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Estimating machining-related energy consumption of parts at the design phase based on feature technology

Author

Listed:
  • Luoke Hu
  • Renzhong Tang
  • Keyan He
  • Shun Jia

Abstract

To overcome the difficulties in previous researches about energy-efficient design of parts, a method to estimate machining-related energy consumption of parts at the design phase is proposed. The binary tree is constructed to describe the structure of a part, and each node in the binary tree represents one feature in the part. The material embodied energy, theoretical cutting energy consumption and air-cutting energy consumption of a feature can be calculated based on its design and manufacturing parameters. At the design phase, manufacturing parameters of a feature can be obtained by the method of feature mapping from design parameters. By adding up above three types of energy consumption, total energy consumption of a feature can be calculated. Further, by adding up total energy consumption of all features in a part, the energy consumption of this part can be estimated. The proposed method was demonstrated by estimating the energy consumption of a shaft part designed by an auto parts manufacturer, and meanwhile the measured energy consumption of the shaft part was acquired by experimental measurement. The estimation accuracy is analysed and verified by comparing the estimated value and measured value.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7016-7033
    DOI: 10.1080/00207543.2014.944281
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    Cited by:

    1. Cai, Wei & Lai, Kee-hung, 2021. "Sustainability assessment of mechanical manufacturing systems in the industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. 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.
    3. 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).
    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. 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. Guo, Yansong & Duflou, Joost R. & Deng, Yelin & Lauwers, Bert, 2018. "A life cycle energy analysis integrated process planning approach to foster the sustainability of discrete part manufacturing," Energy, Elsevier, vol. 153(C), pages 604-617.
    7. Shedong Ren & Fangzhi Gui & Yanwei Zhao & Min Zhan & Wanliang Wang & Jianqiang Zhou, 2021. "An Extenics-Based Scheduled Configuration Methodology for Low-Carbon Product Design in Consideration of Contradictory Problem Solving," Sustainability, MDPI, vol. 13(11), pages 1-41, May.

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