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Title: Investigation on temporal evolution of the grain refinement in copper under high strain rate loading via in-situ synchrotron measurement and predictive modeling

Abstract

Synchrotron X-rays are integrated with a modified Kolsky tension bar to conduct in situ tracking of the grain refinement mechanism operating during the dynamic deformation of metals. Copper with an initial average grain size of 36 μm is refined to 6.3 μm when loaded at a constant high strain rate of 1200 s -1. The synchrotron measurements revealed the temporal evolution of the grain refinement mechanism in terms of the initiation and rate of refinement throughout the loading test. A multiscale coupled probabilistic cellular automata based recrystallization model has been developed to predict the microstructural evolution occurring during dynamic deformation processes. The model accurately predicts the initiation of the grain refinement mechanism with a predicted final average grain size of 2.4 μm. As a result, the model also accurately predicts the temporal evolution in terms of the initiation and extent of refinement when compared with the experimental results.

Authors:
 [1]; ORCiD logo [1];  [2]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22), Scientific User Facilities Division
OSTI Identifier:
1405301
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Acta Materialia
Additional Journal Information:
Journal Volume: 143; Journal Issue: C; Journal ID: ISSN 1359-6454
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Shah, Pooja Nitin, Shin, Yung C., and Sun, Tao. Investigation on temporal evolution of the grain refinement in copper under high strain rate loading via in-situ synchrotron measurement and predictive modeling. United States: N. p., 2017. Web. doi:10.1016/j.actamat.2017.10.005.
Shah, Pooja Nitin, Shin, Yung C., & Sun, Tao. Investigation on temporal evolution of the grain refinement in copper under high strain rate loading via in-situ synchrotron measurement and predictive modeling. United States. doi:10.1016/j.actamat.2017.10.005.
Shah, Pooja Nitin, Shin, Yung C., and Sun, Tao. Tue . "Investigation on temporal evolution of the grain refinement in copper under high strain rate loading via in-situ synchrotron measurement and predictive modeling". United States. doi:10.1016/j.actamat.2017.10.005. https://www.osti.gov/servlets/purl/1405301.
@article{osti_1405301,
title = {Investigation on temporal evolution of the grain refinement in copper under high strain rate loading via in-situ synchrotron measurement and predictive modeling},
author = {Shah, Pooja Nitin and Shin, Yung C. and Sun, Tao},
abstractNote = {Synchrotron X-rays are integrated with a modified Kolsky tension bar to conduct in situ tracking of the grain refinement mechanism operating during the dynamic deformation of metals. Copper with an initial average grain size of 36 μm is refined to 6.3 μm when loaded at a constant high strain rate of 1200 s-1. The synchrotron measurements revealed the temporal evolution of the grain refinement mechanism in terms of the initiation and rate of refinement throughout the loading test. A multiscale coupled probabilistic cellular automata based recrystallization model has been developed to predict the microstructural evolution occurring during dynamic deformation processes. The model accurately predicts the initiation of the grain refinement mechanism with a predicted final average grain size of 2.4 μm. As a result, the model also accurately predicts the temporal evolution in terms of the initiation and extent of refinement when compared with the experimental results.},
doi = {10.1016/j.actamat.2017.10.005},
journal = {Acta Materialia},
number = C,
volume = 143,
place = {United States},
year = {Tue Oct 03 00:00:00 EDT 2017},
month = {Tue Oct 03 00:00:00 EDT 2017}
}

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