Mesoscopic coupled modeling of texture formation during recrystallization considering stored energy decomposition

Abstract

Microstructure-based simulations were performed to understand the mechanism involved with texture formation during recrystallization in polycrystalline interstitial free (IF) steel. The crystal plasticity finite element method (CPFEM) was used to simulate mesoscopic deformation with its heterogeneity. The orientation components were decomposed according to the stored deformation energy, and the results were used to define potential candidates for nucleation sites. On the basis of the oriented nucleation approach, the subsequent evolution of microstructure and texture during recrystallization was simulated with the cellular automaton (CA) method. The coupled microstructure-based simulations provided the recrystallization kinetics, grain size distribution, and crystallographic texture of the recrystallized IF steels. Those results were in good agreement with experimental results obtained from electron back scattered diffraction (EBSD). This suggests that the level of detail of the deformed state captured from the crystal plasticity FE calculation can provide enough information, in terms of local stored energy and nucleation site selection, to enable modeling of the subsequent primary recrystallization process.

Multi-Scale Modeling of Microstructure Evolution during Multi-Pass Hot-Rolling and Cooling Process

“…Simulation studies on microstructure evolution during recrystallization for a single-pass process have been performed by utilizing a CA model [ 9 , 10 , 11 ] and combining the CA method with the finite element method (FEM) [ 12 , 13 ] or the crystal plasticity finite element method (CPFEM) [ 14 , 15 , 16 ]. Those studies investigated the mesoscale grain structural evolution as well as the macro- or meso-scale mechanical response.…”

“…As a result of inherent experimental difficulties, these studies cannot fully elucidate the physical mechanisms contributing to grain refinement, because one needs to consider the temporal evolution of the multi-pass processes of recrystallization and the γ→α transformation, With the development of technologies in computer science, numerical simulation methods have popularized and become an important tool for understanding the mechanisms of microstructure formation during material processes due to their capabilities to present the visual, temporal evolution of microstructures. Among different numerical models, the cellular automata (CA) approach, combining both computational efficiency and simplicity [8], has been commonly applied to investigate various phenomena such as recrystallization [9][10][11][12][13][14][15][16][17][18][19][20][21][22], phase transformation [23][24][25][26][27][28][29][30][31], and grain coarsening [32,33].…”

Crystal plasticity simulation of in-grain microstructural evolution during large deformation of IF-steel

“…Characterization and understanding of these deformation heterogeneities and the associated microstructural evolution during plastic deformation play a critical role in identifying the underlying mechanisms behind many physical phenomena. For instance, a thorough physical understanding of the mechanisms leading to recrystallization is not feasible without properly characterizing microstructural evolution during plastic deformation [1][2][3][4][5][6][7] . Moreover, damage formation, fracture, and failure in metals are also often related to deformation localization and microstructures formed during deformation [8][9][10][11] .…”

A New Stored Energy Model Based on Plastic Work of Back Stress during Cyclic Loading in Polycrystalline Metal

“…They also found that most of the energy stored in the dislocations is associated with their long-range stress fields (back stress), which accounted for more than 95%. Recently, mesoscopic microstructure-based modeling approaches such as crystal plasticity finite element method (CPFEM), have been popular for calculating the stored energy in a heterogeneous polycrystalline [ 25 ]. However, those methods are too complex to be used in engineering applications.…”