Heterogeneities of transverse temperature during laminar cooling of high-strength low-alloy hotrolled strip can cause heterogeneous distribution of microstructure, mechanical property and internal stress. Edge masking is a control technology that can be used to improve laminar cooling heterogeneities. In this paper, different edge masking schemes were set up based on the transverse transformation characteristics of the strip. The results show that a slow cooling of 7.1 s during transformation could reduce the heterogeneity of grain size, internal stress and microhardness at the edge and middle of the strip. In addition, the relationship model between grain size and six variables such as transformation start temperature was established by PCA. It provided a new idea for the construction of a microstructure prediction model under nonlinear cooling.
Herein, the evolution of transformation in the width direction of high‐strength low‐alloy hot‐rolled strip is studied by using thermomechanical simulation, and the temperature, microstructure, and internal stress are correlated based on electron backscatter diffraction data. The temperature and time range of the transverse transformation are determined based on the thermal expansion curve. The results show that the transformation at the edge of the steel plate ends earliest and that at the plate center begins at the latest; only 31.9% austenite undergoes transformation at the plate central by the end of laminar cooling. The fraction of transformed austenite at the plate center is the least throughout the laminar cooling process. The proportion of small grains (within the grain size range of 0–4 μm), mean, and variant coefficient of grain sizes at the plate edge are 75%, 4 μm, and 1.29, respectively. In contrast, those values of the central region are 66%, 4.4 μm, and 1.05, respectively. The correlation between temperature and internal stress evolutions is established. It is demonstrated that the internal stress increases monotonically with the average transformation rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.