Search Results for author:
Found 4 papers, 3 papers with code
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature.
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data
Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images.
Instance Segmentation
Semantic Segmentation
+1
Materials Science
Image and Video Processing
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance.
Overview: Computer vision and machine learning for microstructural characterization and analysis
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties.
