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

Thinh Quy Duc Pham, Truong Vinh Hoang, Xuan Tran (), Quoc Tuan Pham, Seifallah Fetni, Laurent Duchêne, Hoang Son Tran and Anne-Marie Habraken
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Thinh Quy Duc Pham: Thu Dau Mot University
Truong Vinh Hoang: RWTH-Aachen University
Xuan Tran: Thu Dau Mot University
Quoc Tuan Pham: 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

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 4, No 10, 1719 pages

Abstract: 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.

Keywords: Deep learning; Directed energy deposition; Temperature evolutions; Sensitivity analysis; SHAP method (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01896-8

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