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