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Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing

Author

Listed:
  • T. Herzog

    (RMIT University
    CSIRO Manufacturing Business Unit)

  • M. Brandt

    (RMIT University)

  • A. Trinchi

    (CSIRO Manufacturing Business Unit)

  • A. Sola

    (CSIRO Manufacturing Business Unit)

  • A. Molotnikov

    (RMIT University)

Abstract

Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts on-demand, repair damaged components, and provide an increased freedom of design not previously attainable by traditional manufacturing techniques. The increasing maturation of metal AM is attracting high-value industries to directly produce components for use in aerospace, automotive, biomedical, and energy fields. Two leading processes for metal part production are Powder Bed Fusion with laser beam (PBF-LB/M) and Directed Energy Deposition with laser beam (DED-LB/M). Despite the many advances made with these technologies, the highly dynamic nature of the process frequently results in the formation of defects. These technologies are also notoriously difficult to control, and the existing machines do not offer closed loop control. In the present work, the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed. The potential of these methods for enabling process control implementation is discussed. We provide a critical review of trends in the usage of data structures and ML algorithms and compare the capabilities of different sensing technologies and their application to monitoring tasks in laser metal AM. The future direction of this field is then discussed, and recommendations for further research are provided. Graphical abstract

Suggested Citation

  • T. Herzog & M. Brandt & A. Trinchi & A. Sola & A. Molotnikov, 2024. "Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1407-1437, April.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02119-y
    DOI: 10.1007/s10845-023-02119-y
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    References listed on IDEAS

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    1. Oktay Günlük & Jayant Kalagnanam & Minhan Li & Matt Menickelly & Katya Scheinberg, 2021. "Optimal decision trees for categorical data via integer programming," Journal of Global Optimization, Springer, vol. 81(1), pages 233-260, September.
    2. Mojtaba Khanzadeh & Sudipta Chowdhury & Mark A. Tschopp & Haley R. Doude & Mohammad Marufuzzaman & Linkan Bian, 2019. "In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes," IISE Transactions, Taylor & Francis Journals, vol. 51(5), pages 437-455, May.
    3. Mohammad Montazeri & Abdalla R. Nassar & Alexander J. Dunbar & Prahalada Rao, 2020. "In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy," IISE Transactions, Taylor & Francis Journals, vol. 52(5), pages 500-515, May.
    4. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    5. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    6. Chu Lun Alex Leung & Sebastian Marussi & Robert C. Atwood & Michael Towrie & Philip J. Withers & Peter D. Lee, 2018. "In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    7. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    8. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
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