Dislocation Density and Grain Size Evolution in the Machining of Al6061-T6 Alloys

Abstract

This study focuses on addressing the severe plastic deformation (SPD) behavior and the effects o f machining parameters on microstructure alternations in machined surface cre ated from high-speed machining. A finite element (FE) model is proposed to predict the orthogonal machining of A16061-T6 alloys at high speeds. By extracting strains, strain rates, stresses, and temperatures from this model, a dislocation density-based model is incorporated into it as a user-defined subroutine to predict dislocation densities and grain sizes in machined surface. The predicted results show that dislocation densities decrease with the depths below the machined surface, but grain sizes present an opposite tendency. Higher cutting speeds are associated with thinner plastic deformation layers. Dislocation densities decrease with cutting speeds, but grain sizes increase with cutting speeds in machined surface. Dislocation densities decrease initially and then increase with feed rates. There exists a critical feed rate to generate the maximum SPD layer in machined surface. Tool rake angle has a great impact on the depth o f plastic deformation layer. Thus, it affects the distributions of dislocation densities and grain sizes. A large negative rake angle can induce an increased dislocation density in machined surface. The predicted chip thicknesses, cutting forces, distributions of dislocation densities, and grain sizes within the range of machining parameters have good agreement with experi ments in terms of chip morphology, cutting forces, microstructure, and microhardness in chip and machined surface.

An Efficient Multi-Scale Simulation Architecture for the Prediction of Performance Metrics of Parts Fabricated Using Additive Manufacturing

“…[22][23][24][25][26][27][28][29][30][31] Similarly, the in-house thermo-mechanical constitutive relationships for performance metric modeling are addressed in the literature. [17,20,21,[32][33][34][35][36] The computational complexity for currently available finite element algorithms to simulate a full-sized part which fills an SLM machine has been demonstrated to be intractable, [37] even for the most powerful supercomputers. The present simulation environment includes an attempt to solve the problem with intelligent solver algorithms and inclusion of problem specific asymptotics (Figures 7 and 8).…”

An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling

“…The UC problem (Fig. Some recent studies on dislocation density-based grain refinement modeling have been also conducted recently [37,38], The model has been shown to accurately predict average grain size at the interface.…”

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

“…The study by Estrin et al introduced a dislocation density-based model to describe strain hardening behavior [19], which is then employed in [26] to model grain refinement in equal channel angular pressing for copper. Ding et al applied this dislocation density-based model for the simulation of dislocation density and grain size evolution of titanium in [27] and of Al6061-T6 alloys in [28] after orthogonal cutting, respectively. This approach was later utilized by Atmani et al [29] to simulate the grain refinement in machining of oxygen-free high conductivity copper.…”

“…This DRX model has also been adapted for (hard) machining of steels latest by Li et al for H13 steel [30] and earlier by Ding and Shin for AISI 52100 steel [31]. In these studies [26][27][28][29][30], the FE software Abaqus/Explicit with CEL along with ALE methods and in [31] AdvantEdge FEM were used.…”