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Current high strain rate testing techniques typically rely on the split‐Hopkinson bar (SHB). The early response in an SHB test is corrupted by inertia making it difficult to accurately characterise the transition from elasticity to plasticity for metals. Therefore, a new test method is required. This article is the second in a two‐part series which aims at developing a new high strain rate test for elasto‐plasticity identification using the image‐based inertial impact (IBII) method. The goal of this article is to validate the new method experimentally using IBII tests on aluminium 6082‐T6 (minimal rate sensitivity) and stainless steel 316L (rate sensitive). Comparison of the quasi‐static and dynamic stress–strain curves for the aluminium case showed minimal difference providing experimental validation of the method. The same comparison for the steel showed that the method was able to detect rate sensitivity.

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Current high strain rate testing procedures generally rely on the split Hopkinson bar (SHB). In order to gain accurate material data with this technique, it is necessary to assume the test sample is in a state of quasi‐static equilibrium so that inertial effects can be neglected. During the early portion of an SHB test, it is difficult to satisfy this assumption making it challenging to investigate the elastic–plastic transition for metals. With the development of ultra‐high speed imaging technology, the image‐based inertial impact (IBII) test has emerged as an alternative to the SHB. This technique uses full‐field measurements coupled with the virtual fields method to identify material properties without requiring the assumption of quasi‐static equilibrium. The purpose of this work is to develop the IBII method for the identification of elasto‐plasticity in metals. In this paper (part 1), the focus is on using synthetic image deformation simulations to analyse identification errors for two plasticity models, a simple linear hardening model and a modified Voce model. Additionally, two types of virtual fields are investigated, a simple rigid body virtual field and the recently developed sensitivity‐based virtual fields. The results of these simulations are then used to select optimal processing parameters for the experimental data analysed in part 2.

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