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Estimating Primary Fragment Characteristics During Hypervelocity Impact of Spherical Fragment on Thin Plate Using Artificial Neural Network

GAURI NAIK, SATISH CHINCHANIKAR

Abstract


A complex phenomenon of hypervelocity impact shows changing trends in plate damage for range of impact condition. Empirical models predict damage in terms of hole size and characteristics of residual projectile, which is detrimental to cause further damage of next layer plate. In the present work, an attempt is made to develop artificial neural network (ANN) for predicting the characteristics of residual projectile in terms of mass ratio, velocity ratio and obliquity. The ANN predictions were compared with results obtained by simulations with were validated with experimental results in the literature. It is observed that predictions done by ANN are in good agreement with simulation results.


DOI
10.12783/ballistics2019/33194

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