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A modeling method for hybrid energy behaviors in flexible machining systems

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  • Li, Yufeng
  • He, Yan
  • Wang, Yan
  • Wang, Yulin
  • Yan, Ping
  • Lin, Shenlong

Abstract

Increasingly environmental and economic pressures have led to great concerns regarding the energy consumption of machining systems. Understanding energy behaviors of flexible machining systems is a prerequisite for improving energy efficiency of these systems. This paper proposes a modeling method to predict energy behaviors in flexible machining systems. The hybrid energy behaviors not only depend on the technical specification related of machine tools and workpieces, but are significantly affected by individual production scenarios. In the method, hybrid energy behaviors are decomposed into Structure-related energy behaviors, State-related energy behaviors, Process-related energy behaviors and Assignment-related energy behaviors. The modeling method for the hybrid energy behaviors is proposed based on Colored Timed Object-oriented Petri Net (CTOPN). The former two types of energy behaviors are modeled by constructing the structure of CTOPN, whist the latter two types of behaviors are simulated by applying colored tokens and associated attributes. Machining on two workpieces in the experimental workshop were undertaken to verify the proposed modeling method. The results showed that the method can provide multi-perspective transparency on energy consumption related to machine tools, workpieces as well as production management, and is particularly suitable for flexible manufacturing system when frequent changes in machining systems are often encountered.

Suggested Citation

  • Li, Yufeng & He, Yan & Wang, Yan & Wang, Yulin & Yan, Ping & Lin, Shenlong, 2015. "A modeling method for hybrid energy behaviors in flexible machining systems," Energy, Elsevier, vol. 86(C), pages 164-174.
    • Handle: RePEc:eee:energy:v:86:y:2015:i:c:p:164-174
    DOI: 10.1016/j.energy.2015.03.121
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Liping & Tang, Qiuhua & Wu, Zhengjia & Wang, Fang, 2017. "Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops," Energy, Elsevier, vol. 138(C), pages 210-227.
    2. 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.
    3. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
    4. 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.
    5. Junfeng Wang & Zicheng Fei & Qing Chang & Shiqi Li, 2019. "Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net," Energies, MDPI, vol. 12(11), pages 1-17, June.
    6. 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.
    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. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2021. "A multi-perspective analysis of sustainability of machining processes based on a new extended virtual manufacturing framework," Energy, Elsevier, vol. 225(C).
    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.
    11. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    12. 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.
    13. Vansh Vyas & Hyun-woo Jeon & Chao Wang, 2021. "An Integrated Energy Simulation Model of a Compressed Air System for Sustainable Manufacturing: A Time-Discretized Approach," Sustainability, MDPI, vol. 13(18), pages 1-28, September.

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