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Unsupervised learning of dislocation motion
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DOI：10.1016/j.actamat.2019.10.011.png)
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摘要
En 中文
The unsupervised learning technique, locally linear embedding (LLE), is applied to the analysis of X-ray diffraction data measured in-situ during the uniaxial plastic deformation of an additively manufactured nickel-based superalloy. With the aid of a physics-based material model, we find that the lower-dimensional coordinates determined using LLE appear to be physically significant and reflect the evolution of the defect densities that dictate strength and plastic flow behavior in the alloy. The implications of the findings for future constitutive model development are discussed, with a focus on wider applicability to microstructure evolution and phase transformation studies during in-situ materials processing. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Keyword:
Plasticity
Machine learning
Additive manufacturing
X-ray diffraction
Nickel-based superalloy
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期刊
IF:
9.3
论文数:
2.0W
被引数:
12.9W
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Principal manifolds and nonlinear dimensionality reduction via tangent space alignment基于切线空间对齐的主流形和非线性降维
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