  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.
