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A data-driven approach for predicting printability in metal additive manufacturing processes

Author

Listed:
  • William Mycroft

    (University of Sheffield)

  • Mordechai Katzman

    (University of Sheffield)

  • Samuel Tammas-Williams

    (University of Sheffield
    Liverpool John Moores University)

  • Everth Hernandez-Nava

    (University of Sheffield)

  • George Panoutsos

    (University of Sheffield)

  • Iain Todd

    (University of Sheffield)

  • Visakan Kadirkamanathan

    (University of Sheffield)

Abstract

Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process.

Suggested Citation

  • William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-020-01541-w
    DOI: 10.1007/s10845-020-01541-w
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    References listed on IDEAS

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    1. J. Gower, 1975. "Generalized procrustes analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 33-51, March.
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    Cited by:

    1. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    2. Brylowski, Martin & Schwieger, Lea-Sophie & Nagi, Ayman & Kersten, Wolfgang, 2021. "How to apply artificial intelligence in the additive value chain: A systematic literature review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 65-100, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Jia Liu & Jiafeng Ye & Daniel Silva Izquierdo & Aleksandr Vinel & Nima Shamsaei & Shuai Shao, 2023. "A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3249-3275, December.
    4. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
    5. Hyunseop Park & Hyunwoong Ko & Yung-tsun Tina Lee & Shaw Feng & Paul Witherell & Hyunbo Cho, 2023. "Collaborative knowledge management to identify data analytics opportunities in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 541-564, February.
    6. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    7. Huaxian Wei & Bijan Shirinzadeh & Xiaodong Niu & Jian Zhang & Wei Li & Alessandro Simeone, 2021. "Study of the hinge thickness deviation for a 316L parallelogram flexure mechanism fabricated via selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1411-1420, June.
    8. Filippo Simoni & Andrea Huxol & Franz-Josef Villmer, 2021. "Improving surface quality in selective laser melting based tool making," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1927-1938, October.

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