Robust Design: An Overview

TLDR

Robust design aims to produce products that are insensitive to external noise and tolerances, thereby increasing the likelihood of achieving target performance, and it has evolved from theories in other fields into three main approaches: Taguchi, robust optimization, and axiomatic methods. The paper reviews and investigates these three methods, examining them from both theoretical and application perspectives. The authors discuss each method’s advantages and drawbacks and propose future research directions.

Abstract

Robust design has been developed with the expectation that an insensitive design can be obtained. That is, a product designed by robust design should be insensitive to external noises or tolerances. An insensitive design has more probability to obtain a target value, although there are uncertain noises. Theories of robust design have been developed by adopting the theories of other fields. Based on the theories, robust design can be classified into three methods: 1) the Taguchi method, 2) robust optimization, and 3) robust design with the axiomatic approach. Each method is reviewed and investigated. The methods are examined from a theoretical viewpoint and are discussed from an application viewpoint. The advantages and drawbacks of each method are discussed, and future directions for development are proposed.