Dynamic Constitutive Behavior of Ultrafine-Grained Pure Titanium Based on Modified J-C and BP Artificial Neural Network Model
To study the intricate mechanical behavior of ultrafine-grained(UFG)pure titanium under high temperature and high strain rate loading,a model that can accurately describe its dynamic mechanical behavior was established.The dynamic impact test of UFG pure titanium was carried out at loading temperatures of 300-450 ℃ and strain rates of 2000-3000 s-1,the true stress-strain curves were also obtained.The results show that under the studied conditions,the true stress-strain curves show obvious double stress peaks,the annihilation and rearrangement of dislocations at grain boundaries and the subsequent formation of adiabatic shear bands(ASB)are the main factors for the two stress reduction.UFG pure titanium shows positive strain rate sensitivity and negative temperature sensitivity.Considering the strain hardening effect,strain rate hardening effect,and thermal softening effect,a modified Johnson-Cook(J-C)constitutive model and a BP artificial neural network(BP-ANN)model were proposed,and the accuracy of the two models was analyzed.It is found that the BP-ANN model can better predict the dynamic mechanical behavior of UFG pure titanium,the correlation coefficient can reach 0.970 65,and the average relative error(AARE)is only 4.63％.
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