An Adaptive One-Factor-at-a-Time Method for Robust Parameter Design: Comparison with Crossed Arrays via Case Studies

This paper presents a conceptually simple and resource efficient method for robust parameter design. The proposed method varies control factors according to an adaptive one-factor-at-a-time plan while varying noise factors using a two-level resolution III fractional factorial array. This method is compared with crossed arrays by analyzing a set of four case studies to which both approaches were applied. The proposed method improves system robustness effectively, attaining more than 80% of the potential improvement on average if experimental error is low. This figure improves to about 90% if prior knowledge of the system is used to define a promising starting point for the search. The results vary across the case studies, but, in general, both the average amount of improvement and the consistency of the results are better than those provided by crossed arrays if experimental error is low or if the system contains some large interactions involving two or more control factors. This is true despite the fact that the proposed method generally uses fewer experiments than crossed arrays. The case studies reveal that the proposed method provides these benefits by exploiting, with high probability, both control by noise interactions and also higher order effects involving two control factors and a noise factor. The overall conclusion is that adaptive one-factor-at-a-time, used in concert with factorial outer arrays, is demonstrated to be an effective approach to robust parameter design providing significant practical advantages as compared to commonly used alternatives.