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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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    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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