After you have identified the "vital few" by screening, you
need to determine the "best" or optimal values for these experimental
factors. Optimal factor values depend on the process objective. For example,
you may want to maximize process yield or reduce product variability.
· Factorial Designs
Overview describes methods for designing and analyzing factorial designs.
· Response
Surface Designs Overview describes methods for designing and analyzing
central composite and Box-Behnken designs.
· Mixture Designs
Overview describes methods for designing and analyzing simplex centroid,
simplex lattice, and extreme vertices designs. Mixture designs are a special
class of response surface designs where the proportions of the components
(factors), rather than their magnitude, are important.
· Response Optimization
describes methods for optimizing multiple responses. Minitab provides
numerical optimization, an interactive graph, and an overlaid
contour plot
to help you determine the "best" settings to simultaneously
optimize multiple responses.
· Taguchi Designs
Overview describes methods for analyzing Taguchi designs. Taguchi
designs may also be called orthogonal array designs, robust designs, or
inner-outer array designs. These designs are used for creating products
that are robust to conditions in their expected operating environment.
Optimization