On the formulation of higher gradient single and polycrystal plasticity

1998 ◽  
Vol 08 (PR8) ◽  
pp. Pr8-239-Pr8-247
Author(s):  
A. Menzel ◽  
P. Steinmann
Materials ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 5834
Author(s):  
Chi Zhang ◽  
Laszlo S. Toth

During severe plastic deformation (SPD), there is usually extended grain fragmentation, associated with the formation of a crystallographic texture. The effect of texture evolution is, however, coarsening in grain size, as neighbor grains might coalesce into one grain by approaching the same ideal orientation. This work investigates the texture-induced grain coarsening effect in face-centered cubic polycrystals during simple shear, in 3D topology. The 3D polycrystal aggregate was constructed using a cellular automaton model with periodic boundary conditions. The grains constituting the polycrystal were assigned to orientations, which were updated using the Taylor polycrystal plasticity approach. At the end of plastic straining, a grain detection procedure (similar to the one in electron backscatter diffraction, but in 3D) was applied to detect if the orientation difference between neighboring grains decreased below a small critical value (5°). Three types of initial textures were considered in the simulations: shear texture, random texture, and cube-type texture. The most affected case was the further shearing of an initially already shear texture: nearly 40% of the initial volume was concerned by the coalescence effect at a shear strain of 4. The coarsening was less in the initial random texture (~30%) and the smallest in the cube-type texture (~20%). The number of neighboring grains coalescing into one grain went up to 12. It is concluded that the texture-induced coarsening effect in SPD processing cannot be ignored and should be taken into account in the grain fragmentation process.


2005 ◽  
Vol 32 (3-4) ◽  
pp. 284-293 ◽  
Author(s):  
T. Böhlke ◽  
G. Risy ◽  
A. Bertram

2001 ◽  
Vol 49 (17) ◽  
pp. 3433-3441 ◽  
Author(s):  
D. Raabe ◽  
M. Sachtleber ◽  
Z. Zhao ◽  
F. Roters ◽  
S. Zaefferer

2006 ◽  
Vol 519-521 ◽  
pp. 1563-1568 ◽  
Author(s):  
Olaf Engler

In order to predict the mechanical properties of Al sheet products, the evolution of microstructure and crystallographic texture along the process chain must be tracked. During the thermo-mechanical processing in commercial production lines the material experiences a complex history of temperature, time and strain paths, which results in alternating cycles of deformation and recrystallization with the associated changes in texture and microstructure. In the present paper the texture evolution of AA 3104 can body stock is modelled. In particular, the earing behaviour at final gauge is linked to the decisive steps of deformation and recrystallization along the thermomechanical process chain. For this purpose, the textures predicted by a comprehensive throughprocess model of the texture evolution during the thermo-mechanical production of Al sheet are input into a polycrystal-plasticity approach to simulate earing of the final gauge sheets.


2008 ◽  
Vol 75 (5) ◽  
Author(s):  
M. R. Tonks ◽  
A. J. Beaudoin ◽  
F. Schilder ◽  
D. A. Tortorelli

More accurate manufacturing process models come from better understanding of texture evolution and preferred orientations. We investigate the texture evolution in the simplified physical framework of a planar polycrystal with two slip systems used by Prantil et al. (1993, “An Analysis of Texture and Plastic Spin for Planar Polycrystal,” J. Mech. Phys. Solids, 41(8), pp. 1357–1382). In the planar polycrystal, the crystal orientations behave in a manner similar to that of a system of coupled oscillators represented by the Kuramoto model. The crystal plasticity finite element method and the stochastic Taylor model (STM), a stochastic method for mean-field polycrystal plasticity, predict the development of a steady-state texture not shown when employing the Taylor hypothesis. From this analysis, the STM appears to be a useful homogenization method when using representative standard deviations.


2015 ◽  
Vol 2 (10) ◽  
pp. 4898-4903 ◽  
Author(s):  
Kai Zhang ◽  
Knut Marthinsen ◽  
Bjørn Holmedal ◽  
Trond Aukrust ◽  
Antonio Segatori

2013 ◽  
Vol 61 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Nathan R. Barton ◽  
Athanasios Arsenlis ◽  
Jaime Marian

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