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A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Author

Listed:
  • Yuwei Mao

    (Northwestern University)

  • Hui Lin

    (Northwestern University)

  • Christina Xuan Yu

    (Sigma Labs, Inc.)

  • Roger Frye

    (Sigma Labs, Inc.)

  • Darren Beckett

    (Sigma Labs, Inc.)

  • Kevin Anderson

    (Sigma Labs, Inc.)

  • Lars Jacquemetton

    (Sigma Labs, Inc.)

  • Fred Carter

    (Northwestern University
    DMG MORI Advanced Solutions Inc.)

  • Zhangyuan Gao

    (Northwestern University)

  • Wei-keng Liao

    (Northwestern University)

  • Alok N. Choudhary

    (Northwestern University)

  • Kornel Ehmann

    (Northwestern University)

  • Ankit Agrawal

    (Northwestern University)

Abstract

Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

Suggested Citation

  • Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-022-02039-3
    DOI: 10.1007/s10845-022-02039-3
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    References listed on IDEAS

    as
    1. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    2. Vishu Gupta & Kamal Choudhary & Francesca Tavazza & Carelyn Campbell & Wei-keng Liao & Alok Choudhary & Ankit Agrawal, 2021. "Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Dipendra Jha & Kamal Choudhary & Francesca Tavazza & Wei-keng Liao & Alok Choudhary & Carelyn Campbell & Ankit Agrawal, 2019. "Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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