|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Advanced Characterization Techniques for Quantifying and Modeling Deformation
||BB-10: Microstructurally-Short Crack Growth Driving Force Identification: Combining DCT, PCT, Crystal Plasticity Simulations and Machine Learning Technique
||Andrea Rovinelli, Michael D Sangid, Ricardo A Lebensohn, Wolfgang Ludwig, Yoann Guilhem, Henry Proudhon
|On-Site Speaker (Planned)
Identifying the Microstructurally-Short Crack (MSC) growth driving force of polycrystalline engineering alloys is a critical need in assessing performances of materials subject to fatigue load and to improve both material design and component life prediction. However, due to (i) the lack of “cycle-by-cycle” experimental data, (ii) the complexity of MSC growth phenomenon, and (iii) the incomplete physics of constitutive relationships, only simple driving force metrics, inadequate to predict MSC growth, have been postulated. Based on experimental results by Ludwig, Guilhem, et al., “cycle-by-cycle” data of a MSC propagating through a beta-metastable titanium alloy are available via phase and diffraction contrast tomography. To identify the crack driving force, we developed a framework utilizing the aforementioned experimental results and FFT-based crystal plasticity simulations (to compute micromechanical fields not available from the experiment). These results are combined and converted into probability distributions for use in a Bayesian Network.
||Planned: EPD Congress Volume