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A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Author

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  • Jianhua Cao

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

  • Xuhui Xia

    (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)

  • Lei Wang

    (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)

  • Zelin Zhang

    (Precision Manufacturing Institute, 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)

  • Xiang Liu

    (Precision Manufacturing Institute, 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)

Abstract

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

Suggested Citation

  • Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2021. "A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13918-:d:704021
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    References listed on IDEAS

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    1. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
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    Cited by:

    1. Zhaohui Feng & Xinru Ding & Hua Zhang & Ying Liu & Wei Yan & Xiaoli Jiang, 2023. "An Energy Consumption Estimation Method for the Tool Setting Process in CNC Milling Based on the Modular Arrangement of Predetermined Time Standards," Energies, MDPI, vol. 16(20), pages 1-18, October.

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