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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Practical Tools for Integration and Analysis in Materials Engineering
Presentation Title Tools for microstructural analysis using computer vision and machine learning
Author(s) Elizabeth A. Holm, Bo Lei, Andrew Kitahara, Nan Gao, Ryan Cohn
On-Site Speaker (Planned) Elizabeth A. Holm
Abstract Scope Microstructural science relies on quantitative characterization and analysis of microstructure, typically based on images obtained from microscopy, diffraction, or other experimental modalities. Recent progress in data science, including computer vision (CV) and machine learning (ML), offer new approaches to extracting information from microstructural images. However, because they are generally developed to analyze natural images, and because they are not part of the materials curriculum, applying these methods to materials problems is not always straightforward. This talk presents the basic steps for encoding and analyzing microstructural image data: image preprocessing and data augmentation; feature vector construction; and unsupervised and supervised machine learning. A Python code tutorial that applies these operations on an open access data set of steel defects is included. Case studies demonstrate practical aspects of developing CV/ML workflows, including dataset considerations, hyperparameter selection, ML technique, and potential pitfalls.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, Computational Materials Science & Engineering


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Tools for microstructural analysis using computer vision and machine learning

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