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This study presents a novel approach to map metal microstructural heterogeneity using machine learning techniques, specifically through the encoding of Kikuchi patterns into a latent space. The proposed method enhances the understanding of complex microstructures in both wrought and additively manufactured alloys, allowing for the identification of critical heterogeneities that traditional physics-based models may overlook. By integrating variational autoencoders and contrastive learning, this approach aims

Author

Gia Huy

Language

EN