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Low-Carbon Optimization Design of Grinding Machine Spindle Based on Improved Whale Algorithm

Author

Listed:
  • Qi Lu

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Xubo Gao

    (School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Felix T. S. Chan

    (School of Business, Macau University of Science and Technology, Macau, China)

Abstract

To achieve a fundamental reduction in the carbon emissions associated with grinding machines, it is imperative to systematically explore low-carbon considerations in the design phase. The spindle is a significant contributor to carbon emissions in grinding machines, and an effective approach for reducing carbon emissions is the structural optimization of the spindle. Most of the current optimization methods aim at improving processability without considering the reduction of carbon emissions. In this context, the present study addresses the issue of carbon emissions within the spindle design phase. Initially, the determination of the spindle’s carbon emissions function and the selection of the optimization objective were undertaken. The structural factors that have a significant influence on the optimization objective were identified as optimization variables. Subsequently, the optimization objective function was established through the application of the fitting method. Finally, the proposed model was refined through the utilization of an enhanced whale algorithm. The findings indicate an 8.22% reduction in carbon emissions associated with the spindle, accompanied by marginal enhancements in both static and dynamic spindle performance. The concluding section of this paper deliberates on the impact of structural parameters on the specified objectives, thereby providing insights for the optimal design of the spindle.

Suggested Citation

  • Qi Lu & Xubo Gao & Felix T. S. Chan, 2023. "Low-Carbon Optimization Design of Grinding Machine Spindle Based on Improved Whale Algorithm," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:69-:d:1307097
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    References listed on IDEAS

    as
    1. Yingzhi Zhang & Guiming Guo & Fang Yang & Yubin Zheng & Fenli Zhai, 2023. "Prediction of Tool Remaining Useful Life Based on NHPP-WPHM," Mathematics, MDPI, vol. 11(8), pages 1-17, April.
    2. Hu, Luoke & Liu, Ying & Lohse, Niels & Tang, Renzhong & Lv, Jingxiang & Peng, Chen & Evans, Steve, 2017. "Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed," Energy, Elsevier, vol. 139(C), pages 935-946.
    3. Zhang, Tao & Liu, Zhanqiang & Sun, Xiaodong & Xu, Jixiang & Dong, Longlong & Zhu, Genglei, 2020. "Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory," Energy, Elsevier, vol. 192(C).
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