To leverage advancements in machine learning for metallic materials design and property prediction,it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of curr...To leverage advancements in machine learning for metallic materials design and property prediction,it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors.This need is particularly relevant for metallic materials processed through additive manufacturing,which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials.Furthermore,capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties.To address these challenges,we propose the physical spatial mapping of metal diffraction latent space features.This approach integrates(i)point diffraction data encoding via variational autoencoders or contrastive learning and(ii)the physical mapping of the encoded values.Together,these steps offer a method to comprehensively describe metal microstructures.We demonstrate this approach on a wrought and additively manufactured alloy,showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models.This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.展开更多
基金support from DARPAC.B., D.A., H.P. and J.C.S. acknowledge the NSF (award #2338346) for financial support.This work was carried out in the Materials Research Laboratory Central Research Facilities, University of Illinois. Carpenter Technology is acknowledged for providing the 718 material. Morad Behandish and Adrian Lew are acknowledged for their support and leadership. Tresa Pollock, McLean Echlin and James Lamb are acknowledged for their support on the EBSD sharpness calculations. Marat Latypov, Marie Charpagne, and Florian Strub are gratefully acknowledged for their support and insightful discussions.
文摘To leverage advancements in machine learning for metallic materials design and property prediction,it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors.This need is particularly relevant for metallic materials processed through additive manufacturing,which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials.Furthermore,capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties.To address these challenges,we propose the physical spatial mapping of metal diffraction latent space features.This approach integrates(i)point diffraction data encoding via variational autoencoders or contrastive learning and(ii)the physical mapping of the encoded values.Together,these steps offer a method to comprehensively describe metal microstructures.We demonstrate this approach on a wrought and additively manufactured alloy,showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models.This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.
