|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Fatigue in Materials: Fundamentals, Multiscale Modeling and Prevention
||A General Probabilistic Framework Combining Experiments and Simulations to Identify the Small Crack Driving Force
||Andrea Rovinelli, Michael D Sangid, Ricardo A Lebensohn, Wolfgang Ludwig, Yoann Guilhem, Henry Proudhon
|On-Site Speaker (Planned)
Identifying the small crack (SC) driving force of polycrystalline engineering alloys is critical to correlate the inherent microstructure variability and the intrinsic SC growth rate’s scatter observed during stage I. Due to recent experimental advancements, “cycle-by-cycle” data of a SC propagating through a beta-metastable titanium alloy are available via phase and diffraction contrast tomography. To compute micromechanical fields not available from the experiment, FFT-based crystal plasticity simulations are utilized. Results of the experiment and simulations are combined in a single dataset and sampled utilizing non-local mining technique. Sampled data are analyzed into a machine learning Bayesian Network framework to identify statically relevant correlations between state variables, microstructure features, location of the crack front, and experimentally observed growth rate, in order to postulate a data-driven, non-parametric SC driving force. Results are presented with a particular focus on the correlation between well-established SC driving forces and the proposed new driving force metric.
||Planned: Stand-alone book in which only your symposium’s papers would appear (indicate title in comments section below)