Abstract
Dislocations -- the carrier of plastic deformation -- are responsible for a wide range of mechanical properties of metals or semiconductors. Those line-like objects tend to form complex networks that are very difficult to characterize or to link to macroscopic properties on the specimen scale. In this work a machine learning based approach for classification of coarse-grained dislocation microstructures in terms of different dislocation density field variables is used. The performance of the learning algorithm combined with domain knowledge from the underlying physics helps to shed light on the interplay between coarse-graining voxel size and the set of suitable or even required density variables for a faithful microstructure characterization.
Highlights
One of the primary mechanisms of plastic deformation in crystalline material is the movement of dislocations
Dislocation structures in specimens of three different sizes are created using the open source discrete dislocation dynamics code MODEL according to the relaxation procedure outlined in the previous section
Applying the D2C coarse-graining to the discrete dislocation structure we obtain continuous dislocation dynamics (CDD) field data
Summary
One of the primary mechanisms of plastic deformation in crystalline material is the movement of dislocations. Dislocations are one-dimensional lattice defects that cause a distortion of the crystallographic lattice. The distortion results in a stress state through which dislocations interact. In addition to the interaction through their stress fields, dislocations may form junctions or even may climb, i.e., move perpendicular to their slip plane. Understanding the complex relation between dislocation microstructures and the emerging mechanical behavior is important from a fundamental point of view but is required for designing new materials with tailored material properties. To this end, both experimental as well as numerical approaches can give important input to such developments
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