Research on Automatic Identification and Rating of Ferrite-Pearlite Grain Boundaries Based on Deep Learning

Grain size has a significant effect on the mechanical properties of metals. It is very important to accurately rate the grain size number of steels. This paper presents a model for automatic detection and quantitative analysis of the grain size of ferrite-pearlite two-phase microstructure to segment ferrite grain boundaries. In view of the challenging problem of hidden grain boundaries in pearlite microstructure, the number of hidden grain boundaries is inferred by detecting them with the confidence of average grain size. The grain size number is then rated using the three-circle intercept procedure. The results show that grain boundaries can be accurately segmented by using this procedure. According to the rating results of grain size number of four types of ferrite-pearlite two-phase microstructure samples, the accuracy of this procedure is greater than 90%. The grain size rating results deviate from those calculated by experts using the manual intercept procedure by less than Grade 0.5-the allowable detection error specified in the standard. In addition, the detection time is shortened from 30 min of the manual intercept procedure to 2 s. The procedure presented in this paper allows automatic rating of grain size number of ferrite-pearlite microstructure, thereby effectively improving the detection efficiency and reducing the labor intensity.

Automated summary

Grain size significantly affects the mechanical properties of metals, making accurate assessment of grain size in steels crucial. This paper presents a model for automatic detection and quantitative analysis of the grain size in ferrite-pearlite microstructures. By segmenting the ferrite grain boundaries, hidden grain boundaries in pearlite microstructures are inferred based on the confidence of average grain size. The grain size is then rated using the three-circle intercept procedure. The results show accurate segmentation of grain boundaries and a rating accuracy exceeding 90% compared to manual expert-based calculations.