Austenitic Grain Size Prediction in Hot Forging of a 20MnCr5 Steel by Numerical Simulation Using the JMAK Model for Industrial Applications

Abstract

Yield strength and toughness in steels are directly associated with hot forging processes, especially by controlling austenitic grain size and cooling conditions. The phenomenological JMAK model in macroscale has been applied in different material classes to predict grain size after hot forming. However, on an industrial application, there is still a lack of understanding concerning the synergic effects of strain rate and temperature on recrystallization. This preliminary study aimed at investigating the applicability of coupled semi-empirical JMAK and visco-elastoplastic models in numerical simulation to predict austenitic grain size (PAGS). Hot forging of cylindrical samples of a ferritic-perlitic DIN 20MnCr5 steel was performed followed by water quenching. The main influences, such as temperature, strain and strain rate fields following the recrystallization model were investigated using the subroutine of FORGE NxT 2.1 software. The results were evaluated by comparing experimentally measured and simulated PAGS at process end. The forging process generates different strain and strain rate fields in the workpiece, which in turn lead to a variation in the PAGS and recrystallization fractions. The simulation was able to detect the PAGS variation showing a good agreement between the experimental forging results and the applied model.

Constitutive Modelling of High Temperature Flow Behaviour for a Low Carbon High Silicon Bainitic Steel

“…The value of t 0.5 decreases with the increase in temperature and has a considerable influence on the strain rate. It is known that high strain rates increase the driving force for nucleation mechanisms to occur during dynamic recrystallization 8,11,16 . Therefore, the increase of migration rate of dislocations and grain boundaries is one of the consequences of accelerating the DRX process.…”

“…JMAK kinetics has a consolidated application in phase transformation of precipitation through nucleation and growth, which gives the content of a new phase as a function of time [5][6][7] . The global kinetics has been applied in the industrial field of hot forging steels because it represents a "semi-empirical" method, the numerical simulation results of the dynamically recrystallized volume fraction and austenitic grain size [8][9][10] . Besides, in terms of the flow curves, it can be used to model the softening mechanism during the plastic deformation in metallic materials 6,7 .…”

Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

“…It will give a good analysis of causes of forging. Figure 2 represents the identification of defect in forging process [17,18].…”

Numerical and Experimental Study of an Industrial Case for Grain Size Evolution in Bainitic Steel in Controlled Hot Forging and its Influence on Mechanical Performance

“…However, at 950 °C, the strain rate did not influence the grain refinement considerably. That behavior could be related to a decrease in driving force where the temperature dominates the recrystallization mechanism activation 9,15 .…”

“…In this way, and as acquired in an early work 9 , the modeling of the flow curves of this material, as well as the (JMAK) "Johnson-Mehl-Avrami-Kolmogorov" parameters 10 , are necessary for assessing its microstructural evolution such as in recrystallization kinetics. Thus, enabling the application of "semi-empirical" numerical models, based on methods previously proposed by [11][12][13][14][15][16][17] , which can simulate the complexity of the hot forging process and the grain size evolution.…”