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Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Author

Listed:
  • Thinh Quy Duc Pham

    (Thu Dau Mot University
    University of Liège)

  • Truong Vinh Hoang

    (RWTH-Aachen University)

  • Xuan Tran

    (Thu Dau Mot University)

  • Quoc Tuan Pham

    (Ton Duc Thang University
    Ton Duc Thang University)

  • Seifallah Fetni

    (University of Liège)

  • Laurent Duchêne

    (University of Liège)

  • Hoang Son Tran

    (University of Liège)

  • Anne-Marie Habraken

    (University of Liège
    Fonds de la Recherche Scientifique de Belgique (F.R.S-FNRS))

Abstract

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of $$99\%$$ 99 % and $$98\%$$ 98 % , respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.

Suggested Citation

  • Thinh Quy Duc Pham & Truong Vinh Hoang & Xuan Tran & Quoc Tuan Pham & Seifallah Fetni & Laurent Duchêne & Hoang Son Tran & Anne-Marie Habraken, 2023. "Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1701-1719, April.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01896-8
    DOI: 10.1007/s10845-021-01896-8
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    References listed on IDEAS

    as
    1. Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
    2. Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
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