Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Process parameter optimization for MIMO plastic injection molding via soft computing
Expert Systems with Applications: An International Journal
Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments
Applied Soft Computing
Creep feed grinding optimization by an integrated GA-NN system
Journal of Intelligent Manufacturing
Solving the multi-response problem in Taguchi method by benevolent formulation in DEA
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
Intelligence modeling for coping strategies to reduce emergency department overcrowding in hospitals
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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The Taguchi robust parameter design has been widely used over the past decade to solve many single-response process parameter designs. However, the Taguchi method is unable to deal with multi-response problems that are of main interest today, owing to increasing complexity of manufacturing processes and products. Several recent studies have been conducted in order to solve this problem. But, they did not effectively treat situations where responses are correlated and situations in which control factors have continuous values. This study proposed an integrated model for experimental design of processes with multiple correlated responses, composed of three stages which (1) use expert system, designed for selecting an inner and an outer orthogonal array, to design an actual experiment, (2) use Taguchi's quality loss function to present relative significance of responses, and multivariate statistical methods to uncorrelate and synthesise responses into a single performance measure, (3) use neural networks to construct the response function model and genetic algorithms to optimise parameter design. The effectiveness of the proposed model is illustrated with three examples. Results of analysis showed that the proposed approach could yield a better solution in terms of the optimal parameters setting that results in a higher process performance measure than the traditional experimental design.