|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||A Reification Approach to Modeling Material Response by Fitting Johnson Cook Parameters
||Jaylen James, Austin Gerlt, Manny Gonzales, Eric Payton, Reji John, Ibrahim Karaman, Raymundo Arroyave, Douglas Allaire
|On-Site Speaker (Planned)
The dynamic stress-strain response of a material can be described by a number of different models of varying fidelity. However, the inherent fidelity of the model itself can be hindered by experimental variability. A reification approach is presented for fitting a model to experimental measurements with inherent scatter, and is demonstrated for determining optimum parameters of a Johnson-Cook (J-C) model. Quasi-static and high-strain-rate tensile and compression tests with nominally identical input parameters were performed and fused to obtain the reified stress-strain data. Next, an optimization was performed to determine the best fit J-C parameters for the fused model. The parameters of the fused model are used to simulate the upper and lower bounds of material response in simulated Taylor Anvil tests. The reified J-C model is then compared with the J-C model fitted to the mean response of the stress-strain experimental curves.
||Planned: Supplemental Proceedings volume