High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel. (14th March 2019)

• Record Type: Journal Article
• Authors: DeCost, Brian L.; Lei, Bo; Francis, Toby; Holm, Elizabeth A.
• Is Part Of: Microscopy and microanalysis. Volume 25:Number 1(2019)
• Page Start: 21
• Page End: 29
• Publication Date: 2019-03-14
• Subjects: deep learning, -- microstructure, -- segmentation, -- steel; Microscopy -- Periodicals; Microchemistry -- Periodicals; 502.82
• DOI: 10.1017/S1431927618015635
• Languages: English
• ISSNs: 1431-9276

Abstract

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: 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 Widmanstätten 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.