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

• Published in: Journal of the mechanics and physics of solids Vol. 115; no. C; pp. 208 - 229
• Main Authors: Rovinelli, Andrea, Sangid, Michael D., Proudhon, Henry, Guilhem, Yoann, Lebensohn, Ricardo A., Ludwig, Wolfgang
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.06.2018; Elsevier BV; Elsevier
• Subjects: A small crack propagation; B crystal plasticity; B polycrystalline material; Bayesian analysis; BCC metals; Burgers vector; C nondestructive evaluation; Computer simulation; Crack propagation; Data mining; Deformation; Fatigue cracks; Fatigue failure; Fatigue life; Fracture mechanics; Grain boundaries; High cycle fatigue; Machine learning; Materials fatigue; MATERIALS SCIENCE; Mathematical models; Nondestructive testing; Plastic properties; Polycrystals; Predictions; Slip; Titanium alloys; Titanium base alloys; B polycrystalline material; A small crack propagation; C nondestructive evaluation; B crystal plasticity; Machine learning
• DOI: 10.1016/j.jmps.2018.03.007
• ISSN: 0022-5096

•In BCC materials, small cracks propagate accordingly to the pencil-glide model.•A non-local, direction dependent data mining procedure captures crack mechanics.•The proposed non-local driving force adequately reproduces 3D experimental results.•Small cracks overcome grain boundaries by minimizing the residual Burgers vector. 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. [Display omitted]