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.

Research on Intelligent Identification and Grading of Nonmetallic Inclusions in Steels Based on Deep Learning.

Non-metallic inclusions are unavoidable defects in steel, and their type, quantity, size, and distribution have a great impact on the quality of steel. At present, non-metallic inclusions are mainly detected manually, which features high work intensity, low efficiency, proneness to misjudgment, and low consistency of results. In this paper, based on deep neural network algorithm, a small number of manually labeled, low-resolution metallographic images collected by optical microscopes are used as the dataset for intelligent boundary extraction, classification, and rating of non-metallic inclusions. The training datasets are cropped into those containing only a single non-metallic inclusion to reduce the interference of background information and improve the accuracy. To deal with the unbalanced distribution of each category of inclusions, the reweighting cross entropy loss and focal loss are respectively used as the category prediction loss and boundary prediction loss of the DeepLabv3+ semantic segmentation model. Finally, the length and width of the minimum enclosing rectangle of the segmented inclusions are measured to calculate the grade of inclusions. The resulting accuracy is 90.34% in segmentation and 90.35% in classification. As is verified, the model-based rating results are consistent with those of manual labeling. For a single sample, the detection time is reduced from 30 min to 15 s, significantly improving the detection efficiency.

Effects of Cold Rolling on the Microstructure and Corrosion Resistance of the Double-Glow Plasma Ni-Cr Alloying Layer on Q235 Steel.

A Ni-Cr alloyed layer was prepared on the surface of Q235 steel using double-glow plasma surface alloying (DGPSA) technology and the alloyed layer was cold-rolled with different deformation rates. The microstructure, composition distribution and phase composition of the alloyed layer were characterized using a scanning electron microscope (SEM), an energy dispersive spectrometer (EDS), X-ray diffraction (XRD) and an electrochemical workstation. On this basis, the corrosion resistance of the alloyed layer was analyzed. The results showed that a Ni-Cr alloyed layer formed on the surface of Q235 steel following double-glow plasma nickel-chromium alloying. The alloy elements of Ni and Cr were distributed in a gradient from the outside to the inside and the main phases were FeCr0.29Ni0.16C0.06, Cr23C6 and γ solid solution. The alloyed layer, once cold-rolled with different deformation rates, underwent synchronous plastic deformation with the substrate, with no fracturing and spalling. The self-corrosion potential of the cold-rolled specimens in 5% H2SO4 and 3.5% NaCl solution is close to that of 304L stainless steel, and the corrosion currents are much lower. The corrosion resistance of the cold-rolled specimens is comparable to the original specimens, with no significant changes.