Automated Split Hopkinson Bar

The integration of data-driven methods and machine learning is transforming research in high-strain rate mechanics of materials. Split Hopkinson Pressure Bar (SHPB) setups are widely used to characterize material behavior under high strain rates. However, generating the extensive datasets necessary for developing viscoplastic constitutive models remains a challenge. This study presents an automated SHPB experimental setup designed to enable high-throughput testing and data collection. To validate the system, 40 experiments were conducted on Copper 101 samples. The experimental data were processed using a newly developed automated analysis tool. Additionally, the experiments incorporated a full-field, high-speed digital image correlation (DIC) technique to capture detailed strain and deformation fields, providing deeper insights into material behavior under dynamic loading conditions.