Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features

• URL: http://arxiv.org/abs/2501.18064v1
• Date: Thu, 30 Jan 2025 00:16:07 GMT
• Title: Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features
• Authors: Mathieu Calvat, Chris Bean, Dhruv Anjaria, Hyoungryul Park, Haoren Wang, Kenneth Vecchio, J. C. Stinville,
• Abstract summary: It is crucial to develop a data-reduced representation of metal microstructures. This need is particularly relevant for metallic materials processed through additive manufacturing. We propose the physical spatial mapping of metal diffraction latent space features.
• Score: 0.2692359362045324
• License:
• Abstract: 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.

Related papers

• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design [19.10106845551149]
  This study introduces an improved alloy design algorithm that integrates authentic microstructural information to establish precise CPSP relationships. The approach utilizes a deep-learning framework based on a variational autoencoder to map real microstructural data to a latent space.
  arXiv  Detail & Related papers  (2024-09-04T12:26:19Z)
• Compositional Structures in Neural Embedding and Interaction Decompositions [101.40245125955306]
  We describe a basic correspondence between linear algebraic structures within vector embeddings in artificial neural networks. We introduce a characterization of compositional structures in terms of "interaction decompositions" We establish necessary and sufficient conditions for the presence of such structures within the representations of a model.
  arXiv  Detail & Related papers  (2024-07-12T02:39:50Z)
• Revealing the structure-property relationships of copper alloys with FAGC [7.00651980770986]
  We introduce a method known as FAGC (Feature Augmentation on Geodesic Curves), specifically demonstrated for Cu-Cr-Zr alloys. This approach utilizes machine learning to examine the shapes within images of the alloys' microstructures and predict their mechanical and electronic properties. Our FAGC method has shown remarkable results, significantly improving the accuracy of predicting the electronic conductivity and hardness of Cu-Cr-Zr alloys.
  arXiv  Detail & Related papers  (2024-04-15T07:20:09Z)
• Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response [1.283555556182245]
  Composite materials with different microstructural material symmetries are common in engineering applications. We provide neural network architectures that provide effective homogenization models of materials with anisotropic components.
  arXiv  Detail & Related papers  (2024-04-05T14:49:01Z)
• Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators [2.4918888803900727]
  Machine learning is emerging as a transformative tool for the design of architected materials. Here, we introduce a new end-to-end scientific ML framework, leveraging deep neural operators (DeepONet) Results obtained from spinodal microstructures, printed using two-photon lithography, reveal that the prediction error for mechanical responses is within a range of 5 - 10%.
  arXiv  Detail & Related papers  (2023-11-23T05:23:15Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics [77.34726150561087]
  This work proposes a generative adversarial network tailored towards three-dimensional microstructure generation. The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
  arXiv  Detail & Related papers  (2022-05-31T13:26:51Z)
• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning [82.67551367327634]
  We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
  arXiv  Detail & Related papers  (2021-11-10T21:19:02Z)
• Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems [20.659457956055366]
  We propose a novel data-driven metamaterial design framework based on deep generative modeling. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems.
  arXiv  Detail & Related papers  (2020-06-27T03:56:55Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.