Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Process parameter optimization via data mining technique
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
A data mining approach to dynamic multiple responses in Taguchi experimental design
Expert Systems with Applications: An International Journal
Robust design of nonlinear multiple dynamic quality characteristics
Computers and Industrial Engineering
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Taguchi method was known as an off-line quality control methodology to be used in many industries. Until now, most applications only focus on optimizing a single-response in a static system. Furthermore, due to the increasing complexity of the product design, more than one quality characteristic must be considered simultaneously to improve the production quality. Therefore, there are several studies to address the multi-response problem. In order to satisfy the requirements of the production's design, optimization of a dynamic system been mentioned by Taguchi has received more attentions in the recent years. Hence, optimizing a multi-response problem in a dynamic system becomes an important issue to address the quality improvement.This study proposes a procedure utilizing the statistic regression analysis and desirability function to optimize the multiresponse problem with Taguchi's dynamic system consideration. Firstly, the regression analysis is employed to screen out the control factors significantly affecting the quality variation, and the adjustment factors significantly affecting the sensitivity ofa Taguchi's dynamic system. Then, the desirability function will be applied to optimize such a multi-response problem. Finally, the effectiveness of the proposed procedure will be demonstrated by an example of a biological reduction of ethyl acetoacetate process experiment project at the Union Chemical Laboratories of the Industrial Technology Research Institute in Taiwan.