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Detecting voids in 3D printing using melt pool time series data

Author

Listed:
  • Vivek Mahato

    (University College Dublin)

  • Muhannad Ahmed Obeidi

    (Dublin City University)

  • Dermot Brabazon

    (Dublin City University)

  • Pádraig Cunningham

    (University College Dublin)

Abstract

Powder Bed Fusion (PBF) has emerged as an important process in the additive manufacture of metals. However, PBF is sensitive to process parameters and careful management is required to ensure the high quality of parts produced. In PBF, a laser or electron beam is used to fuse powder to the part. It is recognised that the temperature of the melt pool is an important signal representing the health of the process. In this paper, Machine Learning (ML) methods on time-series data are used to monitor melt pool temperature to detect anomalies. In line with other ML research on time-series classification, Dynamic Time Warping and k-Nearest Neighbour classifiers are used. The presented process is effective in detecting voids in PBF. A strategy is then proposed to speed up classification time, an important consideration given the volume of data involved.

Suggested Citation

  • Vivek Mahato & Muhannad Ahmed Obeidi & Dermot Brabazon & Pádraig Cunningham, 2022. "Detecting voids in 3D printing using melt pool time series data," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 845-852, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01694-8
    DOI: 10.1007/s10845-020-01694-8
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    2. Mahesh Mani & Brandon M. Lane & M. Alkan Donmez & Shaw C. Feng & Shawn P. Moylan, 2017. "A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1400-1418, March.
    3. 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.
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    Cited by:

    1. Thanh Q. Nguyen & Nghi N. Nguyen & Xuan Tran, 2024. "Power spectral density moment of having defective 3D printed plastic beams under moving load based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1491-1515, April.
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