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

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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 \(99\%\) and \(98\%\), 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.

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Acknowledgements

This work was funded by Vingroup and supported by Vingroup Innovation Foundation (VINIF) under project code VINIF.2020.DA15. Also, the author would like to thank Dr. Van-Dung Nguyen for his helpful advice on various technical issues examined in this paper.

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Authors and Affiliations

  1. Institute of Strategy Development, Thu Dau Mot University, 75100, Phú Hoà, Binh Duong Province, Vietnam

    Thinh Quy Duc Pham & Xuan Van Tran

  2. Chair of Mathematics for Uncertainty Quantification, RWTH-Aachen University, 52056, Aachen, Germany

    Truong Vinh Hoang

  3. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, 70000, Ho Chi Minh City, Vietnam

    Quoc Tuan Pham

  4. Faculty of Civil Engineering, Ton Duc Thang University, 70000, Ho Chi Minh City, Vietnam

    Quoc Tuan Pham

  5. University of Liège, MSM Unit, Allée de la Découverte, 9 B52/3, B 4000, Liège, Belgium

    Thinh Quy Duc Pham, Seifallah Fetni, Laurent Duchêne, Hoang Son Tran & Anne-Marie Habraken

  6. Fonds de la Recherche Scientifique de Belgique (F.R.S-FNRS), Brussels, Belgium

    Anne-Marie Habraken

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  1. Thinh Quy Duc Pham
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Pham, T.Q.D., Hoang, T.V., Van Tran, X. et al. Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning. J Intell Manuf 34, 1701–1719 (2023). https://doi.org/10.1007/s10845-021-01896-8

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