Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process |
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| Affiliation: | 1. School of Mechanical Engineering & Automation, Beihang University, China;2. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;3. School of Cyber Security, University of Chinese Academy of Sciences; State Key Laboratory of Information Security, Institute of Information Engineering, Beijing, China;1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China;3. Green Fan Manufacturing Collaborative Innovation Center in Hubei Province, Wuchang Institute of Technology, Wuhan 430065, China;1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, China;2. Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, China;3. School of Mechanical Engineering, Shanghai Jiao Tong University, China;4. Department of Industrial Engineering, University of Arkansas, USA;1. Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology De Zaale, 5600 MB, Eindhoven, The Netherlands;2. Equipment and Automation Technologies (E&A), Nexperia Jonkerbosplein 52, 6534 AB, Nijmegen, The Netherlands;1. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310027, China;2. School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China;1. Industrial Engineering, University at Buffalo, Buffalo, NY, 14260 USA;2. School of Computing Informatics & Decision Systems Engineering, Arizona State University, Tempe, AZ, 85251 USA;3. Alta Devices Inc. Sunnyvale, CA 94085 USA;4. Computer Science and Engineering, University at Buffalo, Buffalo, NY, 14260 USA |
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| Abstract: | The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition. |
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| Keywords: | Statistical process control Engineering process control Control chart patterns Recurrent neural network Multivariate adaptive regression splines |
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