A model based on neural network and data envelopment analysis to optimize multi response Taguchi under uncertainty

"Taguchi" method is a conventional method for quality control in offline mode. This method is applied to design and select the best level of parameters for designing a better method to make high quality products. Taguchi method is one-response and it is a disadvantage for it. In the real world applications, there are several problems with some indicators of quality. Therefore, Taguchi method is not appropriate for optimizing multi-response problems and we need to an engineering and optimizing method to judge the best combination of parameters. On the other hand, due to some uncontrollable factors or the impossibility of experimental conditions, only some of experiments are implemented and a large number of them are incomplete. In this paper, to simulate the remaining experiments the Back-Propagation neural network is used. To overcome one-response problem in Taguchi method, the data envelopment analysis (DEA) is used. Since the results obtained from the neural network are uncertain, DEA model with interval grey data is used. To implement this approach and to identify effective factors, the wear characteristics of composite material PBT, the combined approach based on Taguchi method, neural network and DEA is used and the results will be analyzed.