Modeling and Multivariate Methods - SAS

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614 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 The Simulator Figure 24.69 Lower Defect Rate At the new settings the defect rate is 0.066%, much better than the 5.5% for the settings that maximize Yield. That is a reduction of about 100x. Recall the average Yield from the first settings is 0.60 and the new average is 0.59. The decrease in average Yield of 0.01 is very acceptable when the defect rate decreases by 100x. Because we saved the settings using Remember Settings, we can easily compare the old and new settings. The Differences report summarizes the difference. Click the Remembered Settings radio buttons to view the profiler for each setting.
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 615 Noise Factors (Robust Engineering) Figure 24.70 Settings Comparison radio buttons maximum yield settings minimum defect settings The chemist now knows what settings to use for a quality process. If the factors have no variation, the settings for maximum Yield are hot and fast. But, if the process inputs have variation similar to what we’ve simulated, the settings for maximum Yield produce a high defect rate. Therefore, to minimize the defect rate in the presence of factor variation, the settings should be cool and slow. Noise Factors (Robust Engineering) Robust process engineering enables you to produce acceptable products reliably, despite variation in the process variables. Even when your experiment has controllable factors, there is a certain amount of uncontrollable variation in the factors that affects the response. This is called transmitted variation. Factors with this variation are called noise factors. Some factors you can’t control at all, like environmental noise factors. Some factors can have their mean controlled, but not their standard deviation. This is often the case for intermediate factors that are output from a different process or manufacturing step. A good approach to making the process robust is to match the target at the flattest place of the noise response surface so that the noise has little influence on the process. Mathematically, this is the value where the first derivatives of each response with respect to each noise factor are zero. JMP, of course, computes the derivatives for you.
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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18 Interpreting the Profiles . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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232 Analyzing Screening Designs Cha
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents Principal Components. . .
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Modeling and Multivariate Methods - SAS

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