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Volume 8 Issue 5
May  2021

IEEE/CAA Journal of Automatica Sinica

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Jian Pan, Congbo Li, Ying Tang, Wei Li and Xiaoou 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: Jian Pan, Congbo Li, Ying Tang, Wei Li and Xiaoou 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

Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

doi: 10.1109/JAS.2021.1003970
Funds:  This work was supported in part by the National Natural Science Foundation of China (51975075), Chongqing Technology Innovation and Application Program (cstc2018jszx-cyzdX0183)
More Information
  • 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.


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    • 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.


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