Validating Robust-Parameter-Design Methods

This paper discusses validation of design methods. The challenges and opportunities in validation are illustrated by means of an analogy with medical research and development. Inspired by this analogy, a model-based process is presented for validating robust-parameter-design methods. A hierarchical probability model was used to create a large number of polynomial response surfaces with hierarchy and inheritance properties similar to those of an ensemble of 62 engineering responses from published experiments. Various robust-parameter-design methods were applied to each response surface and the robustness of the resulting designs was assessed. It was shown that crossed array methods are more effective than combined array methods if there is a reasonable probability of three-factor interactions. It is also shown that one-factor-at-time methods provide even greater effectiveness than crossed arrays methods.