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A comprehensive approach to parameters optimization of energy-aware CNC milling

Author

Listed:
  • Congbo Li

    (Chongqing University)

  • Lingling Li

    (Chongqing University)

  • Ying Tang

    (Rowan University)

  • Yantao Zhu

    (Chongqing University)

  • Li Li

    (Southwest University)

Abstract

Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

Suggested Citation

  • Congbo Li & Lingling Li & Ying Tang & Yantao Zhu & Li Li, 2019. "A comprehensive approach to parameters optimization of energy-aware CNC milling," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 123-138, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1233-y
    DOI: 10.1007/s10845-016-1233-y
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    References listed on IDEAS

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    1. Chelouah, Rachid & Siarry, Patrick, 2000. "Tabu Search applied to global optimization," European Journal of Operational Research, Elsevier, vol. 123(2), pages 256-270, June.
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    Cited by:

    1. Yakun Jiang & Jihong Chen & Huicheng Zhou & Jianzhong Yang & Guangda Xu, 2020. "Nonlinear time-series modeling of feed drive system based on motion states classification," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1935-1948, December.
    2. Hengyuan Ma & Wei Liu & Xionghui Zhou & Qiang Niu & Chuipin Kong, 2020. "An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 967-984, April.
    3. Lijun Song & Jing Shi & Anda Pan & Jie Yang & Jun Xie, 2020. "A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption," Energies, MDPI, vol. 13(10), pages 1-18, May.
    4. Zhang, Jiaqi & Han, Xin & Li, Li & Jia, Shun & Jiang, Zhigang & Duan, Xiangmin & Lai, Kee-hung & Cai, Wei, 2023. "Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality," Energy, Elsevier, vol. 284(C).
    5. Zhao, Junhua & Li, Li & Li, Lingling & Zhang, Yunfeng & Lin, Jiang & Cai, Wei & Sutherland, John W., 2023. "A multi-dimension coupling model for energy-efficiency of a machining process," Energy, Elsevier, vol. 274(C).
    6. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).

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