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Title: Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations

Abstract

Small crack propagation accounts for most of the fatigue life of engineering structures subject to high cycle fatigue loading conditions. Determining the fatigue crack growth rate of small cracks propagating into polycrystalline engineering alloys is critical to improving fatigue life predictions, thus lowering cost and increasing safety. In this work, cycle-by-cycle data of a small crack propagating in a beta metastable titanium alloy is available via phase and diffraction contrast tomography. Crystal plasticity simulations are used to supplement experimental data regarding the micromechanical fields ahead of the crack tip. Experimental and numerical results are combined into a multimodal dataset and sampled utilizing a non-local data mining procedure. Furthermore, to capture the propensity of body-centered cubic metals to deform according to the pencil-glide model, a non-local driving force is postulated. The proposed driving force serves as the basis to construct a data-driven probabilistic crack propagation framework using Bayesian networks as building blocks. The spatial correlation between the postulated driving force and experimental observations is obtained by analyzing the results of the proposed framework. Results show that the above correlation increases proportionally to the distance from the crack front until the edge of the plastic zone. Moreover, the predictions of the propagationmore » framework show good agreement with experimental observations. Finally, we studied the interaction of a small crack with grain boundaries (GBs) utilizing various slip transmission criteria, revealing the tendency of a crack to cross a GB by propagating along the slip directions minimizing the residual Burgers vector within the GB.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]; ORCiD logo [3];  [4];  [5]
  1. Purdue Univ., West Lafayette, IN (United States). School of Aeronautics and Astronautics
  2. MINES ParisTech, Paris (France). PSL Research Univ., MAT – Centre des materiaux
  3. Laboratoire de Mécanique et Technologie (LMT), Paris (Fance)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  5. Univ. of Lyon (France)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1440486
Report Number(s):
LA-UR-17-31135
Journal ID: ISSN 0022-5096
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of the Mechanics and Physics of Solids
Additional Journal Information:
Journal Volume: 115; Journal Issue: C; Journal ID: ISSN 0022-5096
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; A small crack propagation; B crystal plasticity; B polycrystalline material; C nondestructive evaluation; Machine learning

Citation Formats

Rovinelli, Andrea, Sangid, Michael D., Proudhon, Henry, Guilhem, Yoann, Lebensohn, Ricardo A., and Ludwig, Wolfgang. Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations. United States: N. p., 2018. Web. doi:10.1016/j.jmps.2018.03.007.
Rovinelli, Andrea, Sangid, Michael D., Proudhon, Henry, Guilhem, Yoann, Lebensohn, Ricardo A., & Ludwig, Wolfgang. Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations. United States. doi:10.1016/j.jmps.2018.03.007.
Rovinelli, Andrea, Sangid, Michael D., Proudhon, Henry, Guilhem, Yoann, Lebensohn, Ricardo A., and Ludwig, Wolfgang. Sun . "Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations". United States. doi:10.1016/j.jmps.2018.03.007.
@article{osti_1440486,
title = {Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations},
author = {Rovinelli, Andrea and Sangid, Michael D. and Proudhon, Henry and Guilhem, Yoann and Lebensohn, Ricardo A. and Ludwig, Wolfgang},
abstractNote = {Small crack propagation accounts for most of the fatigue life of engineering structures subject to high cycle fatigue loading conditions. Determining the fatigue crack growth rate of small cracks propagating into polycrystalline engineering alloys is critical to improving fatigue life predictions, thus lowering cost and increasing safety. In this work, cycle-by-cycle data of a small crack propagating in a beta metastable titanium alloy is available via phase and diffraction contrast tomography. Crystal plasticity simulations are used to supplement experimental data regarding the micromechanical fields ahead of the crack tip. Experimental and numerical results are combined into a multimodal dataset and sampled utilizing a non-local data mining procedure. Furthermore, to capture the propensity of body-centered cubic metals to deform according to the pencil-glide model, a non-local driving force is postulated. The proposed driving force serves as the basis to construct a data-driven probabilistic crack propagation framework using Bayesian networks as building blocks. The spatial correlation between the postulated driving force and experimental observations is obtained by analyzing the results of the proposed framework. Results show that the above correlation increases proportionally to the distance from the crack front until the edge of the plastic zone. Moreover, the predictions of the propagation framework show good agreement with experimental observations. Finally, we studied the interaction of a small crack with grain boundaries (GBs) utilizing various slip transmission criteria, revealing the tendency of a crack to cross a GB by propagating along the slip directions minimizing the residual Burgers vector within the GB.},
doi = {10.1016/j.jmps.2018.03.007},
journal = {Journal of the Mechanics and Physics of Solids},
number = C,
volume = 115,
place = {United States},
year = {Sun Mar 11 00:00:00 EST 2018},
month = {Sun Mar 11 00:00:00 EST 2018}
}

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Free Publicly Available Full Text
This content will become publicly available on March 11, 2019
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Cited by: 3 works
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