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Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission

Received: 10 April 2023     Accepted: 17 June 2023     Published: 27 June 2023
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Abstract

In order to achieve the low carbon and efficient working effect of CNC machine tools, this paper takes the 2243 VMC machining centre as the research object to study the problem between carbon emission and time, cost and process parameters in the machining process of machining centre machine tools, and establish the optimization model of CNC milling process parameters based on carbon emission. Carbon emissions, machining cost and machining time are taken as the optimisation objectives, tool life, roughness and machine power are taken as constraints, and spindle speed, feed per tooth, cutting width and depth of cut are taken as optimisation variables. The model is solved using a particle swarm algorithm (PSO) to produce a Pareto solution set for multi-objective optimisation. Finally, the optimisation results were compared with the calculated results for the empirical parameters to verify the feasibility of the model. The results show that the calculated results for the optimised parameters provide a 19.53% reduction in carbon emissions, a 12.96% reduction in processing costs and a 13.72% reduction in processing time after optimisation by the algorithm compared to the calculated results for the empirical parameters, indicating that the algorithm provides a better optimisation than the empirical parameters.

Published in Journal of Electrical and Electronic Engineering (Volume 11, Issue 3)
DOI 10.11648/j.jeee.20231103.12
Page(s) 77-81
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Carbon Emissions, CNC Milling, Process Parameter, Adaptive Grid Particle Swarm Algorithm

References
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[5] Jeswiet J., Kara S. Carbon emissions and CESTM in manufacturing [J]. CIRP Annals- Manufacturing Technology, 2017, 57 (1): 17-20.
[6] Munoz A A, Sheng P. An analytical approach for determining the environmental impact of machining processes [J]. Journal of Materials Processing Technology, 1995, 53 (3-4): 736-758.
[7] Chen Zhichu, Li Cong et al. Cutting parameter optimization based on imperialistic competitive algorithm [J]. Manufacturing Automation, 2012, 34 (12): 10-15.
[8] Li XP, Zhang ChaoYong et al. CNC cutting parameter optimization based on metacellular particle swarm algorithm [J]. Computer Engineering and Applications, 2014, 50 (2): 252-257.
[9] Liu Qiong, Tian Youquan, Zhou Yingdong. Carbon footprint accounting of product manufacturing process and its optimization [J]. China Mechanical Engineering, 2015, 26 (17): 2336-2343.
[10] Mei Zhimin, Zhang Rong, Xiong Qing, et al. Analysis and research on the transformation of low carbon manufacturing process of mechanical products [J]. Machine Manufacturing and Automation, 2012, 02: 75-78.
[11] Li Congbo, Xiao Qinqi et al. Energy-efficient optimization method of CNC milling process parameters based on Taguchi method and response surface method [J]. Computer Integrated Manufacturing Systems, 2015, 21 (12): 3182-3191.
[12] Ni Hengxin, Yan Chunping et al. Multi-objective optimization and decision making method for high speed dry cutting hobbing process parameters [J]. China Mechanical Engineering, 2021, 32 (7): 832-838.
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  • APA Style

    Juan Wei, Yaxin Wei, Mengdi Wei, Simin Ren. (2023). Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission. Journal of Electrical and Electronic Engineering, 11(3), 77-81. https://doi.org/10.11648/j.jeee.20231103.12

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    ACS Style

    Juan Wei; Yaxin Wei; Mengdi Wei; Simin Ren. Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission. J. Electr. Electron. Eng. 2023, 11(3), 77-81. doi: 10.11648/j.jeee.20231103.12

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    AMA Style

    Juan Wei, Yaxin Wei, Mengdi Wei, Simin Ren. Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission. J Electr Electron Eng. 2023;11(3):77-81. doi: 10.11648/j.jeee.20231103.12

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  • @article{10.11648/j.jeee.20231103.12,
      author = {Juan Wei and Yaxin Wei and Mengdi Wei and Simin Ren},
      title = {Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {11},
      number = {3},
      pages = {77-81},
      doi = {10.11648/j.jeee.20231103.12},
      url = {https://doi.org/10.11648/j.jeee.20231103.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20231103.12},
      abstract = {In order to achieve the low carbon and efficient working effect of CNC machine tools, this paper takes the 2243 VMC machining centre as the research object to study the problem between carbon emission and time, cost and process parameters in the machining process of machining centre machine tools, and establish the optimization model of CNC milling process parameters based on carbon emission. Carbon emissions, machining cost and machining time are taken as the optimisation objectives, tool life, roughness and machine power are taken as constraints, and spindle speed, feed per tooth, cutting width and depth of cut are taken as optimisation variables. The model is solved using a particle swarm algorithm (PSO) to produce a Pareto solution set for multi-objective optimisation. Finally, the optimisation results were compared with the calculated results for the empirical parameters to verify the feasibility of the model. The results show that the calculated results for the optimised parameters provide a 19.53% reduction in carbon emissions, a 12.96% reduction in processing costs and a 13.72% reduction in processing time after optimisation by the algorithm compared to the calculated results for the empirical parameters, indicating that the algorithm provides a better optimisation than the empirical parameters.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission
    AU  - Juan Wei
    AU  - Yaxin Wei
    AU  - Mengdi Wei
    AU  - Simin Ren
    Y1  - 2023/06/27
    PY  - 2023
    N1  - https://doi.org/10.11648/j.jeee.20231103.12
    DO  - 10.11648/j.jeee.20231103.12
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 77
    EP  - 81
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20231103.12
    AB  - In order to achieve the low carbon and efficient working effect of CNC machine tools, this paper takes the 2243 VMC machining centre as the research object to study the problem between carbon emission and time, cost and process parameters in the machining process of machining centre machine tools, and establish the optimization model of CNC milling process parameters based on carbon emission. Carbon emissions, machining cost and machining time are taken as the optimisation objectives, tool life, roughness and machine power are taken as constraints, and spindle speed, feed per tooth, cutting width and depth of cut are taken as optimisation variables. The model is solved using a particle swarm algorithm (PSO) to produce a Pareto solution set for multi-objective optimisation. Finally, the optimisation results were compared with the calculated results for the empirical parameters to verify the feasibility of the model. The results show that the calculated results for the optimised parameters provide a 19.53% reduction in carbon emissions, a 12.96% reduction in processing costs and a 13.72% reduction in processing time after optimisation by the algorithm compared to the calculated results for the empirical parameters, indicating that the algorithm provides a better optimisation than the empirical parameters.
    VL  - 11
    IS  - 3
    ER  - 

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Author Information
  • School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, China

  • School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, China

  • School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, China

  • School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, China

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