Getting more from your model with DesignXplorer

CAEA eLearning Series

Pat Cunningham

2/2017

CAE Associates Inc.

Engineering Consulting Firm in Middlebury, CT specializing in FEA, CFD,

and Electromagnetic analysis.

ANSYS® Channel Partner since 1985 providing sales of the ANSYS®

products, training and technical support.

e-Learning Webinar Series

This presentation is part of a series of e-Learning webinars offered by

CAE Associates.

You can view many of our previous e-Learning session either on our

website or on the CAE Associates YouTube channel:

If you are a New Jersey or New York resident you can earn continuing

education credit for attending the full webinar and completing a survey

which will be emailed to you after the presentation.

CAEA Resource Library

Our Resource Library contains over 250 items including:

— Consulting Case Studies

— Conference and Seminar Presentations

— Software demonstrations

— Useful macros and scripts

The content is searchable and you can download copies of the material to

review at your convenience.

CAEA Engineering Advantage Blog

Our Engineering Advantage Blog offers weekly insights from our

experienced technical staff.

CAEA ANSYS® Training

Classes can be held at our Training Center at CAE Associates or on-site

at your location.

CAE Associates is offering on-line training classes in 2015!

Registration is available on our website.

Why talk about DesignXplorer?

DesignXplorer is a component of ANSYS Workbench that can help you

make your designs more efficient and robust. With DesignXplorer you

can:

— Determine the sensitivity of the systems response to variations in the input

quantities.

— Identify which input variables play a dominant role in the response.

— Develop a surrogate function that enables you to quickly predict the system

output for any parameter combination within the design space.

— Use the surrogate function to determine the optimum input settings for a

defined set of goals and constraints.

— Determine the robustness of the design using probabilistic representations of

the systems input/output relationship.

And by the way, if you are running ANSYS release 18 then you already

have it!

Who Has DesignXplorer?

ANSYS Inc. has bundled the DesignXplorer tool with the following

Mechanical products:

— ANSYS Mechanical Enterprise

— Mechanical Premium

— Mechanical Pro

— Mechanical Enterprise Prep/Post

— DesignSpace and ANSYS AutoDyn-3D

DesignXplorer is also bundled with several of the CFD and Electronics

licenses:

— ANSYS CFD Enterprise, Premium and ANSYS CFD Prep/Post

— ANSYS Mechanical CFD

— ANSYS Academic Associate, Research and Teaching products

— Basic DX core capabilities are now included with ANSYS Electronics Desktop

Parametric Modeling Review

In Parts I and II of this eLearning series we showed you how you can

define input and output parameters using your CAD program, either of the

ANSYS geometry tools, and ANSYS Mechanical.

In case you missed Parts I and II or would like to view them again the

recordings can be found in the resource library at caeai.com or at the CAE

Associates YouTube channel:

Part III–Optimization Using DesignXplorer

The focus of part III of this series is on the optimization tools available with

ANSYS DesignXplorer.

This presentation is also being recorded and will also be available for later

viewing on CAE Associates’ website and YouTube channel.

Additionally CAE Associates is offering a 1-day training class on

DesignXplorer in our Middlebury, CT office on April 7, 2017.

You can register for this class on our website or call our office at (203)758-

2914.

Parametric Modeling Review

The inputs to a parametric ANSYS model can include:

— Geometric dimensions, either from a parametric CAD model or from either of

the ANSYS geometry tools (DesignModeler and SpaceClaim).

— Element properties such as shell thickness and beam cross sections.

— Material coefficients

— Mesh sizing controls

— Contact offsets

— Load magnitudes

Output quantities consist of volume, mass and finite element model result

quantities (reaction forces, deformation, strain, stress, natural frequency,

etc.).

DesignXplorer Tools

DesignXplorer links directly to your parametric model.

DesignXplorer includes several tools that help you evaluate the sensitivity

and robustness of your design:

Optimization Using DesignXplorer

Direct Optimization:

— Uses an iterative approach and the optimum is

determined from actual solutions of the finite element

model.

— The solution process is serial.

— Each optimization run is specific to a single

optimization goal set.

Goal Constraint

DesignXplorer Tools

Parameter Correlation:

— Helps you evaluate the sensitivity levels of each

output parameter to variations of the input.

— Helps you make sure that your optimization study

focusses on the inputs that the system is reactive

to.

— Uses Latin Hypercube sampling to characterize the

design space.

Global Parameter Sensitivities Correlation Scatter

DesignXplorer Tools

Design of Experiments helps you set up a table of design points to

characterize your design space. Custom DOE models can also be

imported into DesignXplorer.

Design points submitted through the ANSYS Remote Solve Manager can

utilize the available computing resources to solve simultaneously.

With an HPC Parametric Pack you can clone all of the licenses needed for

a single design point solution (CAD plugin, Geometry, Mechanical, HPC).

DesignXplorer Tools

Response Surface creates surrogate models

based on the deterministic design point solutions

from the Design of Experiments. The surrogate

models are used to calculate rapid predictive

responses of the system.

— Several Response Surface models are available and

can include automated or manual refinement points

(actual model solves) to increase the accuracy of the

surrogate model.

— The Genetic Aggregate Response Surface

(GARS) minimizes user input sensitivity by

automatically testing and building a best fit model

from the available formulations.

Optimization Using DesignXplorer

Response Surface Optimization:

— The Response Surface surrogate model is

used to determine the optimum designs.

— The Response Surface can be used for

multiple optimization goals without the need to

regenerate the design point solutions (unlike

Direct Optimization).

Response Surface Response Surface Sampling Candidate Designs

DesignXplorer Tools

Six Sigma uses probabilistic distributions of the input to help you

determine if your design is robust.

Input OutputResponse Data

Sample Model

For this webinar we will continue with the Lego Man hip piece model that

we discussed in Parts 1 & 2 of this series.

The goal of our optimization study will be to reduce the amount of material

required while maintaining acceptable part strength.

Sample Model

The geometry inputs represent the dimensions of the part that can be

modified within the design limits.

The dimensions come from the CAD geometry and are as follows:

P11

P12 diameter

P16

P14 Radius

P13 Radius

Sample Model

The structural analysis will assume a symmetric bending load that hip joint

is expected to see when Lego Man’s hip and torso are pressed together.

The output parameters are the part volume, deflection, stress and

predicted life of the part (using the Fatigue Module that is also provided

with the Mechanical Enterprise license).

Demonstration model

The defeatured geometry is attached in Mechanical.

— The total part volume is selected as an output parameter.

Demonstration model

The boundary conditions are:

— ½ symmetry is assumed and applied

using frictionless supports.

— A compression only support on the

peg supports the vertical load.

— A cylindrical support at the end of the

peg is used to constrain the

tangential direction only.

— A remote force with an offset is

applied to the top of the peg.

— Large Deformation is turned on.

Demonstration model

Other output parameters include the total deformation and stress in the

base and peg fillets.

A mesh refinement study of the base fillet mesh was conducted to ensure

that the stress distribution was adequately characterized.

Demonstration model

The CAD geometry was attached in DesignModeler where defeaturing

operations were performed.

The CAD parameters filter out with the prefix were selected in

DesignModeler.

The CAD parameters are then visible in the Parameter Set.

Demonstration model

The Parameter Correlation tool is used to evaluate the strength of the

parameter relationships.

The global sensitivities chart shows a strong correlation of the base

thickness the peg diameter to the local stress in their respective regions.

Note that center thickness is only displaying sensitivity to the volume but

has a negligible effect on deflection and stress.

Demonstration model

Scatter plots also display a strong correlation of the base thickness to the

deflection and stress as indicated by the banded nature of the response

points.

Banded scatter indicating a stronger correlation Wide scatter = weak correlation

Demonstration model

The choice of a Design of Experiments model is an important part of the

Response Surface Optimization.

Certain models allow for additional manual or automatic refinement of the

resulting response surface.

In this example we will be using the Central Composite Design model.

Additional post-DOE

refinement available.

Demonstration model

Design of Experiments Parameter Selection:

— The number of actual solves needed to characterize the design space is

dependent on two things:

• The number of input variables

• The selected DOE model

— The correlation study told us that the back thickness has little affect on the

deflection and stress in the part and thus it is removed from the DOE inputs.

Demonstration model

Design of Experiments Input Parameter setup:

— Parameter input can be defined as either discrete or continuous.

— In this case we will define the geometry input parameters as continues with

upper and lower bounds.

— Note that it is important to test the ranges used prior to the DOE run to be sure

that all of the design points are feasible. Failed design points will negatively

affect the quality of the response surface model.

Demo

The Central Composite Design model with full factorials and four design

variable generates a table of 26 design points.

Demonstration model

The Genetic Aggregation response surface is used to create a surrogate

model from the DOE design point solutions.

The Coefficient of Determination (R2) and the Goodness of Fit are used to

access the quality of the response surface.

Note: with the DOE solution set multiple Response Surface types can be

generated and compared.

Demonstration model

The Optimization method using the Response Surface model to locate

optimum candidates in the design space based on defined objectives and

constraints. Optimum candidates can be evaluated deterministically.

Note: The Response Model can be used multiple times for different

optimization goals. This differs from direct optimization when the design

points solutions are generated with respect to a single goal set.

Optimization Result

When we compare the original design to the optimized design we see the

following:

— A volume reduction of 2.7%.

— The change in the maximum deflection is nominal indicating that the global

part stiffness is unchanged.

— The peak stress is reduced by 33%.

— A fatigue life calculation predicts an increase of the minimum number of cycles

from 156 to over 500,000.

Optimization Result

A 2.7% reduction in volume relates directly to a savings in material cost.

Per hip piece this is not a large number but when you consider that Lego

reported sales of 725 million mini-figures in 2015 the savings in material

cost starts to look pretty attractive.

Imagine if they were to optimize the rest of Lego Man’s parts. That’s bound

to put a smile on someone’s face!

Thanks for joining us!

Thank you for participating in part III of our Parametric Modeling using

ANSYS series.

If you would like to learn more please join us for our DesignXplorer training

class in Middlebury, CT. on April 7, 2017.