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

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. 1. 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 [1–3] 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. Su et al. [5] proposed a new circuit design optimization method where genetic algorithm (GA) is combined with Taguchi method. Lo and Tsao [6] modified an analytical linkage-spring model based on neural network analysis and the Taguchi method to determine the design rules for reducing the loop height and the sagging altitude of gold wire-bonding process of the integrated circuit (IC) package. In Taguchi’s design method, the control variables (factors can be controlled by analyst) and noise variables (factors cannot be controlled by analyst) are considered influential on product quality. Therefore, the Taguchi method is to choose the levels of control variables and to reduce the effects of noise variables. That is, control variables setting