Tip: You can use DOE > Custom Design to construct optimal response surface designs that accommodate your specific experimental situation. Custom Design constructs response surface designs that are much more flexible than classical response surface designs. In particular, you can use the Custom Design platform to create response surface designs that involve categorical factors or more than eight continuous factors. You can also specify the number of runs and restrictions on the design space. For examples, see Response Surface Experiments in Examples of Custom Designs.
A central composite design (Figure 11.2) combines a two-level fractional factorial design and two other types of points:
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Center points, where all the factor values are set to the midrange value.
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Axial points, where one factor is set to a high or low value (an axial value) and all other factors are set to the midrange value.
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Figure 11.2 Central Composite Design for Three Factors
A Box-Behnken design (Figure 11.3) has only three levels per factor and has no design points at the vertices of the cube defined by the ranges of the factors. This type of design can be useful when you must avoid these points due to engineering considerations. But, the lack of design points at the vertices of the cube means that a Box-Behnken design has higher prediction variance, and so less precision, near the vertices compared to a central composite design.
Figure 11.3 Box-Behnken Design for Three Factors
In both cases, the design table contains a Model script that you can run to fit a model. The Model script applies the Response Surface Effect attribute to each main effect, so that the main effects appear with a &RS suffix in the Fit Model window. This attribute ensures that the Fit Least Squares report contains a Response Surface report. For details about this report, see Fit Least Squares Report in the Fitting Linear Models book.