Automated Grain Yield Behavior Classification

Darren C. Pagan, Jakob Kaminsky, Wesley A. Tayon, Kelly E. Nygren, Armand J. Beaudoin, Austin R. Benson

Research output: Contribution to journalArticle

Abstract

A method for classifying grain stress evolution behaviors using unsupervised learning techniques is presented. The method is applied to analyze grain stress histories measured in situ using high-energy x-ray diffraction microscopy from the aluminum–lithium alloy Al-Li 2099 at the elastic–plastic transition (yield). The unsupervised learning process automatically classified the grain stress histories into four groups: major softening, no work-hardening or -softening, moderate work-hardening, and major work-hardening. The orientation and spatial dependence of these four groups are discussed. In addition, the generality of the classification process to other samples is explored.

Original languageEnglish (US)
Pages (from-to)3513-3520
Number of pages8
JournalJOM
Volume71
Issue number10
DOIs
StatePublished - Oct 1 2019

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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    Pagan, D. C., Kaminsky, J., Tayon, W. A., Nygren, K. E., Beaudoin, A. J., & Benson, A. R. (2019). Automated Grain Yield Behavior Classification. JOM, 71(10), 3513-3520. https://doi.org/10.1007/s11837-019-03706-2