Research on microhardness prediction of 304 stainless steel turning based on dislocation density

The microscopic dislocation slip and deformation of the machined surface layer of 304 stainless steel during turning leads to severe work hardening, and the study of the microstructural evolution of the surface layer of turned 304 stainless steel is of high importance to investigate microscopic plastic deformation and reduce work hardening. Most existing studies relating to the microstructural evolution of machined surfaces have used two-dimensional (2D) orthogonal cutting models. In this paper, we proposed a method to quickly build a three-dimensional (3D) bevel cutting finite element model and ABAQUS 3D cutting simulation models using temperature-displacement coupling algorithm, which can solve the problems of complex geometric modeling and low computational efficiency of ABAQUS 3D. In addition, a dislocation evolution modeling method was proposed through direct iterative optimization of intrinsic parameters, a subroutine of dislocation density evolution prediction model was written in accordance with finite element secondary development, and a joint simulation-based inverse identification method of dislocation evolution rate parameters was developed to minimize the error between simulated and experimental values of cutting forces. We compared the simulation results and the results of microstructure morphology analysis, verified the effectiveness of the inverse identification method and the dislocation density evolution prediction model, and then investigated the effect of dislocation evolution rate parameters α∗, β∗, k0 on the main cutting force, the microhardness, the dislocation density and the grain size. The microhardness changes of the cut surface were predicted using the microscopic dislocation deformation mechanism. By comparing the experimental data, we found that the predicted microhardness values of the machined surface at different cutting amounts were well consistent with the experimental data, thus verifying the credibility of the microhardness prediction model. Subsequently, the effect of cutting parameters on microhardness values, the average depth of slip zone, the equivalent plastic strain and the strain layer depth was analyzed.