Box-Behnken designs are used to generate higher order response surfaces using fewer required runs than a normal factorial. Box-Behnken designs place points on the midpoints of the edges of the cubical design region, as well as points at the center.
Box-Behnken designs and CCF central composite designs can be visualized as near compliments of each other. They both essentially suppress selected runs from a full factorial matrix in an attempt to maintain the higher order surface definition. For example, for three three-level variables, the full factorial run size is 27. The central composite design drops all of the middle edge nodes, resulting in only 15 runs. The Box-Behnken design is nearly the opposite in that it uses the twelve middle edge nodes and the center node to fit a 2nd order equation. A central composite design plus a Box-Behnken design becomes a full factorial with extra samples taken at the center.
• | Box-Behnken is generally used for fitting a second-order response surface. |
• | A Box-Behnken DOE is only defined when all of the variables have three levels. |
• | A Box-Behnken DOE should not be used when accurate predictions at the extremes are important. |
• | Any data in the inclusion matrix is combined with the run data for post-processing. Any run matrix point which is already part of the inclusion data will not be rerun. |
In the Specifications step, you can change the following settings of Box-Behnken from the Settings tab.
Parameter |
Default |
Range |
Description |
Design |
AutoSelect |
AutoSelect, bbdgn13, bbdgn25, bbdgn41, bbdgn49, bbdgn57, None |
AutoSelect will pick the lowest number of runs sufficient to study effects. bbdgn stands for Box Behnken design. |
Number of runs(npt) |
Dependant upon design selected. |
13-57 |
Number of designs to be evaluated. |
Use Inclusion Matrix |
false |
true or false |
Concatenation without duplication between the inclusion and the generated run matrix. |