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An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

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  • Meihang Zhang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Hua Zhang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Precision Manufacturing Research Institute of Wuhan University of Science and Technology, Wuhan 430081, China)

  • Wei Yan

    (Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    School of Automotive and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Zhigang Jiang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Shuo Zhu

    (Precision Manufacturing Research Institute of Wuhan University of Science and Technology, Wuhan 430081, China
    Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error ( MAE )and mean error( ME ).

Suggested Citation

  • Meihang Zhang & Hua Zhang & Wei Yan & Zhigang Jiang & Shuo Zhu, 2023. "An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5781-:d:1108095
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    References listed on IDEAS

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