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Metallurgical and Materials Transactions A
Erschienen in: Metallurgical and Materials Transactions A 12/2020

29.09.2020 | Metallurgical and Materials Transactions 50th Anniversary Collection

Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis

verfasst von: Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, Srujana Rao Yarasi

Erschienen in: Metallurgical and Materials Transactions A | Ausgabe 12/2020

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Abstract

Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.
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Metadaten
Titel
Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
verfasst von
Elizabeth A. Holm
Ryan Cohn
Nan Gao
Andrew R. Kitahara
Thomas P. Matson
Bo Lei
Srujana Rao Yarasi
Publikationsdatum
29.09.2020
Verlag
Springer US
Erschienen in
Metallurgical and Materials Transactions A / Ausgabe 12/2020
Print ISSN: 1073-5623
Elektronische ISSN: 1543-1940
DOI
https://doi.org/10.1007/s11661-020-06008-4

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