Combining phase field modeling and deep learning for accurate modeling of grain structure in solidification
Résumé
Additive manufacturing by wire deposition is a complex process as it generates overlapping and transient thermal fields, resulting in multiple cycles of solidification and remelting. Consequently, simulating the microstructure becomes challenging, making it difficult to optimize the process and predict the mechanical properties of an additive-manufactured part. In this work, a framework is developed, including several sequentially applied methods to obtain increasingly accurate information about the microstructure resulting from a wire deposition process. This framework is based on understanding experimental data and utilizing them as inputs to simplify the problem or accelerate numerical simulations. The experimental inputs consist of light optical images, SEM, and EBSD images. The methods developed here are initially geometric-based and then progress to shape recognition, unsupervised learning, and phase field modeling.
Mots clés
316LSi Cold Metal Transfer (CMT) microstructure optical and SEM observations artificial intelligence-based image segmentation & phase field approach
316LSi
Cold Metal Transfer (CMT)
microstructure
optical and SEM observations
artificial intelligence-based image segmentation & phase field approach
Domaines
Sciences de l'ingénieur [physics]Origine | Fichiers produits par l'(les) auteur(s) |
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