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

Rovinelli, Andrea; Sangid, Michael D.; Proudhon, Henry; Guilhem, Yoann; Lebensohn, Ricardo A.; Ludwig, Wolfgang

doi:10.1016/j.jmps.2018.03.007

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

Journal Article · Sun Mar 11 00:00:00 EST 2018 · Journal of the Mechanics and Physics of Solids

DOI:https://doi.org/10.1016/j.jmps.2018.03.007· OSTI ID:1440486

^[1]; Sangid, Michael D. ^[1];

^[2];

^[3]; Lebensohn, Ricardo A. ^[4]; Ludwig, Wolfgang ^[5]

Purdue Univ., West Lafayette, IN (United States). School of Aeronautics and Astronautics
MINES ParisTech, Paris (France). PSL Research Univ., MAT – Centre des materiaux
Laboratoire de Mécanique et Technologie (LMT), Paris (Fance)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Lyon (France)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1440486

Report Number(s):: LA-UR-17-31135

Journal Information:: Journal of the Mechanics and Physics of Solids, Vol. 115, Issue C; ISSN 0022-5096

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 74 works

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Identification of Plastic Properties From Conical Indentation Using a Bayesian-Type Statistical Approach Zhang, Yupeng; Hart, Jeffrey D.; Needleman, Alan Journal of Applied Mechanics, Vol. 86, Issue 1 https://doi.org/10.1115/1.4041352	journal	October 2018
Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior Spear, Ashley D.; Kalidindi, Surya R.; Meredig, Bryce JOM, Vol. 70, Issue 7 https://doi.org/10.1007/s11837-018-2894-0	journal	May 2018
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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

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