Quantitative metallographic analysis is significant in predicting the mechanical and physical properties of materials. This paper presents an alternate method to the approach used by Zhao, et al. (2016) in the paper “Metallographic Quantitative Analysis for GCr15 by Digital Image Process” in identifying carbide particles present within GCr15 bearing steel. GCr15 bearing steel is classified as a quality alloy; high carbon, chromium and manganese. This study quantitated the proportion of carbide particles in GCr15 bearing steel microstructure using the Mask Region-Based Convolution Neural Networks (Mask R-CNN) approach. The approach precisely located carbide particles, using bounding box indicators based on the concept Region of Interest (ROI) as used in the Mask R-CNN approach and masked the carbide particles within the ROIs. With this approach, we accurately located and masked more than 90% of the target particles, labeled and calculated the area and perimeter of each corresponding blob within the microstructure of GCr15.
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