Parameter range estimation and uncertainty analysis for high strain rate material response via model fusion

The dynamic stress–strain response of a material can be described by a number of different models of varying fidelity. However, an individual model’s ability to replicate the dynamic stress–strain response of a material can be hindered by experimental variability. Reification, an approach to fusing models and experimental data with inherent scatter, is presented. It is then used to determine the optimum parameters of the Johnson–Cook (JC) and Zerilli–Armstrong (ZA) models using a fusion of Split-Hopkinson Pressure Bar (SHPB) data and the JC and ZA models fit to the SHPB data using a traditional approach. The output of the fused model is a dataset that represents a “best-guess” sampling of the possible stress–strain response of a high strength steel. In the present work, the dynamic response of a newly developed steel, AF9628, is evaluated. Under the reification framework, the experimental variability and limitations of the mathematical model expressions are addressed by the optimized sampling of data and combined fitting process. The JC and ZA models are then re-fit to partitions of the fused dataset, which bound the responses of the traditionally fit JC and ZA models. The behavior of the re-fit models and the traditionally fit models are compared via a simulated Taylor anvil test.