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A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State

Author

Listed:
  • Zhiqiang Yan

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

  • Jian Huang

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

  • Jingxiang Lv

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

  • Jizhuang Hui

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

  • Ying Liu

    (Department of Mechanical Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Hao Zhang

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

  • Enhuai Yin

    (Xi’an Research Institute of Navigation Technology, China Electronics Technology Group Corporation, Xi’an 710068, China)

  • Qingtao Liu

    (School of Construction Machinery, Chang’an University, Xi’an 710000, China)

Abstract

With the increase in environmental awareness, coupled with an emphasis on environmental policy, achieving sustainable manufacturing is increasingly important. Additive manufacturing (AM) is an attractive technology for achieving sustainable manufacturing. However, with the diversity of AM types and various working states of machines’ components, a general method to forecast the energy consumption of AM is lacking. This paper proposes a new model considering the power of each component, the time of each process and the working state of each component to predict the energy consumption. Fused deposition modeling, which is a typical AM process, was selected to demonstrate the effectiveness of the proposed model. It was found that the proposed model had a higher prediction accuracy compared to the specific energy model and the process-based energy consumption model. The proposed model could be easily integrated into the software to visualize the printing time and energy consumption of each process in each component, and, further, provide a reference for coordinating the optimization of parts’ quality and energy consumption.

Suggested Citation

  • Zhiqiang Yan & Jian Huang & Jingxiang Lv & Jizhuang Hui & Ying Liu & Hao Zhang & Enhuai Yin & Qingtao Liu, 2022. "A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3757-:d:777127
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    References listed on IDEAS

    as
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