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

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.

Advanced Multiresponse Process Optimisation

“…Ko et al 1998Ko et al , 1999Hinduja et al 2000;Monostori and Viharos 2001;Hsieh and Tong 2001;Hsu et al 2004;Tong et al 2004a, b;Liau and Chen 2005;Jean et al 2005;Hsieh 2006;Noorossana et al 2008;Chou 2006, 2008;Salmasnia et al 2012), including modelling of a dynamic system (Su et al 2005;Su and Hsieh 1998). nonlinear relationship between process outputs and inputs, without going deep into the mathematical complexity or prior assumptions on the functional form of this relationship (such as linear, quadratic, higher order polynomial, and exponential form) makes ANN an attractive alternative choice for many researchers to model manufacturing processes.…”

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