Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms

To combat climate change, many industries have participated in the research on alternative energies. Industrial Technology Research Institute in Taiwan has developed techniques for the solar energy selective absorption film continuous sputtering process. For this extremely complicated process, plenty of parameters would influence the output quality. If parameters settings simply rely on the experience of engineers, the defect rate may increase due to instability. A more reliable approach is desirable to optimize the condition of manufacturing process parameters, thus improving the quality. The present study applies a systematic procedure for the parameter optimization of the absorption film continuous sputtering process. First, possible variables are determined based on collected data and engineering knowledge. Second, Taguchi methods are utilized to search for the significant factors and the optimal level combination of parameters. Finally, the integration of back-propagation neural network, desirability function, and genetic algorithms is used to obtain the optimal parameters setting. According to the experiment results, the performance of the integrated procedure is better than that of Taguchi methods and traditional approach. Furthermore, if applying the integrated method, the saving energy would achieve 9770.53 kiloliter of oil equivalent (kLOE) per year, which is 11.2 times the saving kLOE of the traditional paint process. (c) 2012 Elsevier Ltd. All rights reserved.