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Fine energy consumption allowance of workpieces in the mechanical manufacturing industry

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  • Cai, Wei
  • Liu, Fei
  • Zhou, XiaoNa
  • Xie, Jun

Abstract

The Energy Consumption Allowance (ECA) has been recognized as an effective analytical methodology and management tool that helps to improve efficiency and performance. With wide distribution and great energy consumption in low efficiency, the mechanical manufacturing industry has considerable energy-saving potential. This paper illustrates the concept and connotation of traditional ECA and has systematically analysed the deficiencies of the traditional ECA in the mechanical manufacturing industry. To overcome the deficiencies in the application process, a new concept of fine energy consumption allowance (FECA) for workpieces has been proposed contributing to strengthening energy monitoring and management and improving energy efficiency in the mechanical manufacturing industry. Based on establishing a framework for the FECA of the workpiece, a method for developing the FECA of the workpiece was proposed including five steps: (i) analysis of energy consumption in the machining process; (ii) establishment of a basic energy consumption database; (iii) determination of time parameters; (iv) determination of the ECA of each procedure and acquirement of the FECA; and (v) application of the fine energy consumption allowance card (FECA-card). Furthermore, a case study illustrates the practicability of the proposed method by establishing a primary FECA for the workpiece in a real machining plant.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:623-633
    DOI: 10.1016/j.energy.2016.08.028
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    7. Liu, Conghu & Cai, Wei & Dinolov, Ognyan & Zhang, Cuixia & Rao, Weizhen & Jia, Shun & Li, Li & Chan, Felix T.S., 2018. "Emergy based sustainability evaluation of remanufacturing machining systems," Energy, Elsevier, vol. 150(C), pages 670-680.
    8. Zhaohui Feng & Xinru Ding & Hua Zhang & Ying Liu & Wei Yan & Xiaoli Jiang, 2023. "An Energy Consumption Estimation Method for the Tool Setting Process in CNC Milling Based on the Modular Arrangement of Predetermined Time Standards," Energies, MDPI, vol. 16(20), pages 1-18, October.
    9. Shun Jia & Qingwen Yuan & Wei Cai & Qinghe Yuan & Conghu Liu & Jingxiang Lv & Zhongwei Zhang, 2018. "Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes," Energies, MDPI, vol. 11(8), pages 1-16, August.
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    11. 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.
    12. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    13. Cai, Wei & Liu, Conghu & Zhang, Cuixia & Ma, Minda & Rao, Weizhen & Li, Wenyi & He, Kang & Gao, Mengdi, 2018. "Developing the ecological compensation criterion of industrial solid waste based on emergy for sustainable development," Energy, Elsevier, vol. 157(C), pages 940-948.
    14. Wang, Chunyan & Wang, Ranran & Hertwich, Edgar & Liu, Yi, 2017. "A technology-based analysis of the water-energy-emission nexus of China’s steel industry," Resources, Conservation & Recycling, Elsevier, vol. 124(C), pages 116-128.
    15. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    16. Jia, Shun & Yuan, Qinghe & Lv, Jingxiang & Liu, Ying & Ren, Dawei & Zhang, Zhongwei, 2017. "Therblig-embedded value stream mapping method for lean energy machining," Energy, Elsevier, vol. 138(C), pages 1081-1098.
    17. Yicong Gao & Qirui Wang & Yixiong Feng & Hao Zheng & Bing Zheng & Jianrong Tan, 2018. "An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line," Energies, MDPI, vol. 11(5), pages 1-18, May.
    18. Hu, Luoke & Liu, Ying & Lohse, Niels & Tang, Renzhong & Lv, Jingxiang & Peng, Chen & Evans, Steve, 2017. "Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed," Energy, Elsevier, vol. 139(C), pages 935-946.
    19. 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.
    20. 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.
    21. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.

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