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A Novel Collaborative Method to Integrate Carbon Efficiency into Multi-Equipment Operational Coupling for Smart Manufacturing System

Author

Listed:
  • Lijun Liu

    (College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Huisong Meng

    (College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Wei Yang

    (College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Xiaoyu Wang

    (College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Yuxuan Li

    (College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China)

  • Xinyu Li

    (College of Mechanical Engineering, Donghua University, Shanghai 201620, China)

Abstract

In the context of carbon neutrality and smart manufacturing, balancing the challenge of carbon and operational efficiency has become a hotspot issue. However, within the specific stage of multi-equipment collaborative manufacturing operational coupling in the production process, multi-state characteristics of equipment operation, multidependencies among operational states, the multi-source of carbon emissions, and spatiotemporal sequence coupling raise the dynamics and complexity of carbon emission modeling and carbon efficiency evaluation. Therefore, a novel methodology to integrate carbon efficiency into a multi-equipment collaboration manufacturing service cell (MECMfg-SC) is proposed in this paper. The stage of multi-equipment collaboration manufacturing operational coupling (MECMfg-OC) in the process of multi-equipment collaboration manufacturing is presented and explained. Then, the operational coupling energy consumption model is constructed based on the MECMfg-OC. The environmental cost performance indicators for smart manufacturing systems, including energy efficiency evaluation (EEe) indicators and carbon efficiency evaluation (CEe) indicators, are proposed. At last, a ball screw smart workshop in a leading Chinese NEV enterprise is introduced to verify the proposed approach. Empirical results confirm the approach’s effectiveness and practical viability.

Suggested Citation

  • Lijun Liu & Huisong Meng & Wei Yang & Xiaoyu Wang & Yuxuan Li & Xinyu Li, 2025. "A Novel Collaborative Method to Integrate Carbon Efficiency into Multi-Equipment Operational Coupling for Smart Manufacturing System," Sustainability, MDPI, vol. 17(18), pages 1-34, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8390-:d:1752856
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    References listed on IDEAS

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    1. Wenbin Gu & Zhuo Li & Zeyu Chen & Yuxin Li, 2020. "An energy-consumption model for establishing an integrated energy-consumption process in a machining system," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 26(6), pages 534-561, November.
    2. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    3. Lishu Lv & Zhaohui Deng & Can Yan & Tao Liu & Linlin Wan & Qianwei Gu, 2020. "Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7078-7093, December.
    4. Manal Alghieth, 2025. "Sustain AI: A Multi-Modal Deep Learning Framework for Carbon Footprint Reduction in Industrial Manufacturing," Sustainability, MDPI, vol. 17(9), pages 1-29, May.
    5. Stanley B. Gershwin, 2018. "The future of manufacturing systems engineering," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 224-237, January.
    6. A. Hafezalkotob & S. Arisian & Raziyeh Reza-Gharehbagh & L. Nersesian, 2023. "Joint Impact of CSR Policy and Market Structure on Environmental Sustainability in Supply Chains," Post-Print hal-04435514, HAL.
    7. Lijun Liu & Zhixin Long & Chuangchuang Kou & Haozeng Guo & Xinyu Li, 2023. "Evaluation of the Environmental Cost of Integrated Inbound Logistics: A Case Study of a Gigafactory of a Chinese Logistics Firm," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
    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. Gong, Qiguo & Chen, Guohui & Zhang, Wen & Wang, Hui, 2022. "The role of humans in flexible smart factories," International Journal of Production Economics, Elsevier, vol. 254(C).
    10. He, Yihai & Zhao, Yixiao & Han, Xiao & Zhou, Di & Wang, Wenzhuo, 2020. "Functional risk-oriented health prognosis approach for intelligent manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Abdulmalek, Fawaz A. & Rajgopal, Jayant, 2007. "Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study," International Journal of Production Economics, Elsevier, vol. 107(1), pages 223-236, May.
    12. Pan, Wei & Li, Kaijian & Teng, Yue, 2018. "Rethinking system boundaries of the life cycle carbon emissions of buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 379-390.
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