Characterization of Hot Workability in AISI 4340 Based on a 3D Processing Map

Abstract

This study performed high-temperature compression tests at the temperature 900 to 1200 °C and strain rate 0.01 to 10 s−1 to characterize the high-temperature deformation behavior of AISI 4340. The constitutive equation of AISI 4340 was expressed using the Arrhenius model and the Zener–Hollomon (Z) parameter. Dynamic Recrystallization (DRX) behavior was evaluated by observing the compressed specimen with Electron Backscatter Diffraction (EBSD). The processing map is based on the dissipation efficiency of the dynamic material model (DMM) and the plastic instability criterion of Ziegler. At strain 0.4, the power dissipation efficiency value is 0.5 or more, and the instable zones are immediately identified through the processing map. The strain, strain rate and temperature data obtained from the FEM simulation of the hot forging process are displayed on the proposed 3D processing map to avoid the flow instability zones and ensure high power dissipation efficiency zones, allowing the operator to control the process’s temperature and speed.

Numerical Simulation of Crack Condition in Forging Products of M50 Bearing Steel Based on Processing Map Theory

“…Figure 4 shows the temperature change history measured using a thermocouple attached to the center of the specimen during the compression test. To account for the temperature-corrected flow stress, the temperature and stress relationship proposed by Park et al [32] was linearly interpolated to match the set temperature of the isothermal compression test. To determine the friction-corrected flow stress, the correction method for determining the friction coefficient m, proposed by Ebrahimi et al [34], was adopted.…”

“…The 3D processing map is shown in Figure 7. Stacking 2D processing maps from strain 0.1 to 1.0, according to Park et al [32], the 3D processing map was plotted separately without overlapping η and ξ to increase visibility. According to the color depth, Figure 7a is a 3D power dissipation efficiency map stacked at 0.01 from strain 0.1 to 1.0.…”

“…Some researchers developed a 3D processing map according to the strain of the material to solve this problem [29][30][31]. Recently, Park et al developed FEM simulation data that can plot on the 3D processing map to control process parameters, avoid flow instability and include high power dissipation efficiency [32]. Jeong et al employed a 3D processing map and utilized a learning environment founded on a Q-learning algorithm in order to optimize processing parameters, encompassing both the temperature and stroke speed of the workpiece [33].…”