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Meeting 2016 TMS Annual Meeting & Exhibition
Symposium Computational Methods for Uncertainty Quantification, Model Validation, and Stochastic Predictions
Presentation Title Uncertainty Quantification and Propagation for Validation of a Microstructure Sensitive Model for Prediction of Fatigue Crack Initiation
Author(s) Saikumar Reddy Yeratapally, Alberto W Mello, Michael D. Sangid, Mark Hardy, Michael G Glavicic; Saikumar Reddy Yeratapally, Alberto W Mello, Michael D. Sangid, Mark C. Hardy, Michael G Glavicic
On-Site Speaker (Planned) Saikumar Reddy Yeratapally
Abstract Scope A fatigue crack initiation and life prediction framework which links microstructure variability of a polycrystalline material to the scatter in fatigue life has been developed. The presence of input uncertainties demands validation of the failure predictive model before it can be used in a production environment. A data driven, Bayesian inference framework that uses Markov chain Monte Carlo algorithm, is used to quantify uncertainties and calibrate the model. Following Bayesian inferences, the identified input uncertainties are propagated through the model to calculate the distribution of fatigue life predictions, which can then be compared to experimental life data. Validation metrics are then used to assess the trustworthiness and the range of applicability of the model. Further, a sub-system level validation is also performed by comparing the heterogeneous strains calculated using crystal plasticity formulation (an integral part of the life prediction framework) to the experimental strain values obtained using digital image correlation.
Proceedings Inclusion? Planned: A print-only volume


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