Dual-response optimization using a patient rule induction method

Abstract

A dual-response surface optimization approach assumes that response surface models of the mean and standard deviation of a response are fitted well to experimental data. However, it is often difficult to satisfy this assumption when dealing with a large volume of operational data from a manufacturing line. The proposed method attempts to optimize the mean and standard deviation of the response without building response surface models. Instead, it searches for an optimal setting of input variables directly from operational data by using a patient rule induction method. The proposed approach is illustrated with a step-by-step procedure for an example case.