Effects of initial grain size and strain on grain boundary engineering of high-nitrogen CrMn austenitic stainless steel

18 Mn18 Cr0.5 N steel with an initial grain size of 28–177 μm was processed by 2.5%–20% cold rolling and annealing at 1000°C for 24 h,and the grain boundary character distribution was examined via electron backscatter diffraction.Low strain(2.5%) favored the formation of low-Σ boundaries.At this strain,the fraction of low-Σ boundaries was insensitive to the initial grain size.However,specimens with fine initial grains showed decreasing grain size after grain boundary engineering processing.The fraction of low-Σ boundaries and the(Σ9 + Σ27)/Σ3 value decreased with increasing strain; furthermore,the specimens with fine initial grain size were sensitive to the strain.Finally,the effects of the initial grain size and strain on the grain boundary engineering were discussed in detail.

Hot deformation mechanism and microstructure evolution of an ultra-high nitrogen austenitic steel containing Nb and V

The flow curves of an ultra-high nitrogen austenitic steel containing niobium(Nb) and vanadium(V) were obtained by hot compression deformation at temperatures ranging from 1000°C to 1200°C and strain rates ranging from 0.001 s-1 to 10 s-1. The mechanical behavior during hot deformation was discussed on the basis of flow curves and hot processing maps. The microstructures were analyzed via scanning electron microscopy and electron backscatter diffraction. The relationship between deformation conditions and grain size after dynamic recrystallization was obtained. The results show that the flow stress and peak strain both increase with decreasing temperature and increasing strain rate. The hot deformation activation energy is approximately 631 k J/mol, and a hot deformation equation is proposed.(Nb,V)N precipitates with either round, square, or irregular shapes are observed at the grain boundaries and in the matrix after deformation. According to the discussion, the hot working should be processed in the temperature range of 1050°C to 1150°C and in the strain rate range of 0.01 to 1 s-1.

Automatic recognition and intelligent analysis of central shrinkage defects of continuous casting billets based on deep learning

The internal quality inspection of the continuous casting billets is very important,and mis-inspection will seriously affect the subsequent production process.The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets.The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9.We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length,width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets.The results show that all the testing images are rated correctly,and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.