High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel

Published

February 1, 2019

Author(s)

Brian DeCost, Toby Francis, Elizabeth A. Holm

Abstract

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 [1, 2]: 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 [3].

Citation

Microscopy and Microanalysis

Pub Type

Journals

Download Paper

https://doi.org/10.1017/s1431927618015635

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Keywords

microsctructure, segmentation, deep learning, SEM, steel

Citation

DeCost, B. , Francis, T. and Holm, E. (2019), High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel, Microscopy and Microanalysis, [online], https://doi.org/10.1017/s1431927618015635, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925942 (Accessed December 7, 2025)