Vu, Viet-Hung, Chaudhry, Shubham et Shahzadi, Gullnaz.
2025.
« Modeling the process-microstructure-mechanical properties in laser powder bed fusion: Review of data-driven approaches and machine learning strategy for Ti-6Al-4V ».
In Proceedings of the CSME-CFDSC-CSR 2025 International Congress (Montreal, QC, Canada, May 25-28, 2025)
Coll. « Progress in Canadian Mechanical Engineering », vol. 8.
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Résumé
This paper reviews the modeling of the Process- Microstructure-Mechanical property in the laser powder bed fusion, focusing on the data-driven approaches, from which a strategy modeling by machine learning algorithms is established for the Ti-6Al-4V. A sensitivity and uncertainty analysis strategy is studied considering the variation of the process parameters to the microstructure features and the mechanical properties. A deep neural network (DNN) is introduced to construct a surrogate model between input parameters and microstructure features. The proposed strategy aims to deliver an efficient and cost-effective prediction of both Microstructure features and the mechanical properties of the laser powder bed fusion Ti-6al-4v. The proposed methodology achieves high predictive accuracy, as evidenced by strong correlations between predicted and actual values. Additionally, it identifies critical process parameters, such as the length of the melt pool tail, that play a significant role in determining the microstructure size. This research provides a cost-effective approach to enhancing process optimization and quality control within the realm of additive manufacturing for metals.
| Type de document: | Compte rendu de conférence |
|---|---|
| Éditeurs: | Éditeurs ORCID Hof, Lucas A. NON SPÉCIFIÉ Di Labbio, Giuseppe NON SPÉCIFIÉ Tahan, Antoine NON SPÉCIFIÉ Sanjosé, Marlène NON SPÉCIFIÉ Lalonde, Sébastien NON SPÉCIFIÉ Demarquette, Nicole R. NON SPÉCIFIÉ |
| Date de dépôt: | 18 déc. 2025 15:33 |
| Dernière modification: | 18 déc. 2025 15:33 |
| URI: | https://espace2.etsmtl.ca/id/eprint/32521 |
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