Taguchi’s methodology for multi‐response optimization

Abstract

Reports on a study carried out on an injection moulding process to produce agitators for washing machines, following complaints from customers. The study revealed that there was a lot of variation in the product dimensions produced. Attempts to demonstrate how robust design methodology is helpful in achieving variation reduction of product dimensions and achieving target values. Various analyses carried out using ANOVA and ANOM helped to understand the dynamics of the process. In the presence of multi‐responses and specially when the responses have conflicting behavior to each other, selection of optimum conditions for the process is explained in detail. Reports on the importance of confirmation experiments and the outcome of this detailed exercise.

Multi-objective Optimal Parameter Design of Nanofilters and Heat Sink Fins in Train Traction Motor Drive System

“…The derived final fuzzy MPCI values in Case 3 are listed in Table 5 and compared with those in Cases 1 and 2. In order to build the statistical model of the RSM, 16 experimental runs with four central-point experimental runs are performed, as listed in Table 5. (19,20) There are a total of 20 experimental runs in the RSM.…”

Simultaneous optimization of multiple performance characteristics of carbonitrided pellets: a case study

“…Detailed discussion on application of Taguchi method in multiresponse optimization can be found in Logothetis and Haigh [2], Tong et al [3] and Maghsoodloo and Chang [4]. Reddy et al [5] presented a case study in the Indian plastic industry based on Taguchi's methodology for multiresponse optimization. While Tong and Su [6] suggested optimization of multiple quality characteristics using fuzzy multiple attribute decision-making, Wu [7] proposed simultaneous optimization of several response variables based on percentage reduction of Taguchi's quality loss.…”