|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Computational Methods and Experimental Approaches for Uncertainty Quantification and Propagation, Model Validation, and Stochastic Predictions
||Using Metropolis-Hasting Algorithm to Calibrate NiTi Precipitation Model Implemented in MatCalc© Code
||Pejman Honarmandi, Raymundo Arroyave, Luke Johnson
|On-Site Speaker (Planned)
Tailorability of transformation temperature (MS) due to Ni partitioning during precipitation of secondary phases makes NiTi shape memory alloys attractive in a number of applications. Therefore, the hard task of implementing and calibrating a physical precipitation model has been performed for this binary system in MatCalc©. Sensitive parameters have been found in this model through forward analysis. A Bayesian approach based on MCMC-Metropolis-Hastings algorithm has been selected to calibrate these model parameters. A relationship has been proposed for matrix/precipitate interfacial energy versus aging temperature and nominal composition, using the calibrations of interfacial energy besides the other parameters with each experimental data individually. After inserting this equation in the model, the other parameters have been calibrated with all experimental data together. Although the model results do not fit the data exactly, the data is located in model's 95% Bayesian confidence intervals.