Robust Parameter Design Method for Multilevel Control Factors with Continuous Noise Factors

Abstract

Robust parameter design is a central technique of the Taguchi method, wherein parameters are designed such that output fluctuations are reduced even when the environmental and operating conditions change marginally. Generally, it is necessary to level the noise factor however this is often difficult to assign. It is sometimes possible to passively measure the value of the noise factor that is, when its value of the noise factor can be observed as a covariate.

Koyano (2013) proposed a robust parameter design method that uses propensity scores when multiple noise factors are observed as covariates. Goto et al. (2015) proposed a robust parameter design method when multiple noise factors are observed as covariates, thus extending the method of Hirano and Miyakawa (2007).

The aforementioned studies only examined cases wherein each control factor had two levels. In this study, we proposed two extensions of the above methods for multilevel control factors: extended Method 1, based on the Koyano (2013) method, and Method 2, based on the Goto et al. (2015) method. Simulations were performed based on the aforementioned studies to evaluate the performance of each method. Method 1 was not valid under any of the simulation conditions. Conversely, Method 2 showed validity regardless of the conditions.