Machine Learning; Artificial Neural Network; Direct Energy Deposition,; Thermal Model
Abstract :
[en] In the last decade, machine learning is increasingly attracting researchers in several scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this technique remains as a black box technique for many researchers. Indeed, it allows obtaining novel insights to overcome the limitation of classical methods, such as the finite element method, and to take into account multi-physical complex phenomena occurring during the manufacturing process. This work presents a comprehensive study for implementing a machine learning technique (artificial neural network) to predict the thermal field evolution during the direct energy deposition of 316L stainless steel and tungsten carbides. The framework consists of a finite element thermal model and a neural network. The influence of the number of hidden layers and the number of nodes in each layer was also investigated. The results showed that an architecture based on 3 or 4 hidden layers and the rectified linear unit as the activation function lead to obtaining a high fidelity prediction with an accuracy exceeding 99%. The impact of the chosen architecture on the model accuracy and CPU usage was also highlighted. The proposed framework can be used to predict the thermal field when simulating multi-layer deposition
Research Center/Unit :
University of Liège, UEE Research Unit, MSM division
Disciplines :
Materials science & engineering
Author, co-author :
El Fetni, Seifallah ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F
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 :
Thermal field prediction in DED manufacturing process using Artificial Neural Network
Publication date :
12 April 2021
Event name :
ESAFORM 2021 24th International Conference on Material Forming
Event organizer :
ESAFORM
Event place :
Liège, Belgium
Event date :
from 13-04-2021 to 17-04-2021
Audience :
International
Main work title :
ESAFORM 2021: The 24th International ESAFORM Conference on Material Forming
Author, co-author :
Habraken, Anne ; Université de Liège - ULiège > Urban and Environmental Engineering
Duchene, Laurent ; Université de Liège - ULiège > Urban and Environmental Engineering
Mertens, Anne ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Metallic materials for additive manufacturing
Publisher :
PoPuPs, Liège, Belgium
ISBN/EAN :
9782870190029
Peer reviewed :
Peer reviewed
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
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