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

Ali Salmasnia,Mahdi Bastan,Asghar Moeini

doi:10.1155/2012/494818

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.

Highlights

Controllable input variables set to an industrial process to achieve proper operating conditions are one of the common problems in quality control
In Taguchi’s design method, the control variables and noise variables are considered influential on product quality
Control variables setting should be determined with the intention that the quality characteristic has minimum variation while its mean is close to the desired target

Summary

Introduction

Controllable input variables set to an industrial process to achieve proper operating conditions are one of the common problems in quality control. Taguchi method [1,2,3] is a widely accepted technique among industrial engineers and quality control practitioners for producing high quality products at low cost. In this regard, Ko et al [4] employed Taguchi method and artificial neural network to perform design in multistage metal forming processes considering work ability limited by ductile fracture. Common problem in the simultaneous optimization of response variables is to be different and sometimes contradictory to their optimality

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Conclusion