In factorial design, only the linear effects of the quantitative factors are studied. Response surface methodology allows you to study the quadratic effects of the factors (i.e., effects that differ depending on the level of the factors), making it well-suited to predictive modeling and optimization. The following types of response surface method designs are available:
Central Composite: A two level full or fractional factorial design is embedded in the central composite design, and additional center and axial points are also used in order to estimate curvature. This is the most commonly used response surface methodology design.
Box-Behnken: Each factor must have three levels, with one level being the center point between the high and low levels. This design type is useful in cases where setting all factors at extreme values simultaneously is undesirable (e.g., if setting all factors at the high level carries a risk of equipment damage or otherwise violates constraints).
For more information about how to use the design types, please consult the documentation on design folios.