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Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality

Author

Listed:
  • Zhang, Jiaqi
  • Han, Xin
  • Li, Li
  • Jia, Shun
  • Jiang, Zhigang
  • Duan, Xiangmin
  • Lai, Kee-hung
  • Cai, Wei

Abstract

To promote energy saving, high efficiency and quality production in mechanical manufacturing industry, a large number of studies have been conducted on parameters optimisation, especially for turning. However, most of the approaches have focused on single objective model and unidirectional turning (UDT). This paper presents a multi-objective parameter optimisation method for energy consumption, cutting time and surface roughness using new process of forward-and-reverse multidirectional turning (MDT). Firstly, the composition characteristics of cutting parameters for energy consumption, cutting time and surface roughness of MDT are analyzed to establish the corresponding models through nonlinear regression fitting, respectively. Then taking the minimum energy consumption, cutting time and surface roughness as the optimisation objectives, the improved fireworks algorithm can generate nearly 100 groups of optimal solutions. The most suitable processing parameters solution needs to be selected according to different machining requirements. In application scenarios, most relative errors of the models are within 10%. Comparing the machining performance of MDT and UDT under the same cutting parameters, the processing efficiency is increased by 25.97% and energy consumption is reduced by 12.38%, using the MDT. This study provides a multi-objective optimisation approach and models for MDT to reduce energy consumption, improve production efficiency and quality.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223025999
    DOI: 10.1016/j.energy.2023.129205
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    1. 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.
    2. Ying CAO & Xiaomei Li & Haoben YAN & Shuya KUANG, 2021. "China’s Efforts to Peak Carbon Emissions: Targets and Practice," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-14, March.
    3. Zhang, Yuanhui & Cai, Wei & He, Yan & Peng, Tao & Jia, Shun & Lai, Kee-hung & Li, Li, 2022. "Forward-and-reverse multidirectional turning: A novel material removal approach for improving energy efficiency, processing efficiency and quality," Energy, Elsevier, vol. 260(C).
    4. Pimenov, Danil Yu & Mia, Mozammel & Gupta, Munish K. & Machado, Álisson R. & Pintaude, Giuseppe & Unune, Deepak Rajendra & Khanna, Navneet & Khan, Aqib Mashood & Tomaz, Ítalo & Wojciechowski, Szymon &, 2022. "Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    5. Cai, Wei & Li, Yanqi & Li, Li & Lai, Kee-hung & Jia, Shun & Xie, Jun & Zhang, Yuanhui & Hu, Luoke, 2022. "Energy saving and high efficiency production oriented forward-and-reverse multidirectional turning: Energy modeling and application," Energy, Elsevier, vol. 252(C).
    6. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. Cai, Wei & Liu, Fei & Zhou, XiaoNa & Xie, Jun, 2016. "Fine energy consumption allowance of workpieces in the mechanical manufacturing industry," Energy, Elsevier, vol. 114(C), pages 623-633.
    8. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    9. Luoke Hu & Renzhong Tang & Keyan He & Shun Jia, 2015. "Estimating machining-related energy consumption of parts at the design phase based on feature technology," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7016-7033, December.
    10. Mouna Gargouri Mnif & Sadok Bouamama, 2020. "A New Multi-Objective Firework Algorithm to Solve the Multimodal Planning Network Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 11(4), pages 91-113, October.
    11. Chen, Xingzheng & Li, Congbo & Tang, Ying & Li, Li & Du, Yanbin & Li, Lingling, 2019. "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, Elsevier, vol. 175(C), pages 1021-1037.
    12. Longhua Xu & Chuanzhen Huang & Chengwu Li & Jun Wang & Hanlian Liu & Xiaodan Wang, 2021. "Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 77-90, January.
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    1. Li, Yanqi & Chen, Junming & Wang, Yu & Li, Shunjiang & Duan, Xiangmin & Jiang, Zhigang & Lai, Kee-hung & Cai, Wei, 2024. "Multi-objective modeling and evaluation for energy saving and high efficiency production oriented multidirectional turning considering energy, efficiency, economy and quality," Energy, Elsevier, vol. 294(C).

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