Cross slip based dynamic recovery during plane strain compression of aluminium and its role in preferential nucleation of the cube-oriented recrystallized grains
Source abstract
Abstract
Understanding nucleation of the cube-oriented grains during static recrystallization of medium to high stacking fault energy (SFE) FCC metals is both scientifically and industrially important. Dynamic recovery due to weak Hirth junctions is considered as one of the plausible explanations for the nucleation advantage of the cube orientation. However, influence of cross slip based dynamic recovery on the nucleation propensity of the cube orientation has not been studied in detail. For this purpose, we explicitly incorporated stress dependent dynamic recovery of screw dislocations into a dislocation density based crystal plasticity model. The model is utilized for studying influence of cross slip based local dynamic recovery during deformation and recrystallization nucleation of aluminium. We show that dynamic recovery influences local dislocation density difference and disorientation with the neighbouring locations for the main rolling texture components. Next, we analysed nucleation propensity of these texture components using a stochastic nucleation model. Simulation results show that without explicit incorporation of the dynamic recovery of screw dislocations, the Goss orientation shows the highest nucleation propensity. On the other hand, incorporation of the cross slip based dynamic recovery enhances nucleation propensity of the cube component. Therefore, cross slip based dynamic recovery is one of the important mechanisms for nucleation of the cube-oriented recrystallized grains. These results provide new insights into deformation and recrystallization nucleation of medium to high SFE FCC metals.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and mechanical properties · Metallurgy and Material Forming · Metal Forming Simulation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (92)
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