Preprint Article Version 2 This version is not peer-reviewed

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Version 1 : Received: 26 July 2018 / Approved: 26 July 2018 / Online: 26 July 2018 (15:20:02 CEST)
Version 2 : Received: 29 June 2019 / Approved: 1 July 2019 / Online: 1 July 2019 (14:47:16 CEST)

How to cite: Parnianifard, A.; AHMAD, S.A.; Ariffin, M..; Ismai, M.. Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems. Preprints 2018, 2018070517 (doi: 10.20944/preprints201807.0517.v2). Parnianifard, A.; AHMAD, S.A.; Ariffin, M..; Ismai, M.. Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems. Preprints 2018, 2018070517 (doi: 10.20944/preprints201807.0517.v2).

Abstract

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Subject Areas

robust design; Taguchi Method; product design; manufacturing systems; quality engineering; quality loss function

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