Effects of Dislocation Density Evolution on Mechanical Behavior of OFHC Copper during High-Speed Machining

Abstract

This paper aims at investigating the change in material behavior induced by microstructure evolution during high-speed machining processes. Recently, high-speed machining has attracted quite a lot of interest from researchers due to its high efficiency and surface quality in machining large-scale components. However, the material behavior could change significantly at high-cutting speeds compared to the conventional cutting conditions, including their microstructure and t mechanical response. This is due to the basic physics of material at microscopic levels with high strain, high strain rates, and high temperatures. In this study, the dislocation density-related microstructure evolution process and mechanical behavior of OFHC (Oxygen-free high-conductivity) copper in high-speed machining with speeds ranging from 750 m/min to 3000 m/min are investigated. SEM (Scanning Electron Microscope) and advanced EBSD (Electron Backscattered Diffraction) techniques are used to obtain high-quality images of the microstructures and analyze the dislocation density and grain size evolution with different cutting speeds. Moreover, as material plasticity is induced by the motion of dislocations at micro-scales, a dislocation-density based (DDB) model is applied to predict strain-stress and microstructure information during the cutting process. The distributions of dislocation densities, both statistically stored dislocations (SSDs) and geometrically necessary dislocations (GNDs), are obtained through simulation and experimentation, respectively. The results show that the fluctuation in the cutting forces at high cutting speeds is induced by the specific evolution and distribution of the dislocation density under high strain-rates, and the periodical distribution of sub-surface and fracture behavior during chip separation, which are also found to be influenced by the evolution of the dislocation density.

Modelling of Guillotine Cutting of a Cold-Rolled Steel Sheet

“…The complete reverse (opposite) process is the modeling of the material separation. In this case, the process should be designed in such a way that the Huber–Mises reduced stress should exceed the allowable stress that appears in the direct cutting zone [5,6,7,8,9,10,11]. Otherwise, the material being removed would not be separated.…”

“…Nowadays, the works concerning the numerical modelling of machining processes using FEM (the finite element method) are very popular. Many literature positions describe numerical or experimental investigations of machining processes, such as turning [5], milling [6,7,8], boring [9,10], grinding [11], etc. Much less attention is given to covering mechanical cutting on guillotines [12,13,14,15,16,17,18].…”

Simulation of Work Hardening in Machining Inconel 718 with Multiscale Grain Size

“…With the increase in the cutting speed, feed rate, and radial cutting depth, the grain size of the machined surface changes slightly and gradually returns to its initial size. Liu et al [17] simulated the cutting process of OFHC copper using the DDB model and concluded from the experiment and simulation results that the fluctuation of a cutting force at high cutting speed was caused by the evolution and distribution of the dislocation density. The dislocation density on the machined surface increased with the increase in cutting speed, while it decreased in the chips.…”

Numerical Simulation of High Strain Rate and Temperature Properties of Laser Powder Bed Fusion Ti6Al4V(ELI) Determined Using a Split Hopkinson Pressure Bar

“…Numerical simulations of the behaviour of materials require constitutive equations that account for properties such as inelastic deformation (flow stress) and damage evolution as a function of variables such as strain, strain rate and temperature. Because of simplicity and the wide availability of required coefficients and constant parameters, numerical simulations of dynamic properties of materials such as alloys of titanium, aluminium, copper and steel, are normally based on the Johnson–Cook (J–C) material model [ 32 , 33 , 34 , 35 ]. This model is in-built in the commercial finite element analysis (FEA) software ABAQUS/Explicit for simulating high-strain-rate deformation of materials including the simulation of adiabatic transients [ 36 ].…”