@article{7b540764d12b4bb7a9e340b9a68eb5c1,
title = "Unsupervised learning of dislocation motion",
abstract = "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.",
author = "Pagan, {Darren C.} and Phan, {Thien Q.} and Weaver, {Jordan S.} and Benson, {Austin R.} and Beaudoin, {Armand J.}",
note = "Funding Information: This work is based upon research conducted at the Center for High Energy X-ray Sciences (CHEXS) which is supported by the National Science Foundation under award DMR-1829070. AJB received support through the Office of Naval Research (Contract N00014-16-1-3126 ). The authors would like to thank Magnus Ahlfors at Quintus Technologies for performing the hot isostatic pressing treatment. The authors would like to thank Professor Matthew Miller, Dr. Kelly Nygren, Dr. Paul Shade, Dr. Nathan Barton, and Dr. Fan Zhang for helpful suggestions. Funding Information: This work is based upon research conducted at the Center for High Energy X-ray Sciences (CHEXS) which is supported by the National Science Foundation under award DMR-1829070. AJB received support through the Office of Naval Research (Contract N00014-16-1-3126). The authors would like to thank Magnus Ahlfors at Quintus Technologies for performing the hot isostatic pressing treatment. The authors would like to thank Professor Matthew Miller, Dr. Kelly Nygren, Dr. Paul Shade, Dr. Nathan Barton, and Dr. Fan Zhang for helpful suggestions. Publisher Copyright: {\textcopyright} 2019 Acta Materialia Inc.",
year = "2019",
month = dec,
doi = "10.1016/j.actamat.2019.10.011",
language = "English (US)",
volume = "181",
pages = "510--518",
journal = "Acta Materialia",
issn = "1359-6454",
publisher = "Elsevier Limited",
}