Abnormal effect of strain rate on dynamic recrystallization of austenite in medium carbon steel alloyed by boron

Abstract

According to literature data confirmed by our experiments on various steels, the strain degree that triggers the dynamic recrystallization of austenite in high-temperature deformation increases with the strain rate. However, as shown in the present work, micro-alloying of medium carbon steel by boron results in the opposite dependence in the strain rate range of 1 to 100 s −1 . The corresponding features of stress-strain diagrams comply with the former austenite structures revealed by chemical etching. A specific effect of boron is discussed with allowance for its segregations at grain boundaries where the recrystallization embryos form. Such segregations hinder rearrangement of atoms involved in the grain boundary migration and hence decelerate their bulging necessary to nucleate recrystallization origins. Similar to the known "yield tooth" effect, very strong strain hardening of the steel with boron eventually enables the boundaries to free from the segregated atoms and begin the dynamic recrystallization at relatively low strain degrees. The obtained results indicate that the considered phenomenon is not due to the ultimate strain rate as such but results from its combination with the boron alloying. The revealed regularities gain in significance for evident reasons. First, they enhance ductility of high-strength steels hot deformed with very high strain rates as, for instance, in case of hammering. Second, such treatments notably refine the austenite structure and, correspondingly, improve the transformed structure and final mechanical properties after steel quenching.

Resistance to Abrasive Wear with Regards to Mechanical Properties Using Low-Alloy Cast Steels Examined with the Use of a Dry Sand/Rubber Wheel Tester

“…The strongest effect of boron is seen when its concentration is over 0.0005-0.0008% but does not exceed 0.003-0.005% [22]. Recent studies are also devoted to the effect of boron on hardenability [23][24][25]. For example, Gent et al [23] established a novel datadriven machine learning model and demonstrated that machine learning can predict accurately and efficiently the hardenability curve of boron steel and guide the material design and heat treatment process of advanced boron steel.…”