Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Jian Pan; Congbo Li; Ying Tang; Wei Li; Xiaoou Li

doi:10.1109/JAS.2021.1003970

Volume 8 Issue 5

May 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(5): 987-1000

J. Pan, C. B. Li, Y. Tang, W. Li, and X. O. Li, "Energy Consumption Prediction of a CNC Machining Process With Incomplete Data," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 987-1000, May. 2021. doi: 10.1109/JAS.2021.1003970

Citation:

J. Pan, C. B. Li, Y. Tang, W. Li, and X. O. Li, "Energy Consumption Prediction of a CNC Machining Process With Incomplete Data," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 987-1000, May. 2021. doi: 10.1109/JAS.2021.1003970

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Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

doi: 10.1109/JAS.2021.1003970

Jian Pan^1
,,
Congbo Li^{1
,
,},
Ying Tang^2
,,
Wei Li^1
,,
Xiaoou Li^3
,

1.
State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
2.
Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028 USA
3.
Computer Science Department, The Research and Advanced Studies Centre of the National Polytechnic Institute, Mexico City 07360, Mexico

Funds: This work was supported in part by the National Natural Science Foundation of China (51975075), Chongqing Technology Innovation and Application Program (cstc2018jszx-cyzdX0183)

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Author Bio:
Jian Pan received the bachelor degree in mechanical engineering from Southwest University in 2018, where he is currently pursuing the M.S. degree in mechanical engineering with a specialization in process modeling and intelligent optimization methods for sustainable manufacturing systems

Congbo Li (M’12–SM’17) received the B.S. and Ph.D. degrees from Chongqing University in 2004 and 2009, respectively. He is currently a Professor of mechanical engineering with Chongqing University. His research interests include modeling and optimization control, and energy-efficient manufacturing. His work has resulted in one book and over 100 journal and conference proceeding articles. Dr. Li is currently serving as the Co-Chair for the IEEE Robotics and Automation Technical Committee on Sustainable Production Automation, a Guest Editor for Special Issue of the IEEE Transactions on Automation Science and Engineering, and Robotics and Computer-Integrated Manufacturing. He has chaired several technical sessions for IEEE conferences, including the Session of the IEEE CASE for Advances in Sustainable Production Automation and the Session of the IEEE SMC for Smart Energy Solutions to Sustainable Production Service and Automation

Ying Tang (M’02–SM’07) received the B.S. and M.S. degrees from Northeastern University in 1996 and 1998, respectively, and the Ph.D. degree from the New Jersey Institute of Technology, USA, in 2001. In 2002, she joined Rowan University, USA, where she is currently a Professor of electrical and computer engineering. Her research interests include modeling and control for computer-integrated systems, green manufacturing, and automation. Her work has resulted in over 150 publications, including one U.S. patent, two edited books, six book chapters, 41 journal articles, and over 100 conference proceeding articles. Dr. Tang is currently serving as an Associate Editor for the IEEE Transactions on Computational Social Systems, the International Journal of Intelligent Control and Systems, and the IEEE Transactions on Automation Science and Engineering, and an Editorial Board Member of the International Journal of Remanufacturing

Wei Li (M’18) received the Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology (HUST) in 2020. He is currently working at the College of Mechanical Engineering, Chongqing University. He had published over 30 refereed papers. His current research interests include energy storage system design optimization, robust design optimization, and multidisciplinary design optimization

Xiaoou Li (M’00–SM’07) has been a professor of Computer Science Department, at The Research and Advanced Studies Centre of the National Polytechnic Institute (CINVESTAV-IPN) in Mexico since 2000. She was a Visiting Professor at the School of Engineering of the University of California Santa Cruz and Shenzhen University in 2010 and 2019, respectively. Her research interests include machine learning and data mining applications, social network analysis, knowledge-based system, Petri nets, neural networks, system modeling and simulation, smart manufacturing, human machine interface (HMI), educational robotics, etc. She has published more than 100 papers on international journals, books, and conferences. Dr. Li is currently Member of Editorial Board of IEEE Press, and Associate Editor of IEEE Transactions on Automation Science and Engineering, IEEE Access, IEEE/CAA Journal of Automatica Sinica, and IEEE SMC Magazine. She is a Regular Member of the AMC (Mexican Association of Science) since 2008, and a Member of SNI (National Researcher System of Mexico) level 2
Corresponding author: Congbo Li, e-mail: congboli@cqu.edu.cn
Received Date: 2020-12-08
Accepted Date: 2021-01-19

Available Online: 2021-02-27

Abstract

Abstract

Energy consumption prediction of a CNC machin- ing process is important for energy efficiency optimization strategies. To improve the generalization abilities, more and more parameters are acquired for energy prediction modeling. While the data collected from workshops may be incomplete because of misoperation, unstable network connections, and frequent transfers, etc. This work proposes a framework for energy modeling based on incomplete data to address this issue. First, some necessary preliminary operations are used for incomplete data sets. Then, missing values are estimated to generate a new complete data set based on generative adversarial imputation nets (GAIN). Next, the gene expression programming (GEP) algorithm is utilized to train the energy model based on the generated data sets. Finally, we test the predictive accuracy of the obtained model. Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data. Experimental results demonstrate that even when the missing data rate increases to 30%, the proposed framework can still make efficient predictions, with the corresponding RMSE and MAE 0.903 kJ and 0.739 kJ, respectively.
- Energy consumption prediction,
- incomplete data,
- generative adversarial imputation nets (GAIN),
- gene expression programming (GEP)

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Highlights

Missing mechanism of energy modeling data for the Machining process is analyzed.
A framework for energy modeling based on incomplete data is proposed.
General and useful tips for utilizing the incomplete data sets are derived.

Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

doi: 10.1109/JAS.2021.1003970

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通讯作者: 陈斌, bchen63@163.com

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