Deep learing; Directed energy deposition; Temperature evolutions; Sensitivity analysis; SHAP method
Abstract :
[en] 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.
Disciplines :
Materials science & engineering
Author, co-author :
Pham Quy Duc, Thinh ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Hoang, Truong Vinh; RWTH-Aachen University
Van Tran, Xuan; Thu Dau Mot University
Tuan Pham, Quoc; Ton Duc Thang University
Fetni, Seifallah; univerité de Liège
Duchene, Laurent ; Université de Liège - ULiège > Département ArGEnCo > Analyse multi-échelles des matériaux et struct. du gén. civ.
Tran, Hoang Son ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
Habraken, Anne ; Université de Liège - ULiège > Département ArGEnCo > Département ArGEnCo
Language :
English
Title :
Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning
Publication date :
07 January 2022
Journal title :
Journal of Intelligent Manufacturing
ISSN :
0956-5515
eISSN :
1572-8145
Publisher :
Springer Nature
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
VINIF.2020.DA15 EDPOMP project
Funders :
Vingroup and supported by Vingroup Innovation Foundation (VINIF)
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