Predicting the 3D fatigue crack growth rate of short cracks using multimodal data via Bayesian network: in-situ experiments and crystal plasticity simulations

Andrea Rovinelli (1) , Michael Sangid (1) , Henry Proudhon (2) , Yoann Guilhem (3) , Ricardo A. Lebensohn (4) , Wolfgang Ludwig (5, 6)

1 School of Aeronautics and Astronautics [Purdue]
2 CDM - Centre des Matériaux
3 LMT - Laboratoire de Mécanique et Technologie
4 LANL - Los Alamos National Laboratory
5 MATEIS - Matériaux, ingénierie et science [Villeurbanne]
6 ESRF - European Synchrotron Radiation Facility

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.

Mots clés: crystal plasticity; small crack propagation; machine learning; polycrystalline material; nondestructive evaluation

Domaines: Mécanique des matériaux [physics.class-ph]; Matériaux et structures en mécanique [physics.class-ph]

Andrea Rovinelli, Michael Sangid, Henry Proudhon, Yoann Guilhem, Ricardo A. Lebensohn, et al.. Predicting the 3D fatigue crack growth rate of short cracks using multimodal data via Bayesian network: in-situ experiments and crystal plasticity simulations. Journal of the Mechanics and Physics of Solids, 2018, 115, pp.208-229. ⟨10.1016/j.jmps.2018.03.007⟩. ⟨hal-01729179⟩