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Title: Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures

Abstract

Computationally efficient estimation of the fatigue response of polycrystalline materials is critical for the development of next generation materials in application domains such as transportation, health, security, and energy industries. This is non-trivial for fatigue of polycrystalline metals since the initiation and growth of fatigue cracks depends strongly on attributes of the microstructure, such as the sizes, shapes, orientations, and neighbors of individual grains. Furthermore, regions of microstructure most likely to initiate cracks correspond to the tails of the distributions of the microstructure features. This requires the execution of large numbers of experiments or simulations to capture the response of the material in a statistically meaningful manner. In this work, a linkage is described to connect polycrystalline microstructures to the statistically signified driving forces controlling the high cycle fatigue (HCF) responses. This is achieved through protocols that quantify these microstructures using 2-pt spatial correlations and represent them in a reduced-dimensional space using principal component analysis. Reduced-order relationships are then constructed to link microstructures to performance characteristics related to their HCF responses. As a result, these protocols are demonstrated for α-titanium, which exhibits heterogeneous microstructure features along with significant elastic and inelastic anisotropies at both the microscale and the macroscale.

Authors:
 [1];  [2];  [3]; ORCiD logo [3]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Mississippi State Univ., Mississippi State, MS (United States)
  3. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF); USDOE
OSTI Identifier:
1466359
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Materials & Design
Additional Journal Information:
Journal Volume: 154; Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 2-point correlations; crystal plasticity; extreme value statistics; fatigue; structure-property relationship

Citation Formats

Paulson, Noah H., Priddy, Matthew W., McDowell, David L., and Kalidindi, Surya R. Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures. United States: N. p., 2018. Web. doi:10.1016/j.matdes.2018.05.009.
Paulson, Noah H., Priddy, Matthew W., McDowell, David L., & Kalidindi, Surya R. Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures. United States. doi:10.1016/j.matdes.2018.05.009.
Paulson, Noah H., Priddy, Matthew W., McDowell, David L., and Kalidindi, Surya R. Wed . "Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures". United States. doi:10.1016/j.matdes.2018.05.009. https://www.osti.gov/servlets/purl/1466359.
@article{osti_1466359,
title = {Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures},
author = {Paulson, Noah H. and Priddy, Matthew W. and McDowell, David L. and Kalidindi, Surya R.},
abstractNote = {Computationally efficient estimation of the fatigue response of polycrystalline materials is critical for the development of next generation materials in application domains such as transportation, health, security, and energy industries. This is non-trivial for fatigue of polycrystalline metals since the initiation and growth of fatigue cracks depends strongly on attributes of the microstructure, such as the sizes, shapes, orientations, and neighbors of individual grains. Furthermore, regions of microstructure most likely to initiate cracks correspond to the tails of the distributions of the microstructure features. This requires the execution of large numbers of experiments or simulations to capture the response of the material in a statistically meaningful manner. In this work, a linkage is described to connect polycrystalline microstructures to the statistically signified driving forces controlling the high cycle fatigue (HCF) responses. This is achieved through protocols that quantify these microstructures using 2-pt spatial correlations and represent them in a reduced-dimensional space using principal component analysis. Reduced-order relationships are then constructed to link microstructures to performance characteristics related to their HCF responses. As a result, these protocols are demonstrated for α-titanium, which exhibits heterogeneous microstructure features along with significant elastic and inelastic anisotropies at both the microscale and the macroscale.},
doi = {10.1016/j.matdes.2018.05.009},
journal = {Materials & Design},
number = C,
volume = 154,
place = {United States},
year = {2018},
month = {5}
}

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