Authors: Seyed Hamid Reza Pasandideh Seyed Taghi Akhavan Niaki Seyed Mahdi Atyabi
Publish Date: 2014/08/14
Volume: 75, Issue: 5-8, Pages: 1149-1162
Abstract
Adjusting control factors independent variables to achieve an optimal level of output response variable is usually required in many realworld manufacturing problems Common optimization methods often begin with estimating the relationship between a response and independent variables Among these techniques response surface methodology RSM due to its simplicity has recently attracted extensive attention However on the one hand in some cases the relationship between a response and independent variables is too complex to be estimated using polynomial regression models On the other hand solving the obtained optimization model is not easy by exact methods This paper introduces a new methodology to solve multiresponse statistical optimization problems The novel hybrid approach of this research involves a modeling technique a neural network methodology and a genetic algorithm The modeling technique that is selected among three common available methods is responsible to model the multiresponse statistical problem The neural network approach generates required input data and finally the genetic algorithm tries to optimize the model and find the adjusted levels of the control factors At the end the application and the performance of the proposed methodology are demonstrated using numerical examples The results of several statistical analyses show that the proposed methodology is superior to other available methods in the literature
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