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Multi-component energy modeling and optimization for sustainable dry gear hobbing

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Listed:
  • Xiao, Qinge
  • Li, Congbo
  • Tang, Ying
  • Pan, Jian
  • Yu, Jun
  • Chen, Xingzheng

Abstract

Sustainable machining becomes a key priority for manufacturing industries due to the ever growing energy costs, associated environmental impacts and carbon emissions. As one of the frequent activities in metal machining, dry gear hobbing contributes to a significant portion of energy consumption. Process parameter optimization is an effective method of decreasing energy from process control perspective. However, hobbing parameter optimization is rarely involved in previous studies. To this end, a multi-component energy model is first developed on a basis of energy characteristics analysis of dry gear hobbing machines. Then, the optimization of hobbing parameters for the minimizing energy consumption and production cost is formulated as mathematical programming problem with a systematic consideration of machining constraints. Finally, the optimization problem is solved by a modified multi-objective imperialist competitive algorithm (MOICA). The results demonstrate that the energy-efficient gear hobbing can be achieved through a collaborative effort of predictive modeling and parameter optimization.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:187:y:2019:i:c:s0360544219315890
    DOI: 10.1016/j.energy.2019.115911
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    References listed on IDEAS

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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. Yoon, Hae-Sung & Kim, Eun-Seob & Kim, Min-Soo & Lee, Jang-Yeob & Lee, Gyu-Bong & Ahn, Sung-Hoon, 2015. "Towards greener machine tools – A review on energy saving strategies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 870-891.
    3. Xiao, Qinge & Li, Congbo & Tang, Ying & Li, Lingling & Li, Li, 2019. "A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning," Energy, Elsevier, vol. 166(C), pages 142-156.
    4. Leilei Meng & Chaoyong Zhang & Xinyu Shao & Yaping Ren & Caile Ren, 2019. "Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines," International Journal of Production Research, Taylor & Francis Journals, vol. 57(4), pages 1119-1145, February.
    5. Hu, Luoke & Liu, Ying & Peng, Chen & Tang, Wangchujun & Tang, Renzhong & Tiwari, Ashutosh, 2018. "Minimising the energy consumption of tool change and tool path of machining by sequencing the features," Energy, Elsevier, vol. 147(C), pages 390-402.
    6. Schudeleit, Timo & Züst, Simon & Weiss, Lukas & Wegener, Konrad, 2016. "The Total Energy Efficiency Index for machine tools," Energy, Elsevier, vol. 102(C), pages 682-693.
    7. Zhao, G.Y. & Liu, Z.Y. & He, Y. & Cao, H.J. & Guo, Y.B., 2017. "Energy consumption in machining: Classification, prediction, and reduction strategy," Energy, Elsevier, vol. 133(C), pages 142-157.
    8. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    9. Jeffrey Kuo, Chung-Feng & Su, Te-Li & Jhang, Po-Ruei & Huang, Chao-Yang & Chiu, Chin-Hsun, 2011. "Using the Taguchi method and grey relational analysis to optimize the flat-plate collector process with multiple quality characteristics in solar energy collector manufacturing," Energy, Elsevier, vol. 36(5), pages 3554-3562.
    10. Liu, Peiji & Liu, Fei & Qiu, Hang, 2017. "A novel approach for acquiring the real-time energy efficiency of machine tools," Energy, Elsevier, vol. 121(C), pages 524-532.
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    Cited by:

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    2. 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).
    3. Benjie Li & Hualin Zheng & Xiao Yang & Liang Guo & Binglin Li, 2020. "Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints," Energies, MDPI, vol. 13(22), pages 1-17, November.
    4. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).

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