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

Pham, Thinh Quy Duc ; Hoang, Truong Vinh ; Van Tran, Xuan ; Pham, Quoc Tuan ; Fetni, Seifallah ; Duchêne, Laurent ; Tran, Hoang Son ; Habraken, Anne-Marie

Journal of intelligent manufacturing, 2023-04, Vol.34 (4), p.1701-1719 [Periódico revisado por pares]

New York: Springer US

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Título:
Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning
Autor: Pham, Thinh Quy Duc ; Hoang, Truong Vinh ; Van Tran, Xuan ; Pham, Quoc Tuan ; Fetni, Seifallah ; Duchêne, Laurent ; Tran, Hoang Son ; Habraken, Anne-Marie
Assuntos: Accuracy ; Additive manufacturing ; Artificial neural networks ; Bayesian analysis ; Business and Management ; Computational mathematics ; Control ; Deep learing ; Deep learning ; Directed energy deposition ; Engineering, computing & technology ; Finite element method ; Heat ; Impact prediction ; Ingénierie, informatique & technologie ; Lasers ; Machines ; Manufacturing ; Materials science & engineering ; Mechatronics ; Model accuracy ; Neural networks ; Optimization ; Parameter uncertainty ; Printing ; Processes ; Production ; Robotics ; Science des matériaux & ingénierie ; Sensitivity analysis ; SHAP method ; Temperature evolutions
É parte de: Journal of intelligent manufacturing, 2023-04, Vol.34 (4), p.1701-1719
Notas: VINIF.2020.DA15 EDPOMP project
scopus-id:2-s2.0-85122668963
Descrição: 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.
Editor: New York: Springer US
Idioma: Inglês

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

Pham, Thinh Quy Duc ; Hoang, Truong Vinh ; Van Tran, Xuan ; Pham, Quoc Tuan ; Fetni, Seifallah ; Duchêne, Laurent ; Tran, Hoang Son ; Habraken, Anne-Marie

New York: Springer US

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