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Automated Grain Yield Behavior Classification

  • Mesoscale Materials Science: Experiments and Modeling
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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.

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Acknowledgement

This work is based upon research conducted at the Cornell High Energy Synchrotron Source (CHESS) which is supported by the National Science Foundation under Award DMR-1332208.

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Authors and Affiliations

  1. Cornell High Energy Synchrotron Source, Cornell University, Ithaca, NY, 14853, USA

    Darren C. Pagan, Jakob Kaminsky, Kelly E. Nygren & Armand J. Beaudoin

  2. Department of Computer Science, Cornell University, Ithaca, NY, 14853, USA

    Jakob Kaminsky & Austin R. Benson

  3. NASA Langley Research Center, Hampton, VA, 23666, USA

    Wesley A. Tayon

  4. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Armand J. Beaudoin

Authors
  1. Darren C. Pagan
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  2. Jakob Kaminsky
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  3. Wesley A. Tayon
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  4. Kelly E. Nygren
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  5. Armand J. Beaudoin
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  6. Austin R. Benson
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Correspondence to Darren C. Pagan.

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Pagan, D.C., Kaminsky, J., Tayon, W.A. et al. Automated Grain Yield Behavior Classification. JOM 71, 3513–3520 (2019). https://doi.org/10.1007/s11837-019-03706-2

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  • DOI: https://doi.org/10.1007/s11837-019-03706-2

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