Accurate distances measures and machine learning of the texture-property relation for crystallographic textures represented by one-point statistics

Abstract The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g. important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is...

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Bibliographic Details
Published in:Modelling and simulation in materials science and engineering 2024-07, Vol.32 (5), p.55016
Main Authors: Iraki, Tarek, Morand, Lukas, Link, Norbert, Sandfeld, Stefan, Helm, Dirk
Format: Article
Language:English
Subjects:
crystallographic texture
distance measure
Earth mover’s distance
machine learning
materials design
optimal processing
Sinkhorn distance
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Summary:Abstract The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g. important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is defined as the probability density function of the orientations of the monocrystal grains conforming a polycrystalline material. For representing the orientation distribution function, there are several approaches such as using generalized spherical harmonics, orientation histograms, and pole figure images. Measuring distances between crystallographic textures is essential for any task that requires assessing texture similarities, e.g. to guide forming processes. Therefore, we introduce novel distance measures based on (i) the Earth Movers Distance that takes into account local distance information encoded in histogram-based texture representations and (ii) a distance measure based on pole figure images. For this purpose, we evaluate and compare existing distance measures for selected use-cases. The present study gives insights into advantages and drawbacks of using certain texture representations and distance measures with emphasis on applications in materials design and optimal process control.
ISSN:0965-0393
1361-651X
DOI:10.1088/1361-651X/ad4c81