Multiple response optimization using Taguchi methodology and neuro‐fuzzy based model

Abstract

Purpose

To provide a good insight into solving a multi‐response optimization problem using neuro‐fuzzy model and Taguchi method of experimental design.

Design/methodology/approach

Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision‐making process. In this paper, a four‐step procedure is proposed to resolve the parameter design problem involving multiple responses. This approach employs the advantage of both artificial intelligence tool (neuro‐fuzzy model) and Taguchi method of experimental design to tackle problems involving multiple responses optimization.

Findings

The proposed methodology is validated by revisiting a case study to optimize the three responses for a double‐sided surface mount technology of an electronic assembly. Multiple signal‐to‐noise ratios are mapped into a single performance statistic through neuro‐fuzzy based model, to identify the optimal level settings for each parameter. Analysis of variance is finally performed to identify parameters significant to the process.

Research limitations/implications

The proposed model will be validated in future by conducting a real life case study, where multiple responses need to be optimized simultaneously.

Practical implications

It is believed that the proposed procedure in this study can resolve a complex parameter design problem with multiple responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready‐made neural and statistical software like Neuro Work II professional and Minitab.

Originality/value

This study adds to the literature of multi‐optimization problem, where a combination of the neuro‐fuzzy model and Taguchi method is utilized hand‐in‐hand.

Citation

Antony, J., Bardhan Anand, R., Kumar, M. and Tiwari, M.K. (2006), "Multiple response optimization using Taguchi methodology and neuro‐fuzzy based model", Journal of Manufacturing Technology Management, Vol. 17 No. 7, pp. 908-925. https://doi.org/10.1108/17410380610688232