Abstract
Slow, intermediate and high strain rate experiments with UT geometries are performed on aluminum AA7075-T6 sheet metal at various temperatures. The comprehensive experimental program characterizes the plasticity response at temperatures ranging from 20°C to 360°C and at strain rates ranging from 0.001/s to 150/s. The elevated temperature - elevated strain rate experiments are performed on a hydraulic tensile testing machine and a Split Hopkinson Pressure Bar system with a Load Inversion Device along with a custom-made induction heating system. A machine learning based modified Johnson-Cook plasticity model is calibrated to capture the complex strain rate and temperature effect of the observed hardening response.
Highlights
There is a constant quest to better understand and model the effect of strain rate and temperature driven by industrial problems including metal forming, machining, accidental loading or high-speed impact, amongst others
Experiments at high loading speeds are conducted on a Split Hopkinson Pressure Bar (SHPB) system with a Load Inversion Device (LID) for tensile testing
The deformation resistance is decomposed into a reference mixed Swift-Voce strain hardening, as well as a neural network term describing the effect of strain rate and temperature
Summary
There is a constant quest to better understand and model the effect of strain rate and temperature driven by industrial problems including metal forming, machining, accidental loading or high-speed impact, amongst others. Aluminium alloy AA 7xxx series has seen particular interest for impact protection systems [1] These alloys often feature minimum strain rate effect at room temperature [2] but high strain rate dependency at elevated temperatures [3]. To characterize these complex effects, many phenomenological models have been developed, most notably the Johnson-Cook (JC) model [4]. The performance of the NN model is compared to that of a classic JC model to highlight the flexibility and accuracy of the NN modelling
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