Article (Scientific journals)
Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting
Le-Hong, Thai; Lin, Pai Chen; Chen, Jian-Zhong et al.
2021In Journal of Intelligent Manufacturing, 34 (3), p. 1241 - 1257
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Keywords :
Artificial neural network; Bayesian optimization; Bead geometry; Machine learning; Selective melting laser; Single-track morphology; Combination of lasers; Laser power; Laser scanning; Machine-learning; Scanning speed; Selective laser melting; Single-tracks; Software; Industrial and Manufacturing Engineering; Artificial Intelligence
Abstract :
[en] In this paper, the effects of two key process parameters of the selective laser melting process, namely laser power and scanning speed, on the single-track morphologies and the bead characteristics, especially the depth-to-width D/W and height-to-width H/W ratios, were investigated using both experimental and Machine Learning (ML) approaches. A total of 840 single tracks were fabricated with several combinations of laser power and scanning speed levels. Surface morphologies of the single tracks and bead profiles were thoroughly investigated, providing a track-type map and the evolutions of the bead characteristics as a function of laser power and scanning speed. The results indicate neither severe balling nor keyholing effect for all combinations of laser power and scanning speed. Besides, simple relationships cannot accurately describe the evolutions of the D/W and H/W ratios as a function of laser power and scanning speed. Two Machine Learning-based regression models, Random Forest and Artificial Neural Network, were chosen to estimate the D/W and H/W ratios using laser power and scanning speed. The Bayesian optimization algorithm was employed to optimize the model hyperparameter selection. Both Machine Learning-based models appear to be able to predict reasonably well the two aspect ratios, D/W and H/W, with an overall R2 value reaching about 90%, evaluated on the cross-validation dataset after a few seconds of training time, respectively.
Disciplines :
Mechanical engineering
Author, co-author :
Le-Hong, Thai;  Institute of Development Strategies, Thu Dau Mot University, Thu Dau Mot, Viet Nam ; IMSIA, CNRS, EDF, CEA, ENSTA Paris, Institut Polytechnique de Paris, Palaiseau, France
Lin, Pai Chen;  Advanced Institute of Manufacturing with High-Tech Innovations, National Chung-Cheng University, Chia-Yi, Taiwan ; Department of Mechanical Engineering, National Chung-Cheng University, Chia-Yi, Taiwan
Chen, Jian-Zhong;  Advanced Institute of Manufacturing with High-Tech Innovations, National Chung-Cheng University, Chia-Yi, Taiwan ; Department of Mechanical Engineering, National Chung-Cheng University, Chia-Yi, Taiwan
Pham Quy Duc, Thinh ;  Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Van Tran, Xuan ;  Institute of Development Strategies, Thu Dau Mot University, Thu Dau Mot, Viet Nam
Language :
English
Title :
Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting
Publication date :
2021
Journal title :
Journal of Intelligent Manufacturing
ISSN :
0956-5515
eISSN :
1572-8145
Publisher :
Springer
Volume :
34
Issue :
3
Pages :
1241 - 1257
Peer reviewed :
Peer Reviewed verified by ORBi
Funding text :
This work was funded by Vingroup and supported by Vingroup Innovation Foundation (VINIF) under Project code VINIF.2020.DA15.
Available on ORBi :
since 11 January 2024

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