Table of Contents
International Journal of Quality, Statistics, and Reliability
Volume 2012, Article ID 494818, 11 pages
http://dx.doi.org/10.1155/2012/494818
Research Article

A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

1Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
2Department of Industrial Engineering, Eyvanekey University, Semnan, Iran
3Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

Received 12 February 2012; Revised 16 May 2012; Accepted 3 June 2012

Academic Editor: Tadashi Dohi

Copyright © 2012 Ali Salmasnia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.