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Title: Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Abstract

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path to failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1425767
Alternate Identifier(s):
OSTI ID: 1547147
Report Number(s):
LA-UR-17-30614
Journal ID: ISSN 0927-0256; TRN: US1802161
Grant/Contract Number:  
AC52-06NA25396; 20170103DR
Resource Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 148; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Computer Science; Material Science; Machine Learning, Brittle Failure, Fracture Propagation

Citation Formats

Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, and Viswanathan, Hari. Predictive modeling of dynamic fracture growth in brittle materials with machine learning. United States: N. p., 2018. Web. https://doi.org/10.1016/j.commatsci.2018.01.056.
Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, & Viswanathan, Hari. Predictive modeling of dynamic fracture growth in brittle materials with machine learning. United States. https://doi.org/10.1016/j.commatsci.2018.01.056
Moore, Bryan A., Rougier, Esteban, O’Malley, Daniel, Srinivasan, Gowri, Hunter, Abigail, and Viswanathan, Hari. Thu . "Predictive modeling of dynamic fracture growth in brittle materials with machine learning". United States. https://doi.org/10.1016/j.commatsci.2018.01.056. https://www.osti.gov/servlets/purl/1425767.
@article{osti_1425767,
title = {Predictive modeling of dynamic fracture growth in brittle materials with machine learning},
author = {Moore, Bryan A. and Rougier, Esteban and O’Malley, Daniel and Srinivasan, Gowri and Hunter, Abigail and Viswanathan, Hari},
abstractNote = {We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path to failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.},
doi = {10.1016/j.commatsci.2018.01.056},
journal = {Computational Materials Science},
number = C,
volume = 148,
place = {United States},
year = {2018},
month = {2}
}

Journal Article:

Citation Metrics:
Cited by: 4 works
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Figures / Tables:

Figure 1 Figure 1: Visualization of the extraction of pair data from a 2D HOSS fracture simulation. In the feature vector, the Ls are lengths of initial fractures, the θs are fracture orientations, Dist is the distance between the two fractures and min DB is the minimum distance to the boundary frommore » either fracture. The simulation above has 20 initial fractures which yield 190 data points (with boundary-fracture pairs included).« less

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