|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Calibrating Strength Model Parameters using Multiple Types of Data
||Jeffrey Florando, Jason Bernstein, Amanda Muyskens, Matthew Nelms, David Rivera, Kathleen Schmidt, Nathan Barton, Ana Kupresanin
|On-Site Speaker (Planned)
Bayesian calibration is a common method for estimating model parameters and quantifying their associated uncertainties; however, calibration becomes more complicated when the data arise from different types of experiments. In the case of material strength, additional types of data are often needed to access higher experimental strain rates. For strength models, it is desirable for parameter estimates to be valid across this range of regimes, especially if there is not expected to be a transition in the underlying physical mechanisms involved in the strength response. Here, we employ different types of data: stress-strain curves from low-strain rate experiments and deformed profiles from higher-strain rate experiments. In particular, we highlight data fusion techniques for incorporating different measurement types into Bayesian calibration and present our results for tantalum.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
||Planned: Supplemental Proceedings volume