Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features
This addresses the problem of inadequate microstructure descriptors for metallic materials design, particularly in additive manufacturing, enabling better property prediction and accelerating material design, though it appears incremental as it builds on existing encoding techniques.
The paper tackled the challenge of representing complex metal microstructures, especially in additively manufactured alloys, by proposing a method that integrates diffraction data encoding and physical mapping to capture spatial heterogeneity, demonstrating it effectively encodes microstructural information and identifies heterogeneity not possible with physics-based models.
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 offers a novel means 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.