Geometry Optimization of Sheet Specimen for the Measurement Accuracy Improvement in the Hopkinson Bar Based on Intelligent Algorithm

At present, the split Hopkinson tensile bar (SHTB) is widely used to determine the dynamic tensile properties of materials under high strain rates, in which sheet specimen with dogbone-shaped structure is commonly adopted. However, the geometry dimensions of the specimen used in different literatures vary widely and no uniform criterion has been formulated. In order to obtain the optimal specimen geometry associated with the best measurement accuracy in SHTB experiments, the specimen geometry influence on the measurement accuracy of SHTB experiments is investigated by using the finite element (FE) method, and several key indicators which can characterize the measurement accuracy of specimen are proposed based on simulation analysis. Orthogonal tests are designed to generate training samples for BP (back propagation) neural network, and the complex and highly nonlinear mapping between the structure parameters and measurement accuracy indicators of specimen is fitted by BP and then utilized for the fitness function design of genetic algorithm (GA). Finally, the optimal geometry as well dimensions of the SHTB sheet specimen are determined using GA. Meantime, the finite element simulations are carried out in further to verify the effectiveness of the optimized geometry of specimen. The results of this investigation will provide a recommendation for specimen geometry design and a basis for data reliability analysis in SHTB experiments.