A Comparison of Parameter Design Methods for Multiple Performance Characteristics

Abstract

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCAbased method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated. †

Optimization of Digital Transfer Textile Printing Process using Multi-Objective Function Analysis

“…The Taguchi method is a robust design technique used to efficiently determine optimum experimental conditions [1,2,10]. Characteristic values are categorized as one of three types including nominalis-best, smaller-the-better, and larger-the-better.…”

“…(9). Analysis was done with the shape parameter s of 0.5, 1, and 2 per previous research [10]. All data was converted…”

“…the standard upper and lower limits of each characteristic value to set a valid range of experimental results[10].…”

The Taguchi Robust Design Method : Current Status and Future Directions

“…et al(1991), Myers et al(1992) Leon et al(1987) Taguchi(1987Taguchi( , 1988aTaguchi( , 1988b Kuhn et al(2000) 등을 참고하기 바란다. Taguchi(1987Taguchi( , 1988b (Box and Jones, 1986;Hamada and Wu, 1990) Shiau, 1990;Tai et al, 1992), 기대손실로 선택하면 종합 기대 손실이 된다 (Soh and Yum, 2012). 그러나, 특성치마다 성능측 도의 변동범위가 상이할 수 있으므로   를 다음과 같이 구간 Lin and Tarng, 1998;Lin and Lin, 2002;Tarng et al, 2002;Gauri and Pal, 2010 등), 퍼지로직에 기반을 둔 접근법 Lin et al, 2000;Kim, 2005 등), 그리고, 원래 다기준 의사결정 을 위해 개발된 Opricovic and Tzeng(2004)의 VIKOR(Serbian으 로 VlseKriterijumska Optimizacija I Kompromisno Resenje의 약자, 영역하면 Multicriteria Optimization and Compromise Solution) 접근법 (Tong et al, 2007;Gauri and Pal, 2010 등)이 있다.…”