC={{stiffness}}, FUNCTION={{stiffness=4.000000}}

19. To parameterize the title of the simulation, add the predefined variable of ascTrialName to the title description so that the first line of bungy.adm ASC Template looks as follows:

Bungy Jump (Adams/Insight ASCII Conduit) {{ascTrialName}}__bungy.adm

The parameterization of the bungy.adm ASC template is complete. Now, you parameterize the analysis names specified in the bungy.acf ASC template (first you open the appropriate ASC template and then parameterize it).

20. From the Template menu, select bungy.acf.

21. Parameterize the first two lines of the bungy.acf ASC template so they look like the following:

{{ascTrialName}}__bungy.adm{{ascTrialName}}__bungy

22. Specify ASC template properties specific to the bungy.acf ASC template: from the Edit menu, select Template Properties.

The Template Properties dialog box appears.

23. Now you will specify how the simulations will be run. Do this by completing the following text boxes:

• On Windows:

• Execution Prefix: mdadams2010 ru-s

• On UNIX:

• Execution Prefix: mdadams2010 -c ru-stan i

• Execution Postfix: exit

24. Enter a Python dictionary definition in the Post Operations (Dict) text box. For this example, copy and paste the following:

{'total_length':'adams_r.Fetch_req(1,3,"max")','max_acc':'adams_r.Fetch_req(1,4,"max")'}

This string specifies what happens after the simulations are complete. Specifically, what parts of the simulation results files will be interrogated or how the postprocessing will occur. The results are retrieved from the solver files and specific values are placed in the Work Space.

Note: Be sure that there are two underscores between the closing curly brace and bungy.

Note: Be sure that there are two underscores between the closing curly brace and bungy.

Note: You can also see an example of this string on a commented-out line in the bungy.adm file.

7Creating and Running an ExperimentUsing Adams/Insight with the ASCII Conduit (ASC)

25. Select OK.

Now you will specify some optional ASC system attributes, such as a name and description. This can be helpful for future reference.

26. From the File menu, select ASC Properties.

The ASC System Properties dialog box appears.

27. Complete the text boxes as follows:

• Name: Jump

• Description: Bungy jump tutorial example

28. To save the ASC system properties, select OK.

29. Save the ASC system to disk.

a. From the File menu, select Save As.

b. In the File name text box, enter j_asc.

c. Select Save.

This creates a file on disk called j_asc.xml. This is the ASC system with the two ASC templates. You can view this file using a text editor or a browser.

30. From the File menu select Export Experiment.

This automatically generates an Adams/Insight experiment with the factors and responses you defined in the ASC system. The default Adams/Insight experiment file is called j_asc_exp.xml.

31. Perform one of the following:

• To continue with the tutorial, see Creating the Experiment.

• To quit the tutorial and exit the ASC Editor, from the File menu, select Exit.

Creating and Running an Experiment

OverviewIn this chapter, you’ll create an experiment and run through a number of trials that you set up in the experiment.


• Creating the Experiment

Note: Look in the window that you used to start the Adams/Insight ASC editor for warning messages. Make corrections as necessary.


8

• Running Your Experiment

• Importing and Reviewing the Results

Creating the ExperimentHere you will create your experiment.

To create your experiment:

1. Start Adams/Insight from the ASCII Conduit by selecting the Adams/Insight tool.

Your experiment will automatically open in Adams/Insight. Note the inclusion factors and responses.

2. Select the desired investigation strategy:

• In the treeview, expand Design, and then select Specification.

• In the Design Specification form, select Full Factorial as the DOE Design Type.

3. Generate the workspace.

You should now have a workspace with 8 trials.

4. Modify AnaPostfix under Simulatin Properties to be __bungy.

5. From the Simulation menu, point to Build, and then select All to write the ASC Multi-Event Regulator (ASC-MER) file.

The *_mer.py file is generated in the current directory. This Python script file works with the ASC system file, and can be used to build, run, and postprocess the ASC systems defined in the _mer.py file. You can run it from the command line using the Adams Python commands, or from the ASC system editor using the toolbar buttons diff, run one, and run all.

• On Windows: mdadams2010 python j_asc_exp_mer.py -h

• On UNIX: mdadams2010 -c python j_asc_exp_mer.py -h exit

6. At the prompt, select Yes to process the ASC system file j_asc.xml.

7. Click OK.

8. From the File menu, select Exit, being sure to save your experiment.

Running Your ExperimentHere you will run the ASC Multi-Event Regulator (_mer.py) file, test the configuration and run trials.

Note: For more information on the options available with the _mer.py file, execute one of the following commands:

9Creating and Running an ExperimentUsing Adams/Insight with the ASCII Conduit (ASC)

To run the ASC Multi-Event Regulator file:

1. Open a command window and change to the ain_examples/asc working directory.

2. To test the configuration and only run the first trial, enter one of the following commands:

• On Windows: mdadams2010 python j_asc_exp_mer.py

• On UNIX: mdadams2010 -c python j_asc_exp_mer.py exit

The results of this operation are placed in a subdirectory with the default prefix of tst.

3. To run all trials, enter one of the following:

• On Windows: mdadams2010 python j_asc_exp_mer.py -t

• On UNIX: mdadams2010 -c python j_asc_exp_mer.py -t exit

To check that the results files were created, change to the tst_dir subdirectory and view its contents.

Importing and Reviewing the ResultsHere you will import the results into Adams/Insight and then review them.

To import and review the results:

1. Start Adams/Insight from the ASCII Conduit by selecting the Adams/Insight tool.

2. In the treeview, select asc under the Simulation leaf.

3. From the File menu, point to Simulation, and then select Load All.

Adams/Insight displays the Work Space.

4. Fit the results.

5. Export the model to an .htm file.

6. Review the model in a Web browser.

Note: To get help, enter one of the following:

• On Windows: adams python j_asc_exp_mer.py -h

• On UNIX: mdadams2010 -c python j_asc_exp_mer.py -h exit

Note: You can also use the toolbar icons to run the trials.

Adams/InsightUsing Adams/Insight with Adams/Car

10

Using Adams/Insight with Adams/Car

Overview

This tutorial guides you through the process of designing an Adams/Car experiment and evaluating the

results using Adams/Insight. Both products are part of the Adams suite of software.

Adams/Insight is a stand-alone product that also works with Adams/View, Adams/Car, and Adams/Chassis. Adams/Insight lets you design sophisticated experiments for measuring the performance of your mechanical system model. It also provides a collection of statistical tools for analyzing the results of your experiments so that you can better understand how to refine and improve your model.

This guide assumes you know how to use Adams/Car. We also assume that you are familiar with parametric modeling capabilities including creating, modifying, and using points and design variables. In addition, you should know how to specify design objectives. For information on Adams/Car or other Adams products, refer to the online help.

This guide also assumes that you have a moderate level of knowledge about experimental design or Design of Experiments (DOE) and that you have access to in-depth references on them.



• Working with Results


OverviewThis section introduces you to the tutorial and gets you started. The tutorial guides you through the process of using Adams/Insight with Adams/Car to investigate transient dynamic response of a vehicle front-suspension model.


• About the Tutorial

• Starting Adams/Car

• Creating the Model

• Adams/Insight Interface


11Introducing the TutorialUsing Adams/Insight with Adams/Car

About the TutorialIn this tutorial, you design an experiment and analyze results for a front suspension assembly. You’ll run the assembly through a number of simulations that you set up in an experiment; fit your data to a polynomial to determine which factors most affect performance of the assembly; and publish results to an HTML page that you can view with a Web browser.

We’ll begin by importing the suspension from the Adams/Car shared database. The suspension assembly contains a Short Long Arm (SLA) independent front suspension that you will exercise through its range of motion while steering input is held constant in the straight-ahead position. You’ll monitor aspects of the assembly while you make modifications to it. Table 1 describes the model modifications used.

Table 1 Modifying and Monitoring Your Model

Starting Adams/CarThe section provides instructions on how to start Adams/Car on UNIX and Windows.

To start Adams/Car on UNIX:

1. Create a new working directory, acar.

2. Change to your working directory.

3. Type the command to start the Adams Toolbar at the command prompt and press Enter.

Parameters you’ll modify:

Performance attributes you’ll monitor: Description of event:

Outer tie-rod location

You’ll track the changes in toe angle as the suspension moves through its range of motion from jounce to rebound.

Toe angle is the projected angle the wheel plane makes with the ground when viewed from above the vehicle. Toe-in is considered positive, and toe-out is considered negative.

You’ll determine how modifying the geometric location of the outer tie-rod affects toe angle. A real-world application for this event includes:

• Determining if exaggerated changes in toe angle result in aggressive tire wear.

• Assessing your model against a manufacturing variation.

• Assessing packaging requirements for your model.


Note: You can skip this step if you previously used the Help‡Copy Examples To feature to copy all of the tutorials for Adams/Insight. Your working directory is ain_examples/acar.


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4. Select the Adams/Car tool .

The Adams/Car main window appears.

To start Adams/Car on Windows:

1. From the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to ACar, and then select Adams - Car.

2. Select OK in the Adams/Car welcome window.

3. From the File menu, select Select Directory to display the Select Directory dialog box.

4. Navigate to the drive and directory that you want to use as your working directory. If you need to create a new directory, select New Folder, enter acar, and then press Enter. Then, double-click the new acar.

5. Select OK.

Creating the ModelIn this section, you create the suspension assembly used in this tutorial.

To create the suspension assembly:

1. From the File menu, point to New and then select Suspension Assembly to display the New Suspension Assembly dialog box.

2. In the Assembly Name text box, type ainsight_susp.

3. Right-click in the Suspension Subsystem text box, point to Search, and then select the <acar_shared>subsystems.tbl database.

4. From the Select a File dialog box, select TR_Front_Suspension.sub, and then select Open.

5. Select the Steering Subsystem check box and right-click in the Steering Subsystem text box, point to Search, and then select the <acar_shared>subsystems.tbl database.

6. From the Select a File dialog box, select TR_Steering.sub, and then select Open.

7. Select OK in the New Suspension Assembly dialog box. When the assembly completes loading, close the Message Window. The main window should look like the image in Figure 1.

Note: If you previously used the Help‡Copy Examples To feature to copy all of the tutorials for Adams/Insight, your working directory is ain_examples/acar.


Figure 1 Suspension Assembly

Running a Simulation

Before you create your experiment, you’ll simulate the suspension model in Adams/Car to run a baseline parallel travel analysis. There are three important reasons to run a simulation before beginning your DOE analysis. They are:

• Running a simulation sets up the assembly for the type of analysis you will perform in Adams/Insight. This is important because the topology of the assembly can change slightly depending on the type of analysis performed.

• Running the simulation creates a simulation script that you use in the Adams/Insight experiment.

• You need to determine whether or not you can analyze the assembly in its current configuration.


14


1. From the Simulate menu, point to Suspension Analysis and then select the Parallel Wheel Travel. This displays the Suspension Analysis: Parallel Travel dialog box.

2. The Suspension Assembly text box should say ainsight_susp. In the Output Prefix text box, enter baseline.

3. In the Number of Steps text box, enter 30.

4. Set Mode of Simulation to interactive.

5. In the Bump Travel text box, enter 50.

6. In the Rebound Travel text box, enter -50.

7. Select OK to close the dialog box and begin the analysis. When the analysis has completed, close the Message Window.

Creating a Design Objective in Adams/Car

In this section, you’ll create a design objective that will be used as a response within Adams/Insight. For more information about objective objects that you are using in this section, refer to the Adams/View online help.

To create a design objective based on a request component:

1. From the main menu in Adams/Car, point to Simulate, point to DOE Interface, point to Design Objective, and then select New to display the Create Design Objective dialog box.

2. Modify the Name text box to: .ainsight_susp.toe_angle_objective.

3. Use the Definition by drop-down to select Existing Result Set Component (Request).

4. Right-click the Result Set Comp text box, select Result_Set_Component, and then select Browse to display the Database Navigator. In the Database Navigator, expand baseline_parallel_travel, expand testrig, expand toe_angle, select left, and then select OK.

5. Set Design Objective’s value is the to maximum absolute value during simulation.

You are interested in the maximum value of the toe because this is the value that you want to minimize as a result of your experiments.

6. Select OK.

Starting Adams/Insight

In this section, you’ll open Adams/Insight from Adams/Car and begin creating an experiment to measure the performance of a suspension model.

Note: Adams/Car places the full object hierarchy as part of the name in the Result Set Comp. text box.


To start Adams/Insight from Adams/Car:

1. From the main menu in Adams/Car, point to Simulate, point to DOE Interface, point to Adams/Insight, and then select Export.

The Export Assembly to Adams/Insight dialog box appears.

Figure 2 Export Assembly to Adams/Insight

2. Set Assembly to ainsight_susp, the assembly you want to export to Adams/Insight.


4. The simulation Simulation Script text box should already contain, .ainsight_susp.simulation_script. You can also browse for the script name in the Database Navigator by right-clicking in the text field, pointing to Simulation_Script, and then selecting Browse. Select simulation_script in the Database Navigator.

5. Select OK.

Adams/Car launches Adams/Insight and the Adams/Insight main window appears.

Adams/Insight InterfaceThis section describes what you see when Adams/Insight first opens. Figure 3 shows the main window as it appears when you first launch Adams/Insight. It includes the following items:

• Menu bar - Contains pull-down menus for File, Edit, Define, Simulation, Tools, and Help.

• Toolbars - Contain commonly used tools for accessing files, and creating and modifying designed experiments.

Note: On Windows, Adams/Car opens a command prompt window to launch Adams/Insight. T his window stays open until you close Adams/Insight. Do not manually close the command prompt window.


16

• Treeview - Displays a hierarchical list of objects that you can include in an experiment. The tree is especially useful for selecting and identifying objects when you are creating a design matrix.

• Viewport - The area of the window that displays parameters for modifying the objects you select from the treeview.

• Status bar - Displays messages and issues prompts during your Adams/Insight session.

Figure 3 Adams/Insight Main Window

Adams/Insight Toolbars

The Adams/Insight main window has four toolbars:

• Main (Experiments) toolbar - Lets you execute basic commands.

• Adams/Insight (Experiments Contents) toolbar - Helps you build and execute your experiment.

• Work Space toolbar - Lets you execute commands on the work space.

• Report toolbar - Lets you generate and export a report.

If you hold your mouse pointer over any tool, tip text appears giving a short description of the tool.

Tools in toolbars are arranged in the order that you’ll use them in the process of creating and executing your designed experiment. Depending on where you are in the process of creating an experiment, Adams/Insight enables or disables the tools (you can always display and undisplay them if you need to). This feature alerts you to the correct order of procedures to follow. For example, the Run simulations tool is disabled until you define required elements for a design matrix.

For more information on the toolbars, see the Adams/Insight online help.

17Creating and Running an ExperimentUsing Adams/Insight with Adams/Car


OverviewIn this section, you’ll create a design matrix and run a model through a number of simulations that you set up in the experiment.


• Creating a Design Matrix


Creating a Design MatrixThis section provides instructions on how to create a design matrix to evaluate the effect that modification of the outer tie rod location has on toe angle.

Automatically Created Factors

Adams/Car automatically creates a set of factors in Adams/Insight for Adams/Car entities. The table below shows the Adams/Car entity and attributes with the corresponding Adams/Insight factor.

Adams/Car Entity Attribute Affected Adams/Insight Design Factors

Hardpoint Location x, y, z

ac_bushing Translational Preload t_preload_x, t_preload_y, t_preload_z

Rotational Preload r_preload_x, r_preload_y, r_preload_z

Translation Offset t_offset_x, t_offset_y, t_offset_z

Rotational Offset r_offset_x, r_offset_y, r_offset_z

Translational Damping tx_damping_force_scale, ty_damping_force_scale, tz_damping_force_scale

Rotational Damping rx_damping_force_scale, ry_damping_force_scale, rz_damping_force_scale

Translational Stiffness fx_scaling_factor, fy_scaling_factor, fz_scaling_factor

Rotational Stiffness tx_scaling_factor, ty_scaling_factor, tz_scaling_factor


18

Promoting Candidates

The first step required to creating your designed experiment is to select the factors that you want to include in your design matrix. You select factors from the Candidates list in the treeview, and then promote them to the Inclusions list. Promoting candidates to inclusions causes them to become part of your design matrix.

To promote factors from candidates to inclusions:

1. In the treeview, click the + in front of Factors. Factors expands to reveal Inclusions and Candidates.

2. Continue by expanding Candidates, ainsight_susp, TR_Front_Suspension, ground, and hpl_tierod_outer. Under hpl_tierod_outer, you’ll see a list of factors that you can include in your design matrix.

3. Select the candidate, hpl_tierod_outer.x, and then move your cursor to the Adams/Insight toolbar and select the Promote to inclusion tool .

The candidate hpl_tierod_outer.x moves to the Inclusion list under Factors in the treeview.

ac_linear_bushing Translational Damping t_damping_x, t_damping_y, t_damping_z

Rotational Damping r_damping_x, r_damping_y, r_damping_z

Translational Stiffness fx_scaling_factor, fy_scaling_factor, fz_scaling_factor

Rotational Stiffness tx_scaling_factor, ty_scaling_factor, tz_scaling_factor

ac_spring Stiffness scale_factor

ac_linear_spring Stiffness stiffness

ac_damper Damping scale_factor

ac_linear_damper Damping damping

ac_bumpstop Force scale_factor

ac_reboundstop Force scale_factor

Adams/Car Entity Attribute Affected Adams/Insight Design Factors

Note: The treeview displays the full object hierarchy for each factor. This tutorial will only refer to the variable name. For example, the variable hpl_tierod_outer.x appears as TR_front_Suspension.ground.hpl_tierod_outer.x in the treeview.

Tip: To select more than one factor, hold the Ctrl key as you click. To promote the factors directly from the treeview, press the shortcut key F5.


4. Continue promoting the following factors:

• hpl_tierod_outer.y

• hpl_tierod_outer.z

The factors move from the Candidates to the Inclusions list.

The factors appear in your treeview as shown in Figure 4.

Figure 4 Treeview Showing Factors

Modifying Your Factors

After you promote your factors, you define parameters for them in the Factor form. To learn more about factor parameters, press the F1 key from the Factor form.

To modify your factors:

1. In the treeview, find the factors in the Inclusions list. Select the factor hpl_tierod_outer.x.

The Factor form appears in the viewport as shown next.


20

Figure 5 Factor Form

2. In the Factor form, set Abbreviation to tierod_x.

The default Abbreviation text string is automatically generated. Because Adams/Insight uses this abbreviation for table column headings, it is a good idea to change the generated text string to something short and meaningful to you.

3. Select the Settings tab, and then enter or verify the following:

4. Select the Description tab, and then set Units to mm.

The Units parameter is for annotation purposes. The units entered do not affect factor values.

• Type: Continuous

• Delta Type: Relative

• Settings: -5, 5


5. Use the defaults for all remaining fields.

6. Select Apply.

Adams/Insight saves your factor modifications.

7. Modify the parameters for the remaining factors, hpl_tierod_outer.y and hpl_tierod_outer.z, as you did in Step 2., above, using appropriate abbreviations.

Promoting Responses

Now that you have finished promoting and modifying your factors, the next step is to promote your responses for the experiment.

To promote responses from candidates to inclusions:

1. In the treeview, click the + in front of Responses.

The levels nested under Responses expand to reveal Inclusions and Candidates.

2. Continue expanding the levels under Candidates and ainsight_susp.

3. Select and promote the toe_angle_objective just as you promoted the factors in Step 3. above

The response moves from the Candidates to the Inclusions list.

Modifying Responses

The modifications you’ll make to the responses are minor. You’ll add units and change one of the parameters. To learn more about response parameters, press the F1 key from the Response form.

To modify responses:

1. In the treeview, under Responses, in the Inclusions list, select the response, toe_angle_objective. The Response form appears, shown next, in the viewport.

Hint: You can click the minus (-) sign in front of Factors to collapse that section of the treeview and save screen space.


22

Figure 6 Response Form

2. In the Response form, enter or verify the following:

• Output Char.: Absolute Maximum (grayed out, but selected)

• Abbreviation: toe_ang

• Units: degrees



4. Select Apply.

Adams/Insight saves your response modifications.

Setting Design Specifications

In this section, you’ll set the design objective and design type for your experiment. To learn more about setting design specifications, press the F1 key from the Design Specification form.

To specify your design objective:

1. In the toolbar, select the Set design specification tool , or in the treeview, expand the levels under Design, and then select Specification. You can also select the Define menu, point to Experiment Design, and select Set Design Specification.

Note: Output characteristics are grayed out when you use Adams/Insight with Adams/View and other Adams applications. The output characteristic is set by the originating CAE application, and is displayed in the Response form for information only.


24

The Design Specification form appears in the viewport as shown in Figure 7.

Figure 7 Design Specification Form

2. In the Design Specification form, make or verify the following selections:

Use defaults for all remaining options.

3. Select Apply.

4. Select the Define menu, point to Experiment Design, and then select Create Design Space.

• Investigation Strategy: DOE Screening (2 Level)

• Model: Linear

• DOE Design Type: Fractional Factorial


5. Select the Define menu, point to Experiment Design, and then select Create Work Space.

The Work Space appears in the viewport as shown in Figure 8. This table displays the work space matrix for the fractional-factorial experiment that you defined earlier in the tutorial. Adams/Car will run a simulation for each trial defined in this matrix. The column headings are sortable and sizeable. You can also select Work Space Review to view summary information for each factor and response in your experiment.

In the treeview, at the Design level, the letters D:W appear to indicate that the Design contains a successfully generated design work space.

Figure 8 Work Space Matrix Before Running Trials in Adams/Car

Running Your ExperimentOnce you’ve verified the information in the Work Space, you’re ready to run the simulations.

To run the simulation:

1. In the Adams/Insight toolbar, select the Run simulations tool . You can also select the Simulation menu, point to Build-Run-Load, and then select All.

2. In the information window that appears after the simulations are run, select OK.

Note: Clicking the Generate Work Space tool in the Adams/Insight toolbar performs Steps 4. and 5..

Note: Columns appear in the work space matrix in the order that you promote factors for inclusion.

Tip: Put your mouse pointer over column headings to display key information about the abbreviation shown.


26

Adams/Car opens and runs the simulations defined by your experiment. The Message window appears and displays standard Adams/Solver messages, which you can ignore for this tutorial.


OverviewThis chapter guides you through reviewing the results of your analysis; fitting your data to a polynomial to determine which factors most affect model performance; and publishing results to an HTML or SYLK file.


• Reviewing Results

• Fitting Results

• Publishing Results

Reviewing ResultsAfter Adams/Car completes the trials defined in your design matrix, you return to Adams/Insight interface to view the results.

To return to Adams/Insight:

1. From the main menu in Adams/Car, point to Simulate, point to DOE Interface, point to Adams/Insight, and then select Display.

The Adams/Insight Display dialog box appears.

2. Verify the name of your experiment, and then select OK.

Adams/Car undisplays and the Adams/Insight window opens.

To view your simulation results:

• In the treeview, under Design, select Work Space.

Note: This procedure builds, runs, and postprocesses all of the simulations within the Adams/Car session. We recommend that you break up the process flow into its separate phases using the MDI INSIGHT BUILD and MDI INSIGHT LOAD commands. This is especially important when you have more than 30 trials.

27Working with ResultsUsing Adams/Insight with Adams/Car

Simulation results from Adams/Car appear in the design matrix as shown in Figure 9.

Figure 9 Work Space Matrix After Running Trials in Adams/Car

• In the treeview, under Design, select Work Space Review.

The Work Space Review offers another means of reviewing the raw data found in the work space.

Fitting ResultsNow that Adams/Car has completed the trials defined in your work space matrix, you can use Adams/Insight to fit your results to a polynomial or a response surface. The purpose of fitting your results is to establish a relationship between the factors and responses that you selected for the work space matrix. Fitting results includes a multiple regression. You will be able to investigate the parts of the regression in the Summary, located in the treeview under Analysis, after completing the following steps. For more information on this topic, refer to the Adams/Insight online help.

To fit your results:

1. From the Adams/Insight toolbar, select the Fit results tool . You can also select the Tools menu, and then select Fit New Model.

The Model Properties Summary window appears. Here, you can enter information on your model.

2. In the Regression column, select the response, toe_angle_objective.

3. In the Display column, select the type of results you want to view. For example, Figure 10 shows an example of the Fit table.


28

Figure 10 Results Table with Fit for Regression

For definitions of the items in the results table, see online help.

The tables also provide you with a color code that indicates the soundness of your results:

Green indicates that all fit criteria meet or exceed highest fitting thresholds

Yellow indicates that the fit criterion may bear investigation

Red indicates that the fit criterion should be investigated

Publishing ResultsAdams/Insight lets you save your findings as either HTML or SYLK files. Once saved, you can use either a browser or spreadsheet program, such as Excel, to modify factors and see the effect on responses without performing full simulations.


To publish your results:

1. In the treeview, under Analysis, select Model_01, and then go to the Adams/Insight toolbar, and select the Export to Web, SLK, etc. tool . You can also select the File menu, point to Export, and then select Model. In the window that opens, set the File Type to HTML File.

The Save dialog box appears and prompts you to save your results as xxx.htm, where xxx is the name of your file.

2. Enter a name for your file and specify the path where you would like it to reside, and then select Save.

Adams/Insight saves your file in the directory that you specified.

3. Continue with the next section, Modifying Values Using a Web Browser, to learn how to view and use the results in the HTML file.

Modifying Values Using a Web Browser

Using the HTML page that you saved (see 1. through 3., above), you can modify the input factor values of your experiment and see the changes instantly reflected in the column that lists estimated responses. To learn more about making modifications to your experiment using an HTML or SYLK pages, refer to the Adams/Insight online help.

To modify your factors in an HTML page:

1. In a Web browser, open the HTML page you created for your experiment. Make certain the browser you use is able to read JavaScript.

The results of your experiment appear as shown in Figure 11.


30

Figure 11 HTML Results Page

2. Change the value for the first factor hpl_tierod_outer.x from 417 to 420, and then select Update.

The estimated responses adjust to reflect the new factor values. Notice that the value for the response, toe_angle_objective, reflects a change.

3. You can continue to vary the factor values and investigate how changes to them affect your responses. To learn more about analyzing the results of your experiment and publishing your results to HTML or SYLK pages, refer to the Adams/Insight online help.


Additional Information on the Web Page

In addition to the basic factor and response information that appears when you first open the HTML file in your Web browser, you can view response statistics and response effects as a function of each factor. To view this information, use the check boxes below the list of factors.

The check boxes are:

• Contributions - This check box appears if you specified tolerances for any factor. When present and selected, this check box displays the Tolerance Contributions table that provides the percent contribution of each factor to the tolerance of each response.

• Stats - Displays R2, R2 adjusted, P, and R/V statistics for each response.

• Effects - For each response, displays effects caused by varying each factor from its minimum to maximum value.

• Nonscalar - Displays composite responses in addition to the scalar responses.

• Plots- Opens a new window that displays a plot for each composite response (providing you specified composite responses).

• Info - This button displays a separate window that provides summary information about the investigation parameters for the current page. It also provides Web environment information that is valuable if you need to contact Adams technical support.

For more information on the controls and information provided by the HTML page, refer to the Adams/Insight online help.

Note: The check boxes only appear if you specified the corresponding data in your experiment. For example, if you didn’t specify tolerances for your factors, the Contributions check box will not display.

Adams/InsightUsing Adams/Insight with Adams/Chassis

32

Using Adams/Insight with Adams/Chassis

Overview

This tutorial guides you through the process of designing an Adams/Chassis experiment and evaluating

the results using Adams/Insight. Both products are part of the Adams suite of software.

This tutorial assumes that you:

• Know how to use Adams/Chassis.

• Are familiar with parametric modeling capabilities including creating, modifying, and using points and design variables.

• Know how to specify design objectives.

• Have a moderate level of knowledge about experimental design or Design of Experiments (DOE) and that you have access to in-depth references on them.

For information on Adams/Chassis or other Adams products, refer to the online help.





OverviewThis chapter introduces you to the tutorial and gets you started. The tutorial demonstrates how to use Adams/Insight with Adams/Chassis to perform a design of experiments (DOE) analysis. In this example, you will see the effect of front spring rate, front stabilizer bar diameter, and front lower ball joint position on understeer gradient.

The sections in this chapter are:


• Starting Adams/Chassis

• Setting up the Investigation



33Introducing the TutorialUsing Adams/Insight with Adams/Chassis

About the TutorialIn this tutorial, you design an experiment for a front suspension that you import. You simulate a front suspension to determine the effect of front spring rate, front stabilizer bar diameter, and front lower ball joint location on understeer gradient. Table 2 describes the model modifications used.


Starting Adams/ChassisYou begin this tutorial by starting Adams/Chassis.

To start Adams/Chassis on UNIX:

1. Create a directory for the tutorial.

2. Type the commands:

mkdir ain_achassis

cd ain_achassis

3. Type the command to start the Adams Toolbar at the command prompt and press Enter.

4. Select the Adams/Chassis tool .

The Adams/Chassis main window appears.

To start Adams/Chassis on Windows:

1. Use the Windows explorer to create the following folders:

{your user drive and directory}/ain_achassis

2. From the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to AChassis, and then select Adams - Chassis.

The Adams/Chassis main window appears.

3. To set the working directory, perform the following:

• From the Edit menu, select Preferences.


Performance attributes you’ll

monitor: Description of event:

Front spring rate Understeer gradient The constant radius event is quasi-static and simulates the vehicle under a series of lateral acceleration levels.

Front stabilizer bar diameter

Front lower ball joint position



34

• In the Current Working Directory text box, enter the name of the working directory you created in step 1. You can use the browse tool or type the absolute path to the directory.

• Select Save.

Setting up the InvestigationIn this section, you set up the investigation and launch Adams/Insight.

To set up the investigation:

1. From the toolbar, select the Test mode .

2. From the toolbar, select the New tool .

A new fingerprint entry appears in the bottom portion of the treeview.

3. In the top section of the treeview, expand Full Vehicle, Handling Analyses, and then double-click Swept Steer.

4. To use the default achassis_gs model, select OK in the informational window that appears.

achassis_gs_full_sys_swpt appears in the fingerprint, and the property editor displays the swept steer event attributes.

5. Enter or verify the following attributes:

Now, you will build and run the simulation, and then verify the results.

6. Select the Improve mode .

7. In the top section of the treeview, double-click achassis_gs_full_sys_swpt.

achassis_gs_full_sys_swpt moves to the Investigation Events folder in the bottom section of the treeview.

8. From the Setup Investigation tab in the property editor, select Create New Investigation.

9. Leave all remaining defaults.

10. Select Go.

Adams/Insight opens with your experiment loaded.

• Vehicle Velocity 100.0

• Final Lateral Acceleration

.4

• Turn direction Left

• Alignment Options Do not specify any auto-alignment

35Introducing the TutorialUsing Adams/Insight with Adams/Chassis



• Toolbars - Contain commonly used tools for accessing files, creating and modifying designed experiments.













36




OverviewIn this chapter, you’ll create a design matrix and run a model through a number of simulations that you set up in the experiment.




Creating a Design MatrixIn this section, you’ll create a design matrix to measure the performance of the front spring rate. This section includes:

• Promoting Factors

• Promoting Responses

• Setting Design Specifications

Promoting Factors



1. In the treeview, select the + in front of Factors. Factors expands to reveal Inclusions and Candidates.

2. Continue by expanding Candidates, properties, Front, achassis_gs_front_suspension, front_suspension, coil_spring, left, and achassis_gs_front_suspension_PA2_lsf. Under achassis_gs_front_suspension_pa2_lsf, you’ll see a list of design variables that you can include in your design matrix.

37Creating and Running an ExperimentUsing Adams/Insight with Adams/Chassis

3. Select the candidate front_suspension_coil_spring_left_achassis_gs_front_suspension_PA2_lsf_rate. This is the front coil spring rate.

4. Move your cursor to the Adams/Insight toolbar and select the Promote to inclusion tool .

The candidate front_suspension_coil_spring_left_achassis_gs_front_suspension_PA2_lsf_rate moves to the Inclusion list under Factors in the treeview.

The Factor form appears in the viewport as shown Figure 13.


5. Set Abbreviation to spr_rate.

6. Select the Settings tab.



38

7. Enter -20,20 in the Settings text box, and then select Relative Percent as the Delta Type. This sets the spring rate to vary from 80% to 120% of its nominal value.

8. Select Apply.

9. Expand properties, Front, achassis_gs_front_suspension, front_suspension, stabilizer_bar, and achassis_gs_front_suspension_beamx_sta. Select the candidate achassis_gs_front_suspension_beamx_sta_diameter.

10. Leave the Settings at their default values to modify the stabilizer bar diameter ± 4 mm.

11. Promote the candidate.

12. To vary the front lower ball joint position, expand properties, Front, achassis_gs_front_suspension, front_suspension, lower_ball_joint, and then left.

13. Hold down the Ctrl key and select front_suspension_lower_ball_joint_left_x and front_suspension_lower_ball_joint_left_y. Promote these factors.

Promoting Responses

The next step in defining the design matrix is to select response variables.

To promote responses:

1. In the treeview, select the + in front of Responses.


2. Under Candidates, you’ll see a list of responses that are potential candidates you can include in your design matrix. Expand e_001_achassis_gs_full_sys_swpt and then select e_001_achassis_gs_full_sys_swpt_Roll_grad (for roll gradient) to display the Response form shown in Figure 14 below.

Hint: You can select the minus (-) sign in front of Factors to collapse that section of the treeview and save screen space.



3. In the treeview, hold down the Ctrl key and select e_001_achassis_gs_full_sys_swpt_Roll_grad and e_001_achassis_gs_full_sys_swpt_Understeer_grad (for understeer gradient), and promote both candidates.

The responses move from the Candidates to the Inclusion list.


You use the Design Specification form to specify the design objective and design type for the experiment.

To specify the design objective:

1. In the Adams/Insight toolbar, select the Set design specification tool , or in the treeview, expand the levels under Design, and then select Specification.


40

The Design Specification form appears in the viewport as shown next.

Figure 15 Setting Design Sepcification Form - DOE Screening (2 Level)

2. In the Design Specification form, make the following selections:

• Investigation Strategy: DOE Response Surface

• Model: Quadratic

• DOE Design Type: CCF



3. Select Apply.

4. In the Adams/Insight toolbar, select the Generate Work Space tool , and verify the design matrix.

The Work Space appears in the viewport as shown in Figure 16. This table displays the work space matrix for the experiment that you defined earlier in the tutorial. The factors and responses that you selected appear as column headings; the runs (called trials) appear as row headings. Adams/Chassis will run a simulation for each trial defined in this matrix.


Figure 16 Design Matrix Before Running Trials in Adams/Chassis

Note: Columns appear in the design matrix in the order that you promote factors for inclusion.


42

Running Your ExperimentOnce you’ve verified the information in the Work Space, you’re ready to run the simulations in Adams/Chassis. This section includes:

• Saving the Experiment

• Using Adams/Chassis to Build and Run Models

Saving the Experiment

Before returning to Adams/Chassis you need to save the experiment and close Adams/Insight.

To save the experiment:


• Select Save from the File menu

• Press Ctrl+S

• Select the Save to file button

2. Exit Adams/Insight.

Using Adams/Chassis to Build and Run Models

You use Adams/Chassis to generate an Adams model (acf and adm files) that corresponds to each row in your design matrix. In this experiment, you create 28 models that correspond to the design matrix rows. Of the 28 runs, 25 are unique and the last three are duplicates of the first run.

To execute the models:

1. In Adams/Chassis, in the property editor, select the Execute Investigation tab.

2. Verify that the following are selected:

• Build Trials

• Run Trials

• Postprocess Trials

• Load Responses

• Launch Adams/Insight

3. Use defaults for all other options.

4. Select Go.

The command window shows Adams/Chassis building and running the 28 runs. Once execution completes, Adams/Insight opens.

Tip: Place your mouse pointer over column headings to display key information about the abbreviation shown.

43Working with ResultsUsing Adams/Insight with Adams/Chassis


OverviewThis chapter guides you through reviewing the results of your analysis, fitting your data to a polynomial to determine which factors most affect model performance, and publishing results to an HTML or SYLK file.



• Fitting Results


Reviewing ResultsAfter Adams/Chassis completes the trials defined in your design matrix, you return to Adams/Insight interface to view the results.



Simulation results from Adams/Chassis appear in the design matrix as shown in Figure 17.


44

Figure 17 Design Matrix After Running Trials in Adams/Chassis

Fitting ResultsNow that Adams/Chassis has completed the trials defined in your design matrix, you can use Adams/Insight to fit your results to a polynomial or a response surface. The purpose of fitting your results is to establish a relationship between the factors and responses that you selected for the design matrix. Fitting results includes a multiple regression. You will be able to investigate the parts of the regression


in the Summary, located in the treeview under Analysis, after completing the following steps. For more information on this topic, refer to the Adams/Insight online help.




2. In the Regression column, select the response, e_001_achassis_gs_full_sys_Understeer_grad.



For definitions of the items in the results tables, refer to the online help.





To refine the fit:

Here is a suggested list of steps that you can use to view and improve the fit:

1. Check R2 and interpret the ANOVA table.

• Verify residuals

• Remove outliers, if any

• Remove terms, if necessary


Note: The material in the following sections includes statistical terms related to DOEs. For explanations of these terms, refer to the Adams/Insight online help.


46

• Transform response, if necessary

• Change model order, if needed


Publishing ResultsAdams/Insight lets you save your files as either HTML or SYLK files. Once saved, you can use either a browser or spreadsheet program, such as Excel, to modify factors and see the effect on responses without performing full simulations.


1. In the treeview, under Analysis, select Model_01, go to the Adams/Insight toolbar, and then select the Export to Web, slk, etc. tool . You can also select the Tools menu, point to Export, and then select HTML Format.

The Save dialog box appears and prompts you to save your results as xxx.htm, where xxx is the name of your file.





Using the HTML page that you saved (see 1. through 3.), you can modify the input factor values of your experiment and see the changes instantly reflected in the column that lists estimated responses. To learn more about making modifications to your experiment using an HTML or SYLK page, refer to the online help.


1. In a Web browser, open the HTML page you created for your experiment. Make sure the browser you use is enabled to read JavaScript.

The results of your experiment appear as shown next.


Figure 19 HTML Page

2. Change the value for the factor achassis_gs_front_suspension_beamx_sta_diameter from 36 to 39, and then select Update.

3. If you use the plus or minus buttons to modify values, the responses change dynamically.

The estimated responses adjust to reflect the new factor values. Notice that the value for both responses variables reflect a change.



In addition to the basic factor and response information that appears when you first open the HTML file in your Web browser, you can view response statistics and response effects as a function of each factor. To view this information, use the check boxes below the list of factors. The check boxes are:


48

• Contributions - This check box appears if you specified a non-zero tolerance for any factor. When present and selected, this check box displays the Tolerance Contributions table that provides the percent contribution of each factor to the tolerance of each response.




• Plots- Opens a new window that displays a plot for each response.

• Info - This button displays a separate window that provides summary information about the DOE parameters for the current page. It also provides Web environment information that is valuable if you need to contact Adams technical support.


49Introducing the Suspension TutorialUsing Adams/Insight with Adams/View

Using Adams/Insight with Adams/View

Overview

This tutorial guides you through the process of designing an Adams/View experiment and evaluating the

results using Adams/Insight. Both products are part of the Adams suite of software.

Adams/Insight is a stand-alone product that also works with Adams/View, Adams/Car, and Adams/Chassis. Adams/Insight lets you design sophisticated experiments for measuring the performance of your mechanical system model. It also provides a collection of statistical tools for analyzing the results of your experiments so that you can better understand how to refine and improve your model.

This guide assumes you know how to use Adams/View. We also assume that you are familiar with parametric modeling capabilities including creating, modifying, and using points and design variables. In addition, you should know how to specify design objectives. For information on Adams/View or other Adams products, see the online help.

This guide also assumes that you have a moderate level of knowledge about experimental design or Design of Experiments (DOE) and that you have access to in-depth references on them.

• Introducing the Suspension Tutorial



• Using the Monte Carlo Method

Introducing the Suspension Tutorial

OverviewThis chapter introduces you to the suspension tutorial and gets you started. The tutorial uses a simple automotive example to illustrate the basics of Adams/Insight. Even if you don’t have an interest in automotive parts as a regular part of your job, we think you’ll find these instructions sufficient to help you focus on the capabilities of Adams/Insight.



• Starting Adams/View

• Creating a Modeling Database



Adams/InsightUsing Adams/Insight with Adams/View

50

About the TutorialThis tutorial guides you through the process of creating an experiment and analyzing results. In the initial sections, you’ll run a model through a number of simulations that you set up in the experiment, fit your data against a polynomial to determine which factors most affect the performance of your model, and publish your results to an HTML page that you can view with a Web browser.

This first tutorial uses an experiment for a model of a simple automotive front suspension system that you’ll import from an examples library. The model is a Short Long Arm (SLA), independent front suspension that you’ll exercise through its range of motion while the steering input is held constant in the straight-ahead position. You’ll monitor aspects of the model while you make modifications to it. Table 3 describes the modifications.


Starting Adams/ViewThe section provides instructions on how to start Adams/View on UNIX and Windows.

To start Adams/View on UNIX:

1. Copy the install_dir/ainsight/examples/ain_tut_101_aview.cmd to your working directory, where install_dir is where the Adams software is installed. If you cannot locate this directory, please see your system administrator.


Performance attributes you’ll monitor: Description of event:

Outer tie-rod location

You’ll track the changes in toe angle as the suspension moves through its range of motion from jounce to rebound.

Toe angle is the projected angle the wheel plane makes with the ground when viewed from above the vehicle. Toe-in is considered positive, and toe-out is considered negative.

You’ll determine how modifying the geometric location of the outer tie-rod affects toe angle. A real-world application for this event includes:

• Determining if exaggerated changes in toe angle result in aggressive tire wear.

• Assessing your model against a manufacturing variation.

• Assessing packaging requirements for your model.


Note: You can skip this step if you previously used the Help‡Copy Examples To feature to copy all of the tutorials for Adams/Insight. Your working directory is ain_examples/aview.


2. Type the command to start the Adams Toolbar at the command prompt, and then press Enter.

3. Select the Adams/View tool .


To start Adams/View on Windows:


2. From the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to Aview, and then select Adams - View.


Creating a Modeling DatabaseYou start this tutorial by creating a modeling database that contains a new model called Suspension Assembly.



2. If the Start in text box doesn’t show the path to your working directory, select the Browse button . Use the Select File dialog box to navigate to your working directory, and then select OK.

3. Select OK in the Welcome dialog box.

The File Import dialog box appears.

4. In the File to Read text box, enter ain_tut_101_aview.cmd or right-click and select Browse. You can then use the Select File dialog box to open ain_tut_101_aview.cmd.

5. Select OK.

Adams/View imports the file, and then displays the car suspension model. It also opens the Simulation Control dialog box.

Running a Simulation

Before you create your experiment, you’ll simulate the suspension model in Adams/View.


• In the Simulation Control dialog box, select the Start tool , and wait for the simulation to

finish.



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Starting Adams/Insight

In this section, you’ll open Adams/Insight from Adams/View and begin creating an experiment to measure the performance of a suspension model.


1. From the Main menu in Adams/View, point to Simulate, point to Adams/Insight, and then select Export.

The Adams/Insight Export dialog box appears.


3. Leave the default values in the Model and Simulation Script text boxes.

4. Select OK.

Adams/View launches Adams/Insight and the Adams/Insight main window appears.



• Toolbars - Contain commonly used tools for accessing files, creating and modifying designed experiments.




Note: On Windows systems, Adams/View opens a command prompt window to launch Adams/Insight. This window stays open until you close Adams/Insight. Do not manually close the command prompt window.


54


OverviewThis chapter guides you through the process of creating a design matrix and running the model through a number of simulations that you set up in the experiment.




Creating a Design MatrixIn this section, you’ll create a design matrix to measure the performance of the suspension model. This section includes:

• Promoting Candidates

• Modifying Your Factors

• Promoting Responses

• Modifying Responses






2. Continue by expanding Candidates, tut_101_aview, ground, and hpl_tierod_outer. Under hpl_tierod_outer, you’ll see a list of design variables that you can include in your design matrix.

3. Select the candidate, hpl_tierod_outer.x, and then move your cursor to the Adams/Insight toolbar and select the Promote to inclusion tool .

Note: The treeview displays the full object hierarchy for each design variable. This tutorial will only refer to the variable name. For example, the variable hpl_tierod_outer.x appears as ground.hpl_tierod_outer.x in the treeview.

55Creating and Running an ExperimentUsing Adams/Insight with Adams/View

The candidate hpl_tierod_outer.x moves to the Inclusion list under Factors in the treeview.

4. Continue promoting the following factors:

• hpl_tierod_outer.y

• hpl_tierod_outer.z

The factors move from the Candidates to the Inclusions list.

The factors appear in your treeview as shown in Figure 21.

Figure 21 Treeview Showing Factors



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Modifying Your Factors



1. In the treeview, find the factors in the Inclusions list. Select the factor hpl_tierod_outer.x.

The Factor form appears in the viewport, as shown next.


2. In the Factor form, set Abbreviation to tierod_outer.x

3. In the Description tab, set Units to mm.

The Units parameter is for annotation purposes. The units entered do not affect factor values.


4. In the Settings tab, enter the following:


6. Select Apply.


7. Modify the parameters for the remaining factors, hpl_tierod_outer.y and hpl_tierod_outer.z, just as you did in step 2., above, using appropriate abbreviations for each.

Promoting Responses

Now that you have finished promoting and modifying your factors, the next step is to promote your responses for the experiment.

To promote responses from candidates to inclusions:



2. Continue expanding the levels under Candidates and tut_101_aview. Under tut_101_aview, you’ll see a list of responses that are potential candidates you can include in your design matrix.

3. Select and promote the following responses just as you promoted the factors in step 3.:

• toe_left_REQ

• toe_right_REQ

The responses move from the Candidates to the Inclusion list.

Modifying Responses

The modifications you’ll make to the responses are minor. You’ll add units and change one of the parameters. To learn more about response parameters, press the F1 key from the Response form.

To modify responses:

1. In the treeview, under Responses, in the Inclusions list, select the response, toe_left_REQ.

• Type: Continuous

• Delta Type: Relative

• Settings: -5, 5

Tip: You can select the minus (-) sign in front of Factors to collapse that section of the treeview and save screen space.


58

The Response form appears, in the viewport, as shown next.


2. In the Response form, enter or verify the following:

Use the defaults for all remaining fields.

• Output Char.: Average

• Abbreviation: toe_left_REQ

• Units: degrees

Note: Output characteristics are grayed out when you use Adams/Insight with Adams/View and other Adams applications. The output characteristic is set by the originating CAE application, and is displayed in the Response form for information only.


3. Select Apply.

Adams/Insight saves your response modifications.

4. Select the second response toe_right_REQ, and make the similar modifications as in 2., above.




1. In the Adams/Insight toolbar, select the Set design specification tool , or in the treeview, expand the levels under Design, and then select Specification. You can also select the Define menu, point to Experiment Design, and select Set Design Specification.

The Design Specification form appears, in the viewport, as shown next.

Figure 24 Design Specification Form - DOE Screening


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2. In the Design Specification form, make or verify the following selections:


3. If you made any changes, select Apply.

4. Select the Define menu, point to Experiment Design, and then select Create Design Space.

5. Select the Define menu, point to Experiment Design, and then select Create Work Space.

The Work Space appears in the viewport as shown in Figure 25. This table displays the work space matrix for the full-factorial experiment that you defined above. Adams/View will run a simulation for each trial defined in this matrix. The column headings are sortable and sizeable. You can also select Work Space Review to view summary information for each factor and response in your experiment.


Figure 25 Work Space Matrix Before Running Trials in Adams/View

• Investigation Strategy: DOE Screening (2 Level)

• Model: Linear

• DOE Design Type: Full Factorial

Note: Selecting the Generate Work Space tool in the Adams/Insight toolbar performs Steps 4. and 5.

Note: Columns appear in the work space matrix in the order that you promote factors for inclusion.

61Working with ResultsUsing Adams/Insight with Adams/View

Running Your ExperimentOnce you’ve verified the information in the Work Space, you’re ready to run the simulations.


1. In the Adams/Insight toolbar, select the Run simulations tool . You can also select the Simulation menu, point to Build-Run-Load, and then select All.

Adams/View opens and runs the simulations defined by your experiment. The Adams/View Status bar displays messages showing simulation progress. The Message window also appears and displays warnings about joint locations, which you can ignore for this tutorial.

2. In the information box that appears, select OK.


OverviewThis chapter guides you through reviewing the results of your analysis, fitting your data to a polynomial to determine which factors most affect model performance, and publishing results to an HTML or SYLK file.



• Fitting Results

• Optimizing Results


Tip: Put your mouse pointer over column headings to display key information about the abbreviation shown.

Note: This procedure builds, runs, and postprocesses all of the simulations within the Adams/View session. We recommend that you break up the process flow into its separate phases using the MDI INSIGHT BUILD and MDI INSIGHT LOAD commands. This is especially important when you have more than 30 trials.


62

Reviewing ResultsAfter Adams/View completes the trials defined in your design matrix, you return to the Adams/Insight interface to view the results.


1. From the Main menu in Adams/View, select Simulate, point to Adams/Insight, and then select Display.

The Adams/Insight Display dialog box appears with the name of your current experiment.

2. Select OK.

Adams/View undisplays and the Adams/Insight window opens.



Simulation results from Adams/View appear in the design matrix as shown in Figure 26.

• In the treeview, under Design, select Work Space Review.

The Work Space Review offers another means of reviewing the raw data found in the work space.

Figure 26 Work Space Matrix After Running Trials in Adams/View


Fitting ResultsNow that Adams/View has completed the trials defined in your work space matrix, you can use Adams/Insight to fit your results to a polynomial or a response surface. The purpose of fitting your results is to establish a relationship between the factors and responses that you selected for the work space matrix. Fitting results includes a multiple regression. You will be able to investigate the parts of the regression in the Summary (located in the treeview under Analysis) after completing the following steps. For more information on this topic, refer to the Adams/Insight online help.




2. In the Regression column, select the response, toe_left_REQ.




64

For definitions of the items in the results tables, refer to the online help.





To review the fit:

Here is a suggested list of steps that you can use to view and modify the fit. For more information on evaluating the fit, refer to Evaluating the Fit in the Adams/Insight online help.


• Verify residuals

• Remove outliers, if any

• Remove terms, if necessary


• Transform response, if necessary

• Change model order, if needed


4. Monitor error DOF in the fit display.

As you attempt these suggestions, go back through the following steps:

1. Running a Simulation

2. Starting Adams/Insight

3. Promoting Candidates

4. Promoting Responses

5. Modifying Responses

6. Setting Design Specifications

7. Running the Experiment

8. Reviewing Results

9. Fitting Results

Optimizing ResultsYou can perform single-objective and multi-objective optimization using Adams/Insight. Single-objective optimization involves trying to achieve a target for one scalar response; multi-objective optimization involves more than one scalar response.

You can optimize your results by:


• Updating Factor Settings

• Updating Design Objective (Response) Settings

Updating Factor Settings

Here you will learn how to optimize your model by changing factors.

To optimize your model by changing factors:

1. From the Tools menu, select Optimize Model.

The window displays your model’s factors and responses (design objectives). Only scalar responses are shown in the window. Composite responses are not displayed.

Figure 28 Optimization Window

2. Modify the current value of one or more factors.

To change the values, use the sliders next to each factor, or enter new values in the corresponding Value text boxes.

3. Press Update.


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Adams/Insight updates the responses to reflect the changes you made to the factors. Use the Reset button to return to the nominal values for each factor. Use Reload to reload all of the optimization settings.

Updating Design Objective (Response) Settings

Here you will learn how to optimize your model by changing design objectives (responses).

To optimize your model by changing design objectives:

1. Modify the response values as appropriate. You can change the following values:

• Oper: Changes the operator.

• Target: Changes the target value for the response.

• Weight: Applies a higher priority in achieving optimization for that factor. Weight values can range from 0.0 to 1.0, where 1.0 indicates greater importance.

2. Select Fixed next to any factor that you don’t want changed during the optimization.

3. Press Run.

Adams/Insight updates the factor values to reflect the changes you made to the responses. Use the Reload button to return to the nominal values for each factor/response.

Publishing ResultsAdams/Insight lets you save your results in .html, .slk, .bas (Visual Basic), and .m (MATLAB) formats. Once saved, you can use other utilities, such as a browser or spreadsheet program, to modify factors and see the effect on responses without performing full simulations.


1. In the treeview, under Analysis, select Model_01, and then go to the Adams/Insight toolbar, and select the Export to Web, SLK, etc. tool . You can also select the File menu, point to Export, and then select Model. In the window that opens, set the File Type to HTML File.

2. The Save dialog box appears and prompts you to save your results as xxx.htm, where xxx is the name of your file.

Note: To save your results, select Write and enter the name of the file to which you want to save. You can save to a number of different formats, including a .cmd file, which can then be read back into Adams/View to set the model using the specified factor settings.

Note: The in the slider area identifies the current value.

Note: To save your results to a text file, select Write and enter the name of the file and file type to which you want to save.






Using the HTML page that you saved (see 1. through 3. above), you can modify the input factor values of your experiment and see the changes instantly reflected in the column that lists estimated responses. To learn more about making modifications to your experiment using an HTML or SYLK page, refer to the Adams/Insight online help.


1. If not already displayed, display the Report toolbar by right-clicking a blank space in the toolbar area, and then selecting Report.

2. Select the Display last exported item tool .

Your default browser opens the HTML page you created for your experiment. Make sure the browser you use is able to read JavaScript.


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The results of your experiment appear as shown in Figure 29.

Figure 29 HTML Page of Results

3. Change the value for the first factor hpl_tierod_outer.x from 417 to 420, and then select Update.

The estimated responses adjust to reflect the new factor values. Notice that the value for only one of the responses, toe_left_REQ, reflects a change. Because the Adams model you’re working with is an independent suspension, in which the right tie rod is not coupled with the left tie rod, the changes in the factor values you made only affect the left side of the suspension.


5. Close your browser window.

6. Exit Adams/Insight.

7. Exit Adams/View.

69Using the Monte Carlo MethodUsing Adams/Insight with Adams/View


In addition to the basic factor and response information that appears when you first open the HTML file in your Web browser, you can view response statistics and response effects as a function of each factor. To view this information, use the check boxes below the list of factors. The check boxes are:

• Contributions - This check box appears if you specified a non-zero tolerance for any factor. When present and selected, this check box displays the Tolerance Contributions table that provides the percent contribution of each factor to the tolerance of each response.




• Plots- Opens a new window that displays a plot for each composite response (providing you specified composite responses).

• Info - This button displays a separate window that provides summary information about the DOE parameters for the current page. It also provides Web environment information that is valuable if you need to contact Adams technical support.


Using the Monte Carlo Method

OverviewThis chapter introduces you to the Monte Carlo method of analysis. The tutorial uses a launch vehicle/spacecraft separation example to illustrate the mechanics of the solution.



• Starting Adams/View

• Creating a Modeling Database

• Running the Simulation

• Starting Adams/Insight


• Running the Experiment



70


Introducing the Monte Carlo MethodThe Monte Carlo method of analysis as applied to mechanical systems involves several runs with varying parameters. The goal is to provide a statistical basis for predicting mechanism performance. The foundation of the method involves characterizing parameters with a Probability Density Function (PDF). This function must be specified for each parameter that will be varied in the analysis. Examples of parameters include spring stiffnesses, damping rates, and initial rotation rates.

To learn more about the Monte Carlo method, consult a technical library. The following book is an excellent reference:

• James E. Gentle. Random Number Generation and Monte Carlo Methods. Springer-Verlag, 1998.

You can also refer to the following online references:

• Introduction to Monte Carlo Methods at http://www.phy.ornl.gov/csep/CSEP/MC/MC.html.

• Numerical Recipes at http://www.nr.com.

Starting Adams/ViewThe section teaches you how to start Adams/View on UNIX and Windows.

To start Adams/View on UNIX:


2. Type the command to start the Adams Toolbar at the command prompt, and then press Enter.

3. Select the Adams/View tool .



http://www.phy.ornl.gov/csep/CSEP/MC/MC.html

http://www.nr.com


To start Adams/View on Windows:


2. From the Start menu, point to Programs, point to MSC.Software, point to MD Adams 2010, point to Aview, and then select Adams - View.


Creating a Modeling DatabaseYou start this tutorial by creating a modeling database that contains a new model.



2. If the Start in text box doesn’t show the path to your working directory, select the Browse tool . Use the Select File dialog box to navigate to your working directory, and then select OK.

3. In the Welcome dialog box, select OK.

4. In the File to Read text box, enter ain_tut_141_aview.cmd or right-click and select Browse. You can then use the Select File dialog box to open ain_tut_141_aview.cmd.

5. Select OK.

Adams/View imports the command file, and then displays the launch vehicle model.



72

6. Zoom in on the top portion of the vehicle. Note the four forces between the adapter frustum and the spacecraft (they’re circled in the following figure).

Figure 30 Launch vehicle model

Running the SimulationBefore you create your experiment, you’ll simulate the launch vehicle model in Adams/View. Here, you will run and animate the simulation.


1. From the Simulate menu, select Scripted Controls.

2. In the Simulation Script Name text box, enter .separation.Sep_script. Use defaults for all other simulation options.

3. Select the Play tool , and wait for the simulation to finish.


4. From the Review menu, select Animation Controls.

5. Select the Play tool and wait for the animation to finish.

Note that the four forces push the spacecraft off of the launch vehicle.

6. Close the Animation Controls dialog box.


Identifying Measures

Here, you will identify the measures in the model.

To identify the measures:

1. From the Build menu, point to Measure, and then select Display.

The Database Navigator opens, displaying the four measures for the model: three angular velocity components and a translational separation velocity component.

For information on measures, refer to the Adams/View online help.

2. Close the Database Navigator.

3. From the Simulate menu, point to Design Objective, and then select Modify.

The Database Navigator opens.

4. Select separation.

The Database Navigator displays the four objectives representing the ending simulation values for each measure.

For information on design objectives, refer to the Adams/View online help.

5. Close the Database Navigator.

Starting Adams/InsightIn this section, you’ll open Adams/Insight from Adams/View and begin creating an experiment to measure the performance of a launch vehicle model.


1. From the Main menu in Adams/View, point to Simulate, point to Adams/Insight, and then select Export.

2. Complete the Adams/Insight Export dialog box as shown below:

Figure 31 Adams/Insight Export Dialog Box

3. Select OK.


74

Adams/View launches Adams/Insight and the Adams/Insight main window appears.

In the treeview of Adams/Insight, note that the model has eight factors and four responses.

Creating a Design MatrixIn this section, you’ll create a design matrix to measure the performance of the launch vehicle model. This section includes:

• Promoting Candidates

• Modifying Factors






2. Continue by expanding Candidates, and then separation. Under separation, you’ll see a list of design variables that you can include in your design matrix.

3. Select all of the candidates by holding down the Ctrl key while mouse-clicking each candidate.

4. Move your cursor to the Adams/Insight toolbar and select the Promote to inclusion tool .

The candidates move to the Inclusion list under Factors in the treeview.



Note: On Windows systems, Adams/View opens a command prompt window to launch Adams/Insight. This window stays open until you close Adams/Insight. Do not manually close the command prompt window.

Tip: To promote the factors directly from the treeview, press the shortcut key F5.

Tip: You can select the minus (-) sign in front of Factors to collapse that section of the treeview and save screen space.


6. Continue expanding the levels under Candidates and separation. Under separation, you’ll see a list of responses that are potential candidates you can include in your design matrix.

7. Select and promote all of the responses just as you promoted the factors in 3..

The responses move from the Candidates to the Inclusion list as shown in Figure 32.

Figure 32 Treeview Showing Inclusions

Modifying Factors



1. In the treeview, find the factors in the Inclusions list. Select the factor spacecraft_lxx.

2. In the Factor form, select the Variation tab.

3. Set Distribution to Normal.

4. Select the Calculator tool next to the Standard Deviation text box.

The Specify Variation Characteristics dialog box appears.

5. Select Tolerance, enter 40 in the corresponding text box, and then select OK.

6. Select Apply in the Factor form.


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7. Modify the parameters for the remaining factors as follows. Be sure to select Apply after modifying each factor.




1. In the Adams/Insight toolbar, select the Set design specification tool .

Factor: Variation Distribution: Tolerance:

spacecraft_lyy Normal 30

spacecraft_lzz Normal 35

spacecraft_mass Uniform 23

spring_rate_nx Normal 200

spring_rate_nz Normal 200

spring_rate_px Normal 200

spring_rate_pz Normal 200


2. Complete the Design Specification form as shown next.

Figure 33 Variation - Monte Carlo

3. Select Apply.

4. To create the work space, select the Generate Work Space tool .

The Work Space appears in the viewport. Note that the response columns are empty.

5. From the treeview, under Design, select Work Space Review.


78

6. Select a factor and view its histogram plot.

Running the ExperimentOnce you’ve verified the information in the Work Space, you’re ready to run the simulations.


1. In the Adams/Insight toolbar, select the Run simulations tool. You can also select the Simulation menu, point to Build-Run-Load, and then select All.

Adams/View displays and runs the simulations defined by your experiment.

2. In the information window that opens, select OK.

Reviewing ResultsAfter Adams/View completes the trials defined in your design matrix, you return to the Adams/Insight interface to view the results.


1. From the Main menu in Adams/View, point to Simulate, point to Adams/Insight, and then select Display.

The Adams/Insight Display dialog box appears.

2. Verify the name of your current experiment, and then select OK.

The Adams/Insight window replaces the Adams/View window.


1. In the treeview, under Design, select Work Space.

Simulation results from Adams/View appear in the response columns.

2. In the treeview, under Design, select Work Space Review.

3. Select the first response, r_01.

The histogram for this response appears above the table as shown in Figure 34. The table includes mean, variance, standard deviation, minimum, maximum, and range values for each factor and each response.

4. Select the other responses to view their histograms.

Note: This procedure builds, runs, and postprocesses all of the simulations within the Adams/View session. We recommend that you break up the process flow into its separate phases using the MDI INSIGHT BUILD and MDI INSIGHT LOAD commands. This is especially important when you have more than 30 trials.


Figure 34 Histogram Plot for r_01


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1Dialog Box - F1 Help

Dialog Box - F1 Help

Adams/InsightAutomatic Refinement - Remove Terms

2

Automatic Refinement - Remove TermsAllows you to specify automatic removal of terms for your experiment. Learn about Automatic (stepwise) refinement.

Tools -> Refine Model Automatically -> Remove Terms

For the option: Do the following:

Controls

Hierarchy Select one of the following:

• None: Stepwise refinement adds or removes terms regardless of the other terms that are in the polynomial. For example, stepwise refinement may remove the term x1 while leaving x12. Stepwise refinement will never remove the constant term.

• Preserve Factors: If a term is active, then all mathematical factors of the term are also active. For example, if x1*x22 is active, then x1, x2, x222, and x1*x2 must all also be active. This preserves variable offsets, such that offsetting a variable does not change the form of the polynomial.

• Preserve Term Order: If a term of a particular order in a particular variable is active, then all other terms of that order that include that variable must also be active. In effect, this applies linear/quadratic/cubic settings on each variable so that each variable may be a different order. For example, the refined regression may be quadratic in x1 but only linear in x2. In this case, all quadratic x1 terms and all linear x2 terms are included.

• Preserve Polynomial Order: This selects linear, quadratic, or cubic for the entire polynomial. This preserves variable rotations, such that rotating or linearly recombining variables doesn't change the form of the polynomial.

Note: These hierarchy options are in order of decreasing flexibility and computational time. No hierarchy allows the most flexibility, but takes the longest to run and may produce a polynomial that does not make intuitive sense. Preserving polynomial order is the least flexible, but runs quickly and produces the most well-rounded polynomial.

Criteria Select one of the following:

• P: Adams/Insight includes terms or sets of terms that have a P value less than the threshold entered below.

• F: Adams/Insight includes terms or sets of terms that have an F value greater than the threshold entered below.

3Dialog Box - F1 HelpAutomatic Refinement - Remove Terms

Threshold Enter the threshold on which the criteria (above) is based.

Cache Select the type of information to cache.

For most experiments, you can select both cache options. Adams/Insight stores intermediate results during the refinement and, in most cases, will greatly speed up the refinement process. The data storage does take computer memory, however, so it is possible that for some very large models it will be necessary to turn one or both off. If it is necessary to turn one option off, turn off the Term caching as it takes the most memory.

Monitor

Messages Select the type of messages you want to view during the refinement process.


Adams/InsightAutomatically Remove Outliers

4

Automatically Remove OutliersTools -> Refine Model Automatically -> Remove Outliers

Allows you to remove individual trials from the fit. Learn about Refinement.


Regressions Select the regression model from which you want the trial(s) removed.

To select multiple trials, hold down the Ctrl key while selecting.

Select All Select to highlight (and remove) all regressions listed.

Clear All Select to unselect (clear) all selected regressions.

Filter Using Select the criteria used to search for trials.

• Actual.

• Estimate.

• Raw Residual. See Residuals.

• Studentized. See Studentized Residuals.

• Cook's. See Cook’s Statistics.

Exclude Runs Select a filter type for excluding trials. Enter the limits below.

Upper Limit Enter the upper limit for the filter.

Lower Limit Enter the lower limit for the filter.

Apply each exclusion to Select one of the following:

• Only the regression with the outlier: The filtered trials will only be removed from the corresponding regressions.

• All regressions: The filtered trials will be removed from all regressions.

Note: You cannot undo removed trials

5Dialog Box - F1 HelpCommand Line Arguments

Command Line Arguments Usage:

ainsight.py [options] [file]

Argument Description

-bg R G B Window/background color, 0 <= RGB <= 255

-deskcolor Use desktop color for window/background

-diag [item] Runs one of the specified application installation diagnostics. Use -diag -help to display available options.

-e file Specifies experiment file

-gtdiff F1 F2 Graphically displays the difference between two text files (primarily used for Adams/Insight ASC).

-reuse file Reuse experiment settings from this experiment file

-experimental Turn on experimental features

-h Display this text and exit

-splash icon Splash image file (.bmp, .png, .xpm)

-subprocess Being called from another application

-v Turn on verbose messages

-wide Start Adams/Insight in a wider window

file Specifies experiment file

Adams/InsightDesign Inclusion

6

Design Inclusion Adams/Insight enables you to import a full or partial design matrix whose factor settings will be included when the complete workspace is generated. Referencing an inclusion matrix is only applicable for D-Optimal design types.

7Dialog Box - F1 HelpDesign Space

Design Space This table displays the Design Space (factors) for each Trial in your experiment. The column headings are sortable and sizeable. You can also select Work Space Review to view summary information for each factor and response in your experiment. See Design Work Space Review for more information.

Columns appear in the design space matrix in the order that you promoted factors for inclusion.

Put your mouse pointer over column headings to display key information about the abbreviation shown.

Adams/InsightDesign Specification

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Design Specification Defines the design of your experiment.


Investigation Strategy Select one of the following:

• Study - Perimeter

• Study - Sweep

• DOE Screening (2 Level)

• DOE Response Surface

• Variation - Monte Carlo

• Variation - Latin Hypercube

Learn more about Investigation Strategies.

Model Select one of the following:

• Linear

• Interactions

• Quadratic

• Cubic

• None. See None Option.

DOE Design Type Select one of the following:

• Plackett-Burman

• Fractional Factorial

• Full Factorial

• Box Behnken

• CCF (Central Composite Faced)

• D-Optimal

• Latin Hypercube

Learn more about DOE Design Types.

9Dialog Box - F1 HelpDesign Specification

Candidate Runs This option is only applicable to D-Optimal designs. It specifies the size of the candidate pool from which the D-Optimal algorithm chooses rows for a design matrix.

• All - Uses all of the candidate runs that are in a full factorial design for a given collection of factors (potentially a very large number).

• Random - Limits the number of Candidates, thus reducing the run time of the D-Optimal algorithm. If you choose Random, enter a value for Number of Candidate Runs.

Number of Runs Indicates a numeric value of unique Trials (rows) in the Design Space and Work Space.

• Value - Adams/Insight specifies a value for all designs types except Plackett-Burman and D-Optimal.

• Range - For Plackett-Burman and D-Optimal designs, this option provides a range of values for the possible number of trials. Generally, you can increase the fidelity of your final results as you increase the number of trials in the experiment.

Number of Center Points

Specifies the number of center points to include in the Number of Runs specified above. This option applies to the following:

• D-Optimal DOE design types - Be sure to set an adequate number of runs so that you have enough trials to create the response surface.

• Variation investigation strategies - The first trials are held at the nominal condition of the system. The scatter plots will also reflect this with the trial identifier in the plot being displayed in red. For example, if you specify Variation - Latin Hypercube, Number of Runs=100, and Number of Center Points=1, then Adams/Insight generates 100 trials with Trial 1 being set to the nominal condition.

Number of Candidate Runs

This option is applicable only to D-Optimal designs. It specifies the size of the candidate pool from which the D-Optimal algorithm chooses rows for a design matrix.

• All - Uses all of the candidate runs that are in a full factorial design for a given collection of factors (potentially a very large number).

• Random - Limits the number of Candidates, thus reducing the run time of the D-Optimal algorithm. If you choose Random, enter a value for Number of Candidate Runs.


Adams/InsightDesign Specification

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Run Order Select one of the following:

• Standard - You can use this option if you are running an analytical Design of Experiment (DOE), and do not expect the order of the runs to have a significant effect on the results.

• Random - This is generally the run order to use for physical DOEs. For example, if your response varies depending on when you measure it during the course of a day, you should randomize the run order in order to capture the overall behavior of the system.

• Ease of Adjustment - This option is also more applicable for physical DOEs. It affects the Work Space and Design Space matrix when you set a Factor attribute to Ease of Adjustment.


11Dialog Box - F1 HelpDesign Work Space

Design Work Space This table displays the work space matrix (factors and responses) for your experiment. Adams/View will run a simulation for each Trial defined in this matrix. The column headings are sortable and sizeable. You can also select Work Space Review to view summary information for each factor and response in your experiment. See Design Work Space Review.

In the treeview, at the Design level, the letters D:W appear, to indicate that the design contains a successfully generated design work space.

Columns appear in the work space matrix in the order that you promoted factors for inclusion.

Put your mouse pointer over column headings to display key information about the abbreviation shown.

Adams/InsightDesign Work Space Review

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Design Work Space Review This form enables you to do preliminary investigations of the raw data from the work space. This can be achieved by graphically reviewing the histograms which depict the distribution of the column values. The bottom of the form offers a summary table of statistics for each work space column. The table includes mean, Variance, Standard Deviation, minimum, maximum, and range, for each work space column. The table can be row sorted, based on a specific column. Click on a column header and the table sorts based on the values in that column. By clicking a second time on the column header, the sort is reversed. (After a sorting operation, if you want to view the table as it was when you first entered the form, simply click on the Work Space Review leaf on the application tree.) The Factor abbreviation or Response abbreviation string is used to help identify which column is being presented in the table as well as the label on the histogram and dependent axis.

13Dialog Box - F1 HelpFactor Form

Factor Form

(Treeview) -> Factors -> Inclusions/Candidates -> Factor name

Defines parameters for your factors. Learn more about Factors.


Name Enter a name to identify a factor. The factor name appears in the treeview.

Abbreviation Modify the abbreviation that Adams/Insight creates for your factor name. Adams/Insight creates a shorter version of factor names to conserve space. Abbreviated names appear in column headings of the Design Space and Work Space tables, and in the list of terms in the results tables. By using short strings as the abbreviation, it will make it easier to understand the terms of the equations which are generated later and review the relative importance of the respective factors.

Nominal Value Enter the nominal value of the factor, which is also known as the center point. The model inputs vary relative to this value when Delta Type is defined as Relative or Relative Percent.

Settings Tab

Settings Specify the numerical values that the Delta Type references. You must separate multiple values with commas, such as: -5, 5.

Tolerance • If doing a Design of Experiment (DOE), enter a balanced manufacturing variation. This value is used to compute the response or functional variation when you publish results to an SYLK or HTML file.

• If doing a Variation experiment, enter either three times the standard deviation for a normal distribution, or half the width for a uniform distribution (see Variation Distribution next).

Note: If you set all the Tolerance values to zero (for all factors), no tolerance Contribution matrix will be written to the .html file.

Type Enter the type of modifications allowed for a factor value.

• Continuous - Allows a factor value to be modified within a range you define in Settings. A continuous factor must have all real numeric values.

• Discrete - Specifies that a factor value be modified to an exact value you define in Settings.

Adams/InsightFactor Form

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Delta Type Define how factor values can be modified during a Trial.

• Absolute - Defines a factor using the values in Settings.

• Relative - Defines a factor value by adding the values in Settings + Nominal Value.

• Relative Percent - Defines a factor using the values in Nominal Value + ((Settings/100) * Nominal Value)

Ease of Adjustment Specify an Easy, Moderate, or Hard sorting option in the Design Sheet and Work Space to minimize the number of times the most difficult (hard) factor needs to be modified. See more information on Sorting.

Variation Tab


15Dialog Box - F1 HelpFactor Form

Distribution Specify the type of Variation Distribution to perform.

• None - Do not perform a Variation Distribution. This is applicable if you're running a DOE and you want to review the variation in the .htm or .slk published reports based on the Tolerance specified in the Settings tab.

• Normal - Specifies a Gaussian distribution. Enter the standard deviation in the Standard Deviation text box. The mean is assumed to be the Factor Nominal Value.

• Lognormal - Specifies a Lognormal distribution. Enter the standard deviation and shape parameter in the Standard Deviation and Shape text boxes, respectively. The mean is assumed to be the Factor Nominal Value.

• Weibull - Specifies a Weibull distribution. Enter the standard deviation and shape parameter in the Standard Deviation and Shape text boxes, respectively. The mean is assumed to be the Factor Nominal Value.

• Uniform - Specifies a uniform distribution. Enter the maximum and minimum values for this distribution in the Cutoff Limits text boxes. These are values plus and minus the tolerance relative to the Nominal Value.

• Discrete - Specifies a discrete distribution. Enter an even number of parameters in the Parameters text box.

• User - Select to specify your own distribution type using a user-written custom method in Python. Use the Parameters text box to enter your custom values. You can also specify values in the Cutoff Limits text boxes. For more information on creating your own distribution, see the example in <installDir>/ainsight/examples/ain_fac_var_*.py.

Note: For Normal, Lognormal, Weibull, and Discrete Distributions, you can use Cutoff Limits to introduce a clipping effect on the generated distribution. The Min/Max values are -/+ relative to the nominal, that is, Min = -1, Max = 1 would limit values to within 1 of the nominal.

Standard Deviation Enter the standard deviation for a Normal, Lognormal, or Weibull distribution.

Shape Enter the shape parameter for a Lognormal or Weibull distribution.

Cutoff Limits Enter the minimum and maximum values for a Uniform distribution. You can also use these text boxes to specify values for your user-defined distribution.


Adams/InsightFactor Form

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Parameters Enter an even number of parameters for a discrete distribution. Enter them in X, P pairs, where X is the value and P is the probability of that value. Because Adams/Insight sorts and normalizes the values, they don't have to be in order of increasing X, and the Ps don't have to add up to 1.

You can also use this text box to specify values for your user-defined distribution.

Distribution Profile Displays an graphical representation of the selected distribution.

Variation Details Select to display the details of the distribution, including upper/lower limits and mean value.

Tie Tab

Tie Type Enter one of the following:

• Scale

• Offset

After choosing the tie type, select Apply to make it effective. Learn more about Tying Factors.

Tied Factors table Depending on the value entered in Tie Type, specify the Scale or Offset for each factor. See Examples.

After making a change to this table, select Apply to make it effective.

Description Tab

Description Add a description to a factor. The description appears in your results when you export them to a SYLK or HTML page.

URL (Universal Resource Locator)

Add a URL (Web address) to link to a factor. If you publish your experiment results to an HTML page, you can use this option to link the factor to that page. A fully qualified URL (identifying protocol, server and specification) is recommended. For example http://support.adams.com/kb/csearch.asp. An easy way to get this string is simply cut a valid URL from your browser location window.

Variable When using Adams/Insight in stand-alone mode, this text box is optional. When using Adams/Insight in conjunction with an Adams modeling application, this field contains the name of a Design Variable, point name, or UDE parameter that gets modified.

Units Add units to describe your factors. Units are annotations only; they do not affect factor values.


17Dialog Box - F1 HelpFactors Candidates Table (All)

Factors Candidates Table (All)(Treeview) -> Factors -> Candidates

Displays a summary of all of the factors not included in your current experiment. Learn more about Factors.

Adams/InsightFactors Inclusions Table (All)

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Factors Inclusions Table (All)

(Treeview) -> Factors -> Inclusions

Displays a summary of all of the factors included in your current experiment. Learn more about Factors.

19Dialog Box - F1 HelpFactors Table (All)

Factors Table (All)

(Treeview) -> Factors

Displays a summary of all of the factors in your experiment. Learn more about Factors.

Adams/InsightFile Export Matrices

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File Export Matrices

File -> Export -> Design Space or Work Space or Full Work Space

Specifies the file to which to export matrices.

21Dialog Box - F1 HelpFile Export Model

File Export Model

File -> Export -> Model

Enables you to select various formats to which to export the regression model.

You can select the following:

• .htm (interactive .htm file utilizing JavaScript)

• .slk (spreadsheet neutral file able to be read into spreadsheet programs such as Excel)

• .bas (Visual Basic set of functions, which can be used in Excel)

Learn more about Exporting Data.

Adams/InsightFile Import Matrices

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File Import Matrices Specifies the file from which to import matrices.

23Dialog Box - F1 HelpFile Open

File Open

File -> Open

Opens an Adams/Insight experiment.

Adams/InsightFile Save As

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File Save As

File -> Save As

Specifies the file from which to import responses and/or factors.

25Dialog Box - F1 HelpFind Directory

Find Directory

File -> Select Directory

Defines the directory in which to store all of your experiments.

To select a directory:

1. Enter the name of the directory you want to use.

2. Select OK.

Adams/InsightImport Experiment Factors/Responses

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Import Experiment Factors/Responses

File -> Import -> Inclusion Factors or Inclusion Responses

Specifies the file from which to import responses and/or factors.

27Dialog Box - F1 HelpModel Properties

Model Properties This form allows you to annotate and investigate aspects of the current regression models. Learn about Refinement of a Fit.


Name Name of the model.

Description Description of the model.

Comments Information that further identifies the model.

Note: These annotations are stored in the experiment file, but are not exported to the Web page or the SYLK files.

Regression Select one of the following:

• Summary - Select this option to display a summary of statistics for the entire model. This set of summary reports offers a quick way to review all the models with just a few clicks. If you want to investigate a particular regression in greater detail, select the desired response in the regression list. If you select Summary and Properties, you can alter the name of the current model or provide comments. This can be helpful if you're assessing different models from the same experiment.

• (Response name) - Select this option to display a summary of statistics for a specific response. Choose a statistic category from the Display list below.

Display Select a statistic category to view. The available options vary depending on the selection in the Regression column.

Learn about the Regression Summary.

Learn about the Response Summary.

Note: The items displayed in this form can help you in reviewing the quality of fit. There is no default option to print the tables or charts. If you want to make hardcopies of these values, use a screen capture tool or cut and paste to get the contents of the tables.

Adams/InsightOptimization Preferences

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Optimization PreferencesTools->Optimize Model->Preferences

Defines preferences for the Optimization form.


Multi-objective Method Select the method for combining multiple objectives into a single-objective optimization problem.

• Total Cost - Optimizes the total deviation from targets (for example, the sum of the deviations for all objectives).

• Total Squared Cost - Optimizes the total squared deviations from targets (for example, the sum of the squared deviations for all objectives).

• Worst Cost - Optimizes the worst case deviation from the target (for example, the worst deviation among all objectives).

Optimization Solver Select the method used to solve the optimization problem. The available methods are loaded when you start Adams/Insight and may vary depending on your particular installation of Adams/Insight.

• OptDes GRG (Generalized Reduced Gradient)

• OptDes SQP (Sequential Quadratic Programming)

• SDI (Stochastic Design Improvement)

The OptDes solvers are conventional gradient-based optimizers. Generalized Reduced Gradient (GRG) is generally more robust, while Sequential Quadratic Programming (SQP) is generally faster. SDI is a stochastic solver. At each step (iteration), SDI randomly generates a set of trials around the current design point. It then selects the best trial, generates a new set of trials around that point, and continues.

Solver Settings Select to open the Solver Settings dialog box where you can change the settings for the selected optimization solver (each solver has its own set of settings).

29Dialog Box - F1 HelpOptimization Write

Optimization Write

Tools -> Optimize Model -> Write

Defines the file to which to save your optimization results. Learn more about Optimization.

Adams/InsightOptimize Model or Experiment

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Optimize Model or ExperimentTools -> Optimize Model

Adams/Insight allows you to optimize your fitted models. With some simulation conduits, Adams/Insight also allows you to directly optimize your experiment using simulations. During optimization, Adams/Insight automatically adjusts the factor values so that the resulting responses come as closely as possible to the specified target values.

You may treat each response as an objective or as a constraint. A response is an objective if you attempt to maximize or minimize that value. A response is a constraint if you want to keep it fixed to a desired value or within a range of desired values.

Adams/Insight performs both single-objective and multi-objective optimization. Adams/Insight computes a cost for each objective based on the objective option, target value, and weighting factor. Adams/Insight combines the individual costs into one overall cost based on your choice of the multi-objective method. Adams/Insight then attempts to minimize the overall cost.

Learn more about Optimization.


To change the values, use the sliders next to each item, or enter new values in the corresponding text boxes.

Design Variables

(Factor) Minimum Modify the minimum value for this factor. You can increase the minimum value to reduce the range of possible factor values. You cannot decrease the minimum lower than the initial value.

(Slider Area) Modify the current value for this factor, which will be the initial value for the next optimization, or you can use the Update button to directly compute the responses using this value.

Note: Moving the slider will dynamically update the response values if you are optimizing a fitted model and the regression model has less than 50 responses. If you are optimizing an experiment or the regression model has more than 50 responses, position the respective sliders to the desired factor setting and select the Update button.

(Factor) Maximum Modify the maximum value for this factor. You can reduce the maximum value to reduce the range of possible factor values. You cannot increase the maximum greater then the initial value.

(Factor) Value Modify the current value for this factor, which will be the initial value for the next optimization, or you can use the Update button to directly compute the responses using this value.

Fixed Check this box if you do not want a specific factor changed during the optimization.

31Dialog Box - F1 HelpOptimize Model or Experiment

Design Objectives

(Response) Minimum Displays the minimum value for this response. This is the ideal minimum, ignoring other responses.

Slider area Change the target value for the response. You can also use the Target text box to change this value. The target is the desired value for Min, Max, MinTo, MaxTo, and FrcTo responses, and is the constraint value for GrEq, LsEq, and Equal responses.

Note: The arrow in the slider area identifies the current value.

(Response) Maximum Displays the maximum value for this response. This is the ideal maximum, ignoring other responses.

(Response) Value Displays the last computed value for this response. Adams/Insight computes new response values when you press the Run or Update button.


Adams/InsightOptimize Model or Experiment

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Oper Select the operator:

• Ignore - Ignore the response.

• Min - Minimize the response.

• Max - Maximize the response.

• MinTo - If the response is greater than the target, minimize down to the target. Ignore if less than the target.

• MaxTo - If the response is less than the target, maximize up to the target. Ignore if the response is greater than the target.

• FrcTo - If the response is greater than the target, minimize down to the target. If the response is less than the target, maximize up to the target.

• GrEq - Constrain the response to be greater than or equal to the target value.

• LsEq - Constrain the response to be less than or equal to the target value.

• Equal - Constrain the response to be equal to the target value.

The MinTo, MaxTo, and FrcTo objective options are similar to the LsEq, GrEq, and Equal constraint options, respectively. Use a constraint option if a response must be controlled exactly. Use an objective option if you want to trade off the response cost against other objectives.

Note: The MinTo, MaxTo, and FrcTo objectives create a discontinuity in the objective slope at the target value. If you use these objectives with the Total Cost or Worst Cost multi-objective methods, the optimization may converge slowly or fail. If this happens, use the Total Squared Cost method, which avoids the slope discontinuity. You can select Total Squared Cost using the Multi-Objective Method setting in Optimization Preferences.

Target Change the target value for the response. You can also use the slider to change this value. The target is the desired value for Min, Max, MinTo, MaxTo, and FrcTo responses, and is the constraint value for GrEq, LsEq, and Equal responses.

Weight Change the weighting factor for this response. You use the weights to adjust the importance of responses relative to each other. Weight values should be greater than zero, where larger values indicate greater importance.

Note: Weighting is not applicable to constraints.


33Dialog Box - F1 HelpOptimize Model or Experiment

Cost Displays the last computed cost of this response, if it is an objective response. This is based on the objective option and the difference between the response value and the target value, multiplied by the weight. Adams/Insight attempts to minimize the costs, so a lower cost is better than a higher cost. If the response is ignored or constrained, the cost value will be empty.

Overall Cost Displays the last computed overall cost of all objectives. This is the value that the optimization attempts to minimize. Using the Total Cost method, the overall cost will be the sum of the individual costs. Using the Total Squared Cost method, the overall cost will be the sum of the squares of the individual costs. Using the Worst Cost method, the Overall Cost will be the worst (largest) cost among the individual costs. You can select Total Cost, Total Squared Cost, or Worst Cost using the Multi-Objective Method setting in Optimization Preferences.

Optimize

Preferences Select to display Optimization Preferences, including multi-optimization method and solution method.

Reload Select to reload all of the optimization settings.

Update Select to update the response values to reflect any changes you made to the factor values. This allows you to manually change the factor values and see the effects on responses.

Reset Select to return factors to their original settings.

Write Select to save your results to a text file. You choose among several formats.

• Report of factor and response values to a text file

• Report of factor and response values to a comma-separated value file

• Factor settings to a comma-separated value file

• Factor settings to an Adams/View .cmd file (only available when Adams/Insight is invoked from Adams/View)

Save Select to save the current factor and response settings as defaults. Adams/Insight uses the default settings to fill the Optimization Form when you display it. You may also modify the default response settings in the Response Form in the Optimization Tab. The default settings will be saved with your experiment. If you make changes to the settings and leave the Optimization Form without saving them, they will be lost.

Run Select to optimize the factor values based on the current factor and response settings.


Adams/InsightPreferences

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Preferences

Edit -> Preferences

Defines preferences for your Adams/Insight experiments.


Design

Random Number Seed Enter a value to control the sequences of random numbers in Adams/Insight. Adams/Insight uses random number sequences to create Variation experiments, randomize run order, and randomly select candidate runs for D-optimal designs.

If you use the same nonzero random number seed each time, Adams/Insight will generate the same series of random numbers. For example, this means that you can recreate the Work Space for a Variation experiment (as long as you have the same factors and number of Trials).

If you change the seed to another nonzero value, Adams/Insight will generate a different sequence. For example, this might be helpful if you want to generate several different Variation experiments for the same factors.

If you set the seed to zero, Adams/Insight will use a seed based on the current time for each sequence. This ensures that Adams/Insight will always generate a different sequence, but you will not be able to reproduce the sequence.

The random number seed is also used for randomizing the design order and selecting random trials for Dopt, if you select those options.

Fit

Significance (CI) Enter a value used to compute the coefficient Confidence Interval in the Terms of Regression dialog box. The default confidence interval is 95% (0.05). The pop-up help on the '+/-' column header in the Terms of Regression dialog box shows the % confidence.

35Dialog Box - F1 HelpPreferences

Thresholds

(Fit Summary Types) These settings control which icon (red, green, yellow) is displayed in the respective fit summary pages. The left column values are the thresholds between the values marked by green icons and those marked by yellow icons. The right values are the thresholds between yellow and red icons.

For example, if you enter .95 as the first value and .8 as the second value, the following happens:

• A green icon will be displayed if the value in your experiment falls above .95

• A yellow icon will be displayed if the value in your experiment falls between .8 and .95

• A red icon will be displayed if the value in your experiment falls below .8


Adams/InsightRefinement - Change Order

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Refinement - Change Order

Tools -> Refine Model Manually -> Change Order

Allows you to specify the model order for your experiment. Learn about Refinement.


Model Order Select one of the following:

• Linear

• Interactions

• Quadratic

• Cubic

37Dialog Box - F1 HelpRefinement - Remove Outliers

Refinement - Remove Outliers

Tools -> Refine Model Manually -> Remove Outliers

Allows you to select Outliers to remove from your experiment. Learn about Refinement.


Removed Runs Enter the number of the run(s) you want removed.

Adams/InsightRefinement - Remove Terms

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Refinement - Remove Terms

Tools -> Refine Model Manually -> Remove Terms

Allows you to select terms to remove from your experiment. Learn about Refinement.


Remove Terms Enter the term(s) you want removed.

39Dialog Box - F1 HelpRefinement - Transform Response

Refinement - Transform ResponseTools -> Refine Model Manually -> Transform Response

Before fitting the regression model, you can transform factors. The most common type of transformation is an orthogonal transformation. The transformation is based on the range of values for the factor, such that the transformed value ranges from -1 to 1. In some instances, a transformation may also be applied on the response.

For example, if a factor, F1, has values ranging from 80 to 120, the transformed factor, say, TF1, has a range of -1 to 1.

Learn about Refinement.


Response Transform Select one of the following:

• None

• Square

• Square root

• Inverse

• Inverse of square root

• Log base 10

Adams/InsightResponse

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Response Defines parameters for your responses. Learn more about Responses.


Name Enter a name to identify a response. The response name appears in the treeview.

Abbreviation Modify the abbreviation that Adams/Insight creates for your response name. Adams/Insight creates shorter versions of actual response names to conserve space. Abbreviated names appear in column headings of the Design Space and Work Space tables.

Type Select either:

• Scalar

• Composite

For More information see Response Types.

Description Tab

Description Add a description to a response. The description appears in your results when you export them to an SYLK or HTML report.

URL (Universal Resource Locator)

Add a URL (Web address) to link to a factor. If you publish your experiment results to an HTML page, you can use this option to link the factor to that page. A fully qualified URL (identifying protocol, server and specification) is recommended. For example http://support.adams.com/kb/csearch.asp. An easy way to get this string is to simply cut a valid URL from your browser location window.

Variable When using Adams/Insight in stand-alone mode, this text box is optional. When using Adams/Insight in conjunction with an Adams modeling application, this text box contains the name of the design objective, or refers to the Fetch class that retrieves the results values.

For more information, in a command window, enter the command-line argument adams ain -examples -fetchHelp to see a list of available Fetch classes. Or, review the file <install_dir>/ainsight/examples/exa_r.py to learn how to create a user-defined Fetch class.

Units Add units to describe your responses. Units are annotations only; they do not affect response values.

If you set Response Type to Composite, Adams/Insight displays the following options:

Arguments An optional text box that lets you specify arguments that you want to pass to the post processor that you are using.

41Dialog Box - F1 HelpResponse

Columns An integer value that indicates how many columns you want to allocate in the workspace matrix for this User response. The default is one.

Optimization Tab

Operation Select a default optimization operation for this response. When you display the Optimize Model or Experiment dialog box, Adams/Insight uses this option to initialize the Operation option for this response.

Target Enter a default target value for the response. When you display the Optimize Model or Experiment dialog box, Adams/Insight uses this value to initialize the Target text box for this response.

Approximate Limits Enter default minimum and maximum value limits for the response. When you display the Optimize Model or Experiment dialog box for direct optimization, Adams/Insight uses these values to initialize the Minimum and Maximum text boxes for this response. If you display the Optimization dialog box for optimizing a fitted model, Adams/Insight uses the minimum and maximum values from the model instead.

Weight Enter a default weighting factor for the response. When you display the Optimize Model or Experiment dialog box, Adams/Insight uses this value to initialize the Weight text box for this response.


Adams/InsightResponses Candidates Table (All)

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Responses Candidates Table (All)

(Treeview) -> Responses -> Candidates

Displays a summary of all of the responses not included in your current experiment. Learn more about Responses.

43Dialog Box - F1 HelpResponses Inclusions Table (All)

Responses Inclusions Table (All)

(Treeview) -> Responses -> Inclusions

Displays a summary of all of the responses in your experiment. Learn more about Responses.

Adams/InsightResponses Table (All)

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Responses Table (All)(Treeview) ->Responses

Displays a summary of all of the responses in your experiment. Learn more about Responses.

45Dialog Box - F1 HelpReuse

ReuseFile -> Reuse -> Factors, Responses, Specifications, All

Enables you to reference an existing experiment to include select factors, responses, specifications, or all elements of an existing experiment to the current experiment. More on Reusing Components.


(File name) Specify the experiment file from which to reuse factors, responses, and/or design specifications.

Adams/InsightSimulation Properties

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Simulation Properties

(Treeview) -> Simulation -> (Product name)

Displays information on the simulation used for your experiment.

47Dialog Box - F1 HelpSolver Settings

Solver SettingsTools -> Optimize Model -> Preferences -> Solver Settings

Defines settings for the optimization solver. The values in this dialog box vary based on the Optimization Solver setting in the Optimization Preferences dialog box.


Settings for all optimization solvers

Name Displays the name of the optimization solver.

Description Displays a brief description of the optimization solver.

Verbose Select to turn on additional diagnostic output.

Convergence Tolerance

Specify the relative tolerance for convergence. The solver will stop when the overall cost has converged to this tolerance.

Maximum Iterations

Specify the maximum number of steps (iterations) to perform. The solver will stop after this number of steps, even if the overall cost has not converged.

Settings for SDI only

Step Ratio Specify the amount to vary the factor values for each step (iteration). During each step SDI will generate values around the current factor value within this amount of the factor value range. Be sure that the maximum iterations times the step ratio is about one, or more; otherwise, SDI may not be able to use the full range of factor values.

Number of Runs Specify the number of runs in each step. SDI will generate this number of overall cost evaluations for each step.

Settings for OptDes GRG and OptDes SQP only

Differencing Perturbation

Specify the relative amount to perturb variables during differencing. OptDes uses finite differencing to compute partial derivatives of the overall cost with respect to the factor values. OptDes uses this value to compute the perturbed factor values.

Differencing Method

Select the method for computing derivatives using finite differences:

• Central - Perturb above and below the nominal value and use the average slope as the derivative.

• Forward - Perturb above the nominal value only, and use the slope as the derivative.

Note: Central is slower than Forward, since it requires two additional evaluations of the overall cost instead of one, but it may be more accurate.

Adams/InsightTreeview

48

Treeview Displays a hierarchical list of objects that you can include in an experiment. The tree is especially useful in selecting and identifying objects when you are creating a design matrix. Learn About the Toolbars.

What the Plus and Minus sign mean.

49Dialog Box - F1 HelpWork Space Column Calculator

Work Space Column Calculator

Tools -> Work Space Column Calculator

The Workspace column calculator enables you to perform mathematical operations on the columns of an existing workspace. Learn more about the Work Space Column Calculator.


Column to Compute Select the column, abbreviation, name on which you wish to perform the operation.

Expression Enter the expression you wish to have applied to every row of the selected column. Use valid Python syntax in the expression.

Note: Use the factor abbreviation in the expression, not the full factor name.

The standard math and random Python module functions are available in an expression. See References for more information on Python syntax.

Adams/InsightWork Space Correlation

50

Work Space Correlation

Tools -> Work Space -> Correlation

Allows you to measure the potential strength of a relationship, or lack of relationship, between two variables. Learn more about Work Space Correlations.

Dialog box tips:

• Select a cell to display the Pearson and Spearman Rank correlations and their difference in the status bar at the bottom of the window.

• Double-click a cell (or select Enter on your keyboard) to display a scatter plot of the raw work space matrix values. To return to the Work Space Correlations form, select Backspace on your keyboard or double-click the plot.

• Put your mouse arrow over a column or row header or row header to display the full name and description of the input (factor) or output (variable).

• Double-click a column header to sort the rows in that column.


Correlation Select one of the following:

• Pearson

• Spearman rank

Display Select one of the following:

• All - Displays all of the factors and responses along each axis of the grid. Note that the diagonal of the grid will always contain a value of one.

• Factors vs. Responses Only - Displays the inputs (factors) along the rows and the outputs (responses) along the columns.

Correlation Details Select a cell in the table and then select Correlation Details to display addition information on the factor/response combination that was selected.

Highlight values larger than

Enter a value above which correlations will be displayed. For example, entering .8 will display all correlations with a value of .8 or above.

Green icons are displayed in all cells of the grid where the correlation is above this value.

Note: If you set this value to 1.0, the icons are not displayed.

51Dialog Box - F1 HelpWork Space Correlation

Hide values smaller than

Enter a value below which correlations will be hidden. For example, entering .5 will hide all correlations with a value of .5 or below.

Hide empty rows and columns

Select to display the table without empty rows and columns. Adams/Insight only displays rows and columns with data in them.


Adams/InsightWork Space Correlation

52

Appendix

Adams/InsightANOVA

2

ANOVAANOVA stands for Analysis of Variance which is a statistical method for breaking down the total variability in a dataset into components attributable to various sources.

3AppendixAdams/Insight Toolbars

Adams/Insight ToolbarsThe tools in the toolbars are arranged in the order that you use them in the process of creating and executing your designed experiment. Depending on where you are in the process of creating an experiment, Adams/Insight enables or disables the tools. This feature alerts you to the correct order of procedures to follow. For example, the Run simulations tool is disabled until you define required elements for a design matrix.

Adams/InsightAdjusted R-Squared

4

Adjusted R-SquaredThe R-Squared value always increases when a term is added to the model, irrespective of whether or not the new model is better than the previous one. With every additional term in the model, the residual Degrees of Freedom (DOF) are reduced by one. Therefore, unless the error Sum of Squares (SS) of the new model is reduced by an amount greater than the previous error Mean Squares (MS), the new model will have a larger error mean square and is therefore not a better model. To overcome this deficiency, the adjusted R-squared value is based on the ratio of model mean square to total mean square.

Values: The adjusted R-squared value is typically smaller than the R-squared value for a given regression model. When the R-squared value is very small, the adjusted R-squared can be a negative number.

Troubleshooting: If the R-squared value is fairly high but the adjusted R-squared value is low, it indicates that some of the terms in the model are not very useful. It also indicates that all the variability in the response data has not been explained by the model. You should examine the terms and make a decision about dropping some terms and adding some new ones. The decision about which terms to drop can be made by looking for terms with low T-values.

5AppendixBeta (standardized coefficient)

Beta (standardized coefficient)Standardized coefficients are the result of fitting the model to transformed data, where the response and term values have been centered and scaled to give a mean of zero and a unit variance or unit length (Adams/Insight scales to unit length). The resulting coefficients can be compared to determine which terms are most important (since the term values are normalized).

Note: If the data is both centered and scaled, the constant term is zero in standardized coefficients.

Adams/InsightCandidates

6

CandidatesCandidates are the potential members in a group as compared to the Inclusions. In Adams/Insight candidate factors or candidate responses are the possible factors or response you have to choose from for the experiment. You promote a candidate factor or response to the Inclusions list.

In the graphical user interface (GUI) you can select a candidate factor or response with the mouse, then select the promote button on the toolbar (or press the F5 key). You can select multiple candidates to promote by using the Ctrl key with the left mouse button. To demote a factor or a response, use the Demote button or the F6 key.

7AppendixConfidence Interval

Confidence IntervalThe confidence interval, or confidence limits, measure uncertainty in an estimate. The confidence limits about the estimate contain the true value, with a specified level of confidence. For example, 95% confidence limits around the estimate of a regression coefficient mean that there is a 95% chance that the true value of the regression coefficient is within those limits.

Adams/InsightCook’s Statistics

8

Cook’s StatisticsCook's statistics measure the influence of each run on the fit. Values are usually between zero and 1, but can be larger. A larger value indicates that a run has more effect on the final fit than a run with a smaller value. Values larger than most of the others, for example larger than .5 or especially larger than 1, suggest that the run is unusually influential. The run might be an outlier, or at least should be examined to make sure it is accurate.

Di = ri2*hii / p*(1 - hii)

Di - ith Cook's statistic

ri - ith Studentized residual (See Studentized Residuals)

hii - ith entry on hat matrix diagonal, hat matrix H = X(X'X)-1X'

p - Number of coefficients (terms) in regression polynomial

References• DS - Applied Regression Analysis, Draper and Smith (pg 210)

• MM - Response Surface Methodology - Process and Product Optimization Using Designed Experiments, Myers and Montgomery (pg 49)

9AppendixDegrees of Freedom (DOF)

Degrees of Freedom (DOF)Degrees of freedom (DOF) is the number of independent parameters in a Sum of Squares (SS). If there are n data points, the total number of degrees of freedom in the corrected sum of squares for the dataset is (n-1). If a model with p parameters or coefficients is used to fit the data, then (n-1-p) degrees of freedom remain to estimate the error. These are called the residual degrees of freedom. You should always ensure that there is more than one degree of freedom left to estimate the error.

Adams/InsightDesign Variable

10

Design VariableA design variable is considered an input or factor for a Design of Experiment (DOE). When running a DOE in Adams/View, the only input or factor which you can use is an Adams/View design variable. If using Adams/Insight in conjunction with Adams/View, there are three possible object types which are used as inputs to the DOE. The x, y, and z values of a point, certain attributes of a user-defined element and traditional Adams/View DOE design variables.

11AppendixDesign of Experiment (DOE)

Design of Experiment (DOE)DOE is an active approach to quality improvement. DOE is a methodology by which you design a set of runs or Trials and extract from the results a pattern of behavior for the system. DOEs can:

• Provide up-front optimization

• Reduce sensitivity to manufacturing variation

• Enable you to balance conflicting designs

• Link physical and computer tests

• Correlate computer models

• Transfer up-front information downstream

• Communicate results to non-computer literate individuals

Adams/InsightF-Ratio

12

F-RatioThe F-ratio is used in the regression ANOVA to test the Significance of the regression. The F-ratio is computed as the ratio of the mean square variation due to the regression model (MSM or MSR) to the mean square variation due to error (MSE). The F-ratio is compared to an F-distribution to test the hypothesis that all coefficients are zero. High values for the F-ratio will lead to the rejection of this hypothesis and, therefore, suggest that the regression is significant and the model is useful.

13AppendixFit Table

Fit TableThe fit table displays these quantities for the fit:

• Degrees of Freedom (DOF)

• Sum of Squares (SS)

• Mean Squares (MS)

• F-Ratio

• Regression Significance (P)

Adams/InsightGoodness of Fit

14

Goodness of FitThe goodness-of-fit summary displays some of the primary statistics to view when assessing the goodness of fit. This includes R-Squared, Adjusted R-Squared, Regression Significance (P), and Range-to-variance ratio.

15AppendixHTML Web Page Example

HTML Web Page Example

Adams/InsightInclusions

16

InclusionsInclusions are the actual members in a group to be investigated as compared to the Candidates. In Adams/Insight, inclusion factors or Inclusion responses are the actual factors or responses you will be using in the experiment. You promote a candidate factor or response to the Inclusions list or you can demote an inclusion member to the Candidates list.

In the graphical user interface (GUI) you can select an inclusion factor or response with the mouse, then select the Demote button on the toolbar (or press the F6 key). You can select multiple inclusions to demote by using the Ctrl key with the left mouse button.

17AppendixInteraction

InteractionWhen the effect that a factor has on a response depends on the value of a different factor, the two factors have an interaction. Interaction effects are captured through special terms in the model that consist of products of factors. Interaction effects are important because in the presence of strong interaction, the main effects of the factors may be misleading. Learn more about Main Effects.

Adams/InsightLeast Squares Method

18

Least Squares MethodThe least squares method is a widely used technique for computing regression coefficients, that is, fitting a model to observed data. The goal in regression is to choose coefficients that minimize the fitting error. A common way to measure error is to sum the squares of the Residuals (the differences between observed and predicted values at the original data points). Minimizing this sum leads to the least squares method.

19AppendixLevel

LevelLevels are the number of possible values that can be taken by a factor.

Values: In most screening designs the factors have 2 levels. In RSM designs they typically have 3 levels. It is also possible to have some factors at 2 levels and others at 3 or more levels.

Design objective: Number of levels:

Screening 2

Quadric Surface 3

Cubic Surface 4

Discrete # of levels for each variable entered

Adams/InsightMain Effects

20

Main EffectsMain effect refers to the primary effect of a factor. A good way to examine the main effects is through a Pareto chart.

The Adams/Insight .htm file computes main effects on the fly using JavaScript.

The displayed main effect of a factor is the difference between the response at the factor maximum value and the response at the factor minimum value, while all other factors are at their average values. Effects may be positive (response increases with larger factor value) or negative (response decreases with larger response value).

Note that the minimum and maximum factors' values do not necessarily produce the minimum and maximum response values. If a response is highly nonlinear over the factor value range, the minimum and/or maximum response values may be in the middle of the curve. In this case, the main effects values are meaningless.

The effect % is the ratio of the effect value to the response value with all factors at their average values. An effect % greater than 100% means that the variation in the response value is larger than the average response value.

The effects are sorted largest to least absolute value. The longest bar is always the same length. The other bars are proportional to the largest based on the effect value relative to the largest value. Positive effects have a dark blue bar, negative effects have a light blue bar.

21AppendixMean Squares (MS)

Mean Squares (MS)The mean square value is simply the Sum of Squares (SS) divided by the corresponding Degrees of Freedom (DOF).

Adams/InsightModel

22

ModelIn performing a regression analysis, the objective is to fit an equation (referred to as the model) to the data such that the error between the values predicted by the equation and the actual observed values is minimized. The model can have a constant Term, linear terms, and nonlinear terms.

(Linear model R = a1 + a2F1 + a3F2 + e)

(Interaction model R = a1 + a2F1 + a3F2 + a4F1F2 + e)

(Quadratic model R = a1 + a2F1 + a3F2 + a4F1F2 + a5F1^2 + a6F2^2 + e)

23AppendixMulti-Objective Optimization

Multi-Objective OptimizationIf you have more than one response set to Min or Max, then you are performing a multi-objective optimization.

You use the target and weight values to adjust the relative importance of the responses. Based on these, Adams/Insight computes a cost for each response and combines these into an overall cost. Adams/Insight then minimizes the overall cost. The response cost is the difference between the response value and the target value multiplied by the weight. The target value acts as the desired value, and the weight scales the response relative to other responses. The weight should reflect both different units or scales among the responses, as well as increased or decreased importance.

You can optimize either Total Cost or Worst Cost. You can select Total Cost or Worst Cost using the Settings button. If you select Total Cost, Adams/Insight minimizes the sum of the response costs. If you select Worst Cost, Adams/Insight minimizes the worst (largest) cost among the responses.

Adams/InsightNone Option

24

None OptionUnder Model, the none option is automatically selected when it is inappropriate to attempt to fit results based on the investigation strategy selected. For example, a Perimeter study will automatically force a None option. Certain variations of the sweep study will also force a none selection.

25AppendixOutlier

OutlierAn outlier is a data point that does not seem to fit with the others, and perhaps should be fixed or removed from the fit. A simple case is data that nicely follows a straight line, except for one point in the middle that lies far off the line. Often, this is the result of an anomaly or unexpected situation in the case that generated the data. In Adams, this might be a combination of variable values that leads to completely different model behavior, such as a part missing a stop or a linkage locking up. You can find outliers by examining Residuals and Cook’s Statistics for each run.

Troubleshooting: When running analytical Design of Experiment (DOE)s, make sure that all the Trials ran successfully. Often disk space limitations or a license server dropping off line for a few seconds can cause an entire block of runs to be missing from the results.

Adams/InsightPareto

26

ParetoA Pareto diagram is a ranking of most significant to least significant, and then displaying the results with a bar graph. Adams/Insight optionally displays the Main Effects of the fit in the published Web page.

27AppendixPlus and Minus sign

Plus and Minus signIf a plus sign (+) appears in front of an entry, it means that there are additional objects within that entry.

To see the contents of an object in the treeview:

• Click the plus sign (+) in front of the object.

To collapse the contents of an object in the treeview:

• Click the minus sign (-) in front of the object.

Adams/InsightProperties

28

PropertiesThe Regression Summary Properties window allows you to alter the name of the current model or add comments. This can be helpful if you're assessing different models from the same experiment.

29AppendixR-Squared

R-SquaredAn R-squared value is the proportion of total variability in the data which is explained by the regression model. It is computed as the regression or model Sum of Squares (SS) divided by the total sum of squares.

Values: Range is 0 to 1.

Troubleshooting: An R-squared of 1 indicates a perfect model. This is unlikely and may be due to the number of terms being the same as the number of data points. Check the number of Error Degrees of Freedom in the fit for regression response summary area. Generally, the more Error Degrees of Freedom a model has, the better you can quantify the fit. You should add a few extra runs and then fit the model. An R-squared of 0 indicates that the data is purely random or that the model is totally inappropriate. You should check the range of response values to make sure that they make physical sense. Ideally, you should obtain R-squared values greater than 0.9 for high confidence in the results.

Adams/InsightRMS Error

30

RMS ErrorRMS error or Root Mean Square error is an estimate of the unexplained variability remaining after a model has been used to fit the data. If the model is good, RMS error should be small compared with the mean value of the response.

Values: Theoretically, the smallest value of RMS Error is zero. However, this implies a perfect fit which is unlikely and should, therefore, be suspect. In general, values which are two orders of magnitude smaller than the mean value of the response are good.

Troubleshooting: If the R-Squared values are very good and the RMS Error is large, it indicates that the model is reasonably good but there is a lot of variability in the data. For physical experiments, it may be useful to check pure repeatability. If both R-squared values and RMS Error values are poor, it is advisable to check the validity of the model and the data.

31AppendixRange-to-variance ratio

Range-to-variance ratioThe range-to-variance ratio measures how well a fitted regression model might predict new values. It is defined as the range of estimated values at the original data points, divided by the average variance of the estimated values. It measures the variation of predicted values due to the model, versus the variation due to uncertainty in the model. A high value, greater than 10 for example, indicates that the prediction is likely worthwhile. A low value, less than 4 for example, suggests that the uncertainty in the model is high enough that predicted changes may not be significant.

Adams/InsightRegression Significance

32

Regression SignificanceRegression significance is defined as the probability that the regression coefficients are all zero. In other words, the regression model has no useful terms.

A low value of Significance, .02 for example, means that it is likely that at least one term in the model is related to the response. A high value of significance, .3 for example, means that there is a high probability that none of the terms in the model is related to the response. A low regression significance only means that at least one term is likely significant, not necessarily all terms. Look at the Term Significance values to check individual terms.

The regression significance is computed from the regression F-Ratio.

33AppendixResiduals

ResidualsResidual is the difference between the predicted (estimate) and observed (actual) values of the response.

Troubleshooting: Residual plots are useful to examine when troubleshooting your model. Examples of such plots are a plot of the residuals versus the run number, or residuals versus a response or factor value. Any trends that are observed indicate an effect that has not been properly captured in the experiment. Residual plots can be based on either raw values or values that are Studentized. If a few runs have very high residuals, you should examine the runs to check the validity of the data.

Adams/InsightResiduals Table

34

Residuals TableThe residuals table displays these quantities for each trial:

• Actual response

• Estimated response

• Raw residual. See Residuals.

• Studentized residual. See Studentized Residuals.

• Cook's statistic. See Cook’s Statistics.

35AppendixRules Summary

Rules SummaryAdams/Insight uses a number of rules-of-thumb to help you evaluate regression results. The Rule-of-thumb summary table summarizes the results of these rules. For each response in the experiment, the summary displays the worst case among the Goodness of Fit rules, the Term Significance, Studentized Residuals, and Cook’s Statistics.

Default Thresholds:

The summary categories are:

• fit - worst case of R2, R2adj, regression P, and range/variance

• term - worst case of term P for all terms Trials

• residuals - worst case of studentized and Cook's for all

The colored Icons (green, yellow with question mark, red with cross), help identify between which threshold the particular measure falls.

More information on threshold Preferences.

Quantity

R2 0.95 0.8

R2adj 0.90 0.7

reg. P 0.01 0.05

Range/Variance 10 4

term P 0.01 0.05

abs(cooks) 0.5 1.0

abs(studentized) 3.0 4.0

Adams/InsightSignificance

36

SignificanceSignificance is the probability that a value at least as extreme as the value of the statistic being tested could occur by random chance. Adams/Insight reports two types of significance values: one for the regression as a whole (Regression Significance), and a value for each term (Term Significance).

Values: Since it is a probability, significance values range from 0 to 1. Low values (0.1 and smaller) signify important terms and useful regressions.

37AppendixSingle-Objective Optimization

Single-Objective OptimizationIf you have only one response, or if you have only one response set to achieve an objective, then you are performing a single-objective optimization. In this case, Adams/Insight will adjust the factors to try and meet the objective of the single response. The weight will not affect the resulting factor values.

Adams/InsightSorting

38

SortingFor proper sorting to occur, you must set the Run Order option to Ease of Adjustment in the Design Specification dialog box. Ease of Adjustment is generally not applicable when running analytical Design of Experiment (DOE)s. This option is primarily used when you’re using Adams/Insight in stand-alone mode. It lets you specify a relative expense to modify one factor compared to another when running a physical DOE.

39AppendixStandard Deviation

Standard DeviationThe standard deviation of a random variable is defined as the positive square root of the Variance. Standard deviation is a measure of the variability of the variable about the mean. If a variable is normally-distributed (in other words, its distribution follows the standard bell-shaped curve), 68.3% of the time its value will fall within one standard deviation of the mean, 95.4% within two standard deviations, and 99.7% within 3 standard deviations.

Adams/InsightStandard Error

40

Standard ErrorFor any estimate, the standard error represents the variability in that estimate. In a regression model, the standard error of a coefficient is an estimate of the Standard Deviation that would be obtained by repeatedly estimating the coefficient with new data.

41AppendixStudentized Residuals

Studentized ResidualsStudentized residuals are residual values that are scaled to make them independent of the magnitude of the actual Residuals. This makes it easier to identify large errors in the estimates. Studentized residuals always have a Variance and Standard Deviation of 1. If the fitted model is correct, and basic assumptions about errors are true, then the residuals should be normally-distributed. Therefore, for a good model almost all studentized residuals should be between -3 and 3, with most between -2 and 2, and about 2/3 between -1 and 1.

If most Studentized residuals fall within these guidelines, but one or two runs stand out as poor, it may be that those runs are Outliers which should be corrected or removed. If many Studentized residuals fall outside these guidelines, then the model may not be accurate and may need more terms or a smaller range of factor values.

Troubleshooting: If the residuals are ~1e-10, then the regression is more-or-less an exact fit and many of the measures become undefined and/or lose their meaning.

References:

• DS - Applied Regression Analysis, Draper and Smith (pg 207)

• MM - Response Surface Methodology Process and Product Optimization Using Designed Experiments, Myers and Montgomery (pg 45)

• ri = ei / (s2 (1-hii))1/2

• ri = ith studentized residual

• ei = ith residual

• s2 = estimate of error variance; that is, the residual mean square (MSE) from the ANOVA table

• hii = ith entry on hat matrix diagonal, hat matrix H = X(X'X)-1X'

The denominator is the standard error of the ith residual, so the studentized residual is the raw residual normalized by dividing by its Standard Error. This is also called the internally studentized residual. There is a variation called the externally studentized residual.

Note: If the fit is exact, there is no residual and therefore no standard error for the residual, so the studentized residual is undefined. The Cook's statistic is similar (See Cook’s Statistics). In Adams/Insight if the absolute value of the raw residual is < 1e-12, it is considered an exact fit and the Cook's and studentized are set to zero.

Adams/InsightSum of Squares (SS)

42

Sum of Squares (SS)The total or corrected sum of squares for a set of data is calculated by taking the difference between each data point from the mean, squaring this difference, and then summing the squared values. In a regression analysis, this total sum of squares is partitioned into portions attributable to the model and to random error.

43AppendixT-value

T-valueThe T-value is a statistic which is used to test a hypothesis by comparing it to a T-distribution. In regression, it is used to determine whether or not a term in the model is significant (See Significance). An assumption is made that the underlying distribution is normal. Then a T-value is calculated under the hypothesis that the true value of the coefficient for that term is zero. If the T-value is large, this hypothesis is rejected because it indicates that the true value is not zero and that the term is indeed significant.

Values: T-values can be either positive or negative.

Adams/InsightTerm

44

TermThe equation that is used to fit the data consists of various terms. The terms can be of type linear, interactions, quadratic, or cubic.

For example, let F1 and F2 be two factors and R1 the response. The regression equation for R1 can take the form:

R1 = C0 + C1*F1 + C2*F2 + C3*F1*F2 + C4*F1*F1

Here, the Cs are coefficients:

• C1*F1 and C2*F2 are the linear terms

• C3*F1*F2 is an interactions term

• C4*F1*F1 is a quadratic term

45AppendixTerm Significance

Term SignificanceTerm significance is defined as the probability that the term coefficient is zero. In other words, that the term does not affect the response. The term significance is computed from the term T-value.

For instance, let the T-value in a T-test for a term in a model be 11.0. Significance then gives us the probability of the T-value being as high as 11.0 under the assumption that the true value of the coefficient for that term is zero. To identify terms that have a significant effect on the regression, look for low significance values.

Adams/InsightTerms Table

46

Terms TableThe terms table displays these quantities for each term in the fitted polynomial:

• Coefficient

• Confidence Interval

• Standard Error

• Beta (standardized coefficient)

• T statistic

• Term Significance

• Term definition (Term)

47AppendixTrial

TrialA trial is a single run from the total number of runs that together make up the experiment. Each row of the Work Space matrix represents a trial or run. With each run, the inputs (factors) are modified and the output (response) is monitored and recorded.

Adams/InsightVariance

48

VarianceThe variance of a random variable is defined as the expected (average) value of the squared difference from the mean. Variance is a measure of the variability of the variable about the mean. The positive square root of the variance is the Standard Deviation.

The range-to-variance ratio measures how well a fitted regression model might predict new values. It's defined as the range of estimated values at the original data points, divided by the average variance of the estimated values. It measures the variation of predicted values due to the model versus the variation due to uncertainty in the model. A high value, greater than 10 for example, indicates that the prediction is likely worthwhile. A low value, less than four for example, suggests that the uncertainty in the model is high enough that predicted changes may not be significant.

49AppendixWork Space Column Calculator Example

Work Space Column Calculator Example

Example 1: Synthesize a relationship between three factors and a response

1. Create three factors and one response.



4. Enter the following expression: '2 + 3*f_01 + 4*f_02**2 + 5*f_03**3'

5. Select OK.

Example 2: Use of Python functions in the expression

1. Create two factors and one response.



4. Enter the following expression: '2 + 3*f_01 + 4*f_02**2 + 5*f_03*f_02 + random()'

5. Select OK.

Adams/InsightWork Space Column Calculator Example

50

using adams/insight - md adams 2010

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