A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors

TLDR

Taguchi Robust Design methods address two main problems: minimizing performance variation caused by noise factors (Type I) and minimizing variation caused by control factors (Type II). The paper proposes a novel variation of Taguchi Robust Design methods that simultaneously tackles both Type I and Type II variation minimization. The method integrates Response Surface Methodology with a compromise Decision Support Problem and is demonstrated on a solar‑powered irrigation system. Our approach is especially useful for design problems lacking closed‑form solutions and where system performance is computationally expensive to evaluate.

Abstract

In this paper, we introduce a small variation to current approaches broadly called Taguchi Robust Design Methods. In these methods, there are two broad categories of problems associated with simultaneously minimizing performance variations and bringing the mean on target, namely, Type I—minimizing variations in performance caused by variations in noise factors (uncontrollable parameters). Type II—minimizing variations in performance caused by variations in control factors (design variables). In this paper, we introduce a variation to the existing approaches to solve both types of problems. This variation embodies the integration of the Response Surface Methodology (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example.