Volume 36 Issue 6
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Tan Yang, Chi Yilin, Huang Yayu, Yao Tingqiang. An internal state variable viscoplastic constitutive model considering the evolution of microstructural characteristic length[J]. Explosion And Shock Waves, 2016, 36(6): 869-875. doi: 10.11883/1001-1455(2016)06-0869-07
Citation: Tan Yang, Chi Yilin, Huang Yayu, Yao Tingqiang. An internal state variable viscoplastic constitutive model considering the evolution of microstructural characteristic length[J]. Explosion And Shock Waves, 2016, 36(6): 869-875. doi: 10.11883/1001-1455(2016)06-0869-07

An internal state variable viscoplastic constitutive model considering the evolution of microstructural characteristic length

doi: 10.11883/1001-1455(2016)06-0869-07
  • Received Date: 2015-04-09
  • Rev Recd Date: 2016-10-08
  • Publish Date: 2016-11-25
  • During high strain rate and large strain deformation of crystalline metals, there exist phenomena of continuous refinement of microstructural characteristic length, like the size of dislocation cells, which occurs intensively and would have significant influence on the work hardening and macroscopic flow stress. In this work, based on the inverse relation between macroscopic flow stress and and the cell size, a new type of BCJ constitutive model was proposed. The flow rule and evolution equations for internal state variables in BCJ model were modified by involving the cell size parameter; the evolution equation for the cell size considering the dependence of the strain rate and the temperature was introduced into the model; and an internal state variable viscoplastic constitutive model that considers the evolution of microstructural characteristic length, accumulation and annihilation of dislocations was then established. The new constitutive model was illustrated by predicting the experimental stress-strain data of OFHC Cu over a wide range of strain rates (10-4 -103 s-1), temperatures (298-542 K) and strains (0-1). The results show that the predicted data agree very well with the experimental data. Compared with the BCJ model, the predictive accuracies of the proposed model in various loading conditions are obviously improved, the maximum average relative error is reduced from 9.939% to 5.525%.
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