Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
Markus K\"uhbach, Franz Roters

TL;DR
This paper develops novel algorithms to quantify 3D spatial correlations between microstructural state variables and distances to grain boundaries in spectral method crystal plasticity simulations, enabling detailed microstructure analysis.
Contribution
It introduces new grain reconstruction and distancing algorithms that improve post-processing of spectral method simulations for microstructure analysis.
Findings
Algorithms successfully quantify microstructure-boundary correlations.
Parallel implementation enhances computational efficiency.
Application reveals stress and disorientation gradients near grain boundaries.
Abstract
Deformation microstructure heterogeneities play a pivotal role during dislocation patterning and interface network restructuring. Thus, they affect indirectly how an alloy recrystallizes if at all. Given this relevance, it has become common practice to study the evolution of deformation microstructure heterogeneities with 3D experiments and full-field crystal plasticity computer simulations including tools such as the spectral method. Quantifying material point to grain or phase boundary distances, though, is a practical challenge with spectral method crystal plasticity models because these discretize the material volume rather than mesh explicitly the grain and phase boundary interface network. This limitation calls for the development of interface reconstruction algorithms which enable us to develop specific data post-processing protocols to quantify spatial correlations between…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
