Semantic segmentation of SEM images of lower bainitic and tempered martensitic steels

• URL: http://arxiv.org/abs/2312.17251v1
• Date: Sat, 2 Dec 2023 05:11:34 GMT
• Title: Semantic segmentation of SEM images of lower bainitic and tempered martensitic steels
• Authors: Xiaohan Bie, Manoj Arthanari, Evelin Barbosa de Melo, Juancheng Li, Stephen Yue, Salim Brahimi, Jun Song
• Abstract summary: This study employs deep learning techniques to segment scanning electron microscope images, enabling a quantitative analysis of carbide precipitates in lower bainite and tempered martensite steels with comparable strength. Our findings reveal that lower bainite and tempered martensite exhibit comparable volume percentages of carbides, albeit with a more uniform distribution of carbides in tempered martensite.
• Score: 0.8554538518952843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study employs deep learning techniques to segment scanning electron microscope images, enabling a quantitative analysis of carbide precipitates in lower bainite and tempered martensite steels with comparable strength. Following segmentation, carbides are investigated, and their volume percentage, size distribution, and orientations are probed within the image dataset. Our findings reveal that lower bainite and tempered martensite exhibit comparable volume percentages of carbides, albeit with a more uniform distribution of carbides in tempered martensite. Carbides in lower bainite demonstrate a tendency for better alignment than those in tempered martensite, aligning with the observations of other researchers. However, both microstructures display a scattered carbide orientation, devoid of any discernible pattern. Comparative analysis of aspect ratios and sizes of carbides in lower bainite and tempered martensite unveils striking similarities. The deep learning model achieves an impressive pixelwise accuracy of 98.0% in classifying carbide/iron matrix at the individual pixel level. The semantic segmentation derived from deep learning extends its applicability to the analysis of secondary phases in various materials, offering a time-efficient, versatile AI-powered workflow for quantitative microstructure analysis.

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