Characterization of heterogeneous–nano structure in austenitic stainless steel: crystal orientations and hardness distribution

JOURNAL OF MATERIALS SCIENCE(2020)

引用 6|浏览1
暂无评分
摘要
Hardness distribution in the heterogeneous–nano (HN) structure developed in a heavily cold-rolled SUS316LN austenitic stainless steel was systematically investigated. The HN structure consisted of twin domains surrounded by shear bands, which were further embedded in the conventional low-angle lamellae. The twining planes of {111} in the twin domains were nearly parallel to the rolling direction (RD). The average twin spacing was 20–40 nm. The longitudinal direction of the lamellae was also nearly parallel to the RD, and the average interboundary spacing was about 100 nm. Rather shortened ultra-fine grains with an average size of 100 nm were well developed within the shear bands. Microstructural observations using the transmission Kikuchi diffraction technique revealed that both of the disorientation and the value of the kernel average misorientation in the twin domains were relatively low compared to those in the shear bands and lamellar grains, and the misorientation angle of twin boundaries within the twin domain still remained 60°. On the other hand, the misorientation angles among grains within the shear bands were considerably high. The intense strain localization within the shear bands during cold rolling would result in the instant formation of ultra-fine grains surrounded by high-angle boundaries. These results suggested that dislocation density within twin domains was lower than that of the shear bands and lamellar grains. Although the twin domains possess a smaller interboundary spacing than that in the shear bands and lamellar grains, nanoindentation tests revealed that hardnesses were almost identical among the component nanostructures. The nearly identical hardness among the component nanostructures would be ascribed to a lower amount of dislocation strengthening in the twin domains than that in the shear bands and lamellae.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要