期刊
MICROSCOPY AND MICROANALYSIS
卷 25, 期 1, 页码 21-29出版社
OXFORD UNIV PRESS
DOI: 10.1017/S1431927618015635
关键词
deep learning; microstructure; segmentation; steel
资金
- National Science Foundation [DMR-1507830, CMMI-1826218]
- John and Claire Bertucci Foundation
- National Institute of Standards and Technology
- National Research Council Research Associate Program
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstatten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.
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