Faculty of Mechanical Engineering

Advance Manufacturing MME6134

ASSIGNMENT 1

SEM 1 (2015-2016)

Dr. Saiful Anuar Che Ghani

ASSIGNMENT Number: Assignment # 1

Submission Date: 7 Disember 2015

Solutions Presentation by: 1 week time.

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1.MOHAMMAD HAZIM BIN MOHAMAD HAMDAN 01 KMM15005

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Date submitted:……………………………………..hard copy to FKM.

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Introduction

The analysis of fractional factorial experiments, is detecting a nonlinear relationship between the dependent variable and quantitative factors analysed. In these situations, experiment Response Surface is useful because they have the ability to ' shape ' the function relates these variables.

In this article we discuss some concepts of Response Surface Experiments methodology Central compounds. Subsequently, an example is presented using the tool Response Surface, MINITAB statistical software. In addition, the steps to follow in MINITAB to perform this analysis are also described in detail

The response surface methodology

In the context of design of experiments, the main goal of researchers is to characterize the relationship between one or more response variables and a set of factors of interest. This can be performed by building a model that describes the response variable as a function of values for these factors.

Certain types of scientific problems involves the expression of a variable response, such as the yield of a product, as an empirical function of one or more quantitative factors such as reaction temperature and pressure. This can be done using one-response surface modelling the relationship

Yield = f (feed rate, tool tip radius, depth of cut).

Knowledge of the functional form of f() Often obtained with modeling data from designed experiments, allows both summarize the results of the experiment as to predict the response values of quantitative factors. Thus, the function f() defines a response surface.

Normal Probabilty Plot of the Residuals:

It is necessary we use the Normal probability plot of waste to verify that they do not deviate substantially from a normal distribution.

•If residues follow a normal distribution, describe points, rough-mind, the blue line.

Residuals Versus the Fitted Values

For these data, the normal probability plot of the residuals shown that we can assume that these residues follow a normal distribution. The normality can be assessed using the histogram, but the normal probability graph is generally more informative, especially for small samples.

We use the waste graphic versus adjusted values to see if the following assumptions are met:

•Variance constant over all levels of the factors;

•There is no point in disparate data.

The graphical output, we see that the points seem to be randomly distributed in random zero in the worst graphic versus the adjusted values. Therefore, we consider that the two assumptions above were met

Residuals Versus the Order of the Data

Used for waste chart versus the order of the data to verify that the waste is independent. If there is an effect due to the data collection order, the waste will not be scattered randomly around zero.

In this case, you can spot a nográfico standard.

In this example, as the true order of data collection is unknown, the chart is not significant.

Through these charts, we can evaluate which combinations of factors provide the maximum values of surface roughness. In addition, we can see the shape of the response surface and have a general idea of performance in various settings feed rate, depth of cut, and tool tip radius.