Energy Consumption Prediction of a CNC Machining Process with Incomplete Data

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

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Energy consumption prediction of a CNC machining 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.

    Original languageEnglish (US)
    Article number9395543
    Pages (from-to)987-1000
    Number of pages14
    JournalIEEE/CAA Journal of Automatica Sinica
    Volume8
    Issue number5
    DOIs
    StatePublished - May 2021

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Information Systems
    • Artificial Intelligence

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