5 papers
Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features
Mathieu Calvat, Gregory Sparks, Dhruv Anjaria +9
Many material systems exhibit complex spatial and temporal interactions across multiple length scales and modalities that govern macroscopic behavior. Although Machine Learning (ML…
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features
Mathieu Calvat, Chris Bean, Dhruv Anjaria +4
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…
Dynamic Plastic Deformation Delocalization in FCC Solid Solution Metals
Dhruv Anjaria, Milan Heczko, Daegun YoU +7
Metallic materials undergo irreversible deformation under mechanical loading, leading to intense local plastic localization that reduces their mechanical performance. We identify a…
Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction
Mathieu Calvat, Chris Bean, Dhruv Anjaria +3
Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of m…
Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification
C. Bean, M. Calvat, Y. Nie +5
Recent improvements in additive manufacturing and high-throughput material synthesis have enabled the discovery of novel metallic materials for extreme environments. However, high-…