Modelling of Grain Size Evolution with Different Approaches via FEM When Hard Machining of AISI 4140

Abstract

Thermo-mechanical loads during hard turning lead to the formation of so-called White Layers on the machined surface. Characterized by a very fine microstructure and high hardness, White Layers have a negative effect on the fatigue life of a component. The fundamental mechanism for the White Layer formation is the dynamic recrystallization (DRX). Therefore, in the current work, two different DRX models, Helmholtz free energy and Zener-Hollomon, are implemented into Abaqus/Explicit to predict the thickness of the White Layer when hard turning quenched/tempered AISI 4140 and the results are compared with each other. For the simulation of the machining process a Finite Element Method (FEM) model based on the Coupled-Eulerian-Lagrangian (CEL) method is built up. Although both DRX models achieved a very good match between predicted and measured White Layer thickness and grain size evolution on the workpiece rim zone, the Zener-Hollomon model produced more closer agreement.

Computational modelling of dynamic recrystallisation of Ni-based superalloy during linear friction welding

“…In this study, the critical temperature for onset of DRX is assumed to be 1213 K. The material constants in relation to DRX of γ grains of IN718 alloy were sourced from Geng et al [11,30]. X DRX is predicted according to an incremental form of the JMAK model that is activated by plastic strain expressed as [30,46]:…”

“…where g c1 , g c2 , g c3 , g l1 , g l2 , and g l3 are material constants, ε < 0.1s −1 for low strain rate regimes, 0.1s −1 < ε < 10s −1 for medium strain rate regimes, and ε > 10s −1 for high strain rate regimes, and their values were taken from Geng et al [11]. The average grain size D ave was calculated by using a weighted average of the recrystallised grain size d DRX and non-recrystallised grain size D ave (t−1) as [46]: where (t) and (t − 1) are the current and previous computational time steps.…”

“…However, there has been very little work published in relation to the computational modelling for the material microstructural evolution during IN718 LFW. Computational modelling studies about DRX processes have been published for steel, aluminium alloys, and titanium alloys during hot forging or hot isothermal compression testing processes [30,[46][47][48][49][50][51][52]. While these studies have used renowned models such as the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model and the cellular automaton model, they have not been implemented for modelling of DRX of γ grains during LFW of IN718 (or any nickel-based superalloy) [30,46,51,53,54].…”

“…Computational modelling studies about DRX processes have been published for steel, aluminium alloys, and titanium alloys during hot forging or hot isothermal compression testing processes [30,[46][47][48][49][50][51][52]. While these studies have used renowned models such as the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model and the cellular automaton model, they have not been implemented for modelling of DRX of γ grains during LFW of IN718 (or any nickel-based superalloy) [30,46,51,53,54]. The authors previously developed an integrated multiphysics computational model for predicting the microstructural evolution of δ phase during LFW of IN718 at the component scale [34].…”

Analysis of Surface State after Turning of High Tempered Bearing Steel

“…In contrast with grinding, dislocation density in WL after turning is usually higher, causing severely strained matrix [ 4 , 8 , 9 ]. Despite high temperatures and superimposing high stresses, the size of the carbides inside WL remains unchanged, whereas martensite matrix is refined down to several tens of nanometers [ 8 , 10 , 11 , 12 ]. Li et al [ 11 ] also reported the steep grain size gradient in the near surface region.…”