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Unsupervised Learning of Dislocation Motion

Published

Author(s)

Darren Pagan, Thien Q. Phan, Jordan Weaver, Austin Benson, Armand Beaudoin

Abstract

The unsupervised learning technique, locally linear embedding (LLE), is applied to the analysis of X-ray diffraction data measured in-situ during 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 ow behavior in the alloy. The implications of the findings for future constitutive model development are discussed, with a focus on wider applicability of the approach to microstructure evolution and phase transformation studies during in-situ materials processing.
Citation
Acta Materialia
Volume
181

Keywords

Additive Manufacturing, Synchrotron X-Ray Diffraction, Unsupervised Machine Learning

Citation

Pagan, D. , Phan, T. , Weaver, J. , Benson, A. and Beaudoin, A. (2019), Unsupervised Learning of Dislocation Motion, Acta Materialia, [online], https://doi.org/10.1016/j.actamat.2019.10.011 , https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928192 (Accessed July 16, 2024)

Issues

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Created October 14, 2019, Updated February 23, 2022