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Energy consumption model of Binder-jetting additive manufacturing processes

Author

Listed:
  • Xin Xu
  • Simon Meteyer
  • Nicolas Perry
  • Yaoyao Fiona Zhao

Abstract

Considering the potential for new product design possibilities and the reduction of environmental impacts, Additive Manufacturing (AM) processes are considered to possess significant advantages for automotive, aerospace and medical equipment industries. One of the commercial AM techniques is Binder-jetting (BJ). This technique can be used to process a variety of materials including stainless steel, ceramic, polymer and glass. However, there is very limited research about this AM technology on energy consumption aspect. This paper presents a method to build an energy consumption model for printing stage of BJ process. Mathematical analyses are performed to find out the correlation between the energy consumption and geometry of the manufactured part. Based on the analyses, total energy consumption is calculated as a function of part geometry and printing parameters. Finally, test printing is performed to check the accuracy of the model. This process model provides a tool to optimise part geometry design with respect to energy consumption.

Suggested Citation

  • Xin Xu & Simon Meteyer & Nicolas Perry & Yaoyao Fiona Zhao, 2015. "Energy consumption model of Binder-jetting additive manufacturing processes," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7005-7015, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7005-7015
    DOI: 10.1080/00207543.2014.937013
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    References listed on IDEAS

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    1. S.H. Choi & H.H. Cheung, 2012. "Virtual Prototyping for Rapid Product Development," Chapters, in: Catalin Alexandru (ed.), Modeling and Simulation in Engineering, IntechOpen.
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    Cited by:

    1. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Gao, Mengdi & Li, Lei & Wang, Qingyang & Liu, Conghu & Li, Xinyu & Liu, Zhifeng, 2024. "Feature-based energy consumption quantitation strategy for complex additive manufacturing parts," Energy, Elsevier, vol. 297(C).

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