Dynamic compaction of aluminum with nanopores of varied shape: MD simulations and machine-learning-based approximation of deformation behavior

FT Latypov and EV Fomin and VS Krasnikov and AE Mayer, INTERNATIONAL JOURNAL OF PLASTICITY, 156, 103363 (2022).

DOI: 10.1016/j.ijplas.2022.103363

We compare two machine-learning-based approaches, artificial neural network (ANN) and micromechanical model with automatic Bayesian identification of the model parameters, in application to mimicking the deformation behavior of nanoporous aluminum extracted from molecular dynamics (MD) simulations. Reference data are generated by means of MD simulation of both hydrostatic and uniaxial deformation with compression of representative volume elements of aluminum single crystal with nanopores of spherical, cubic and cylindrical shapes at the temperatures of 300, 500, 700 and 900 K. Several typical sizes of the nanopores are considered: The smallest one corresponds to the initial porosity less than 1%, while the largest one gives the initial porosity in the range of 30-50%. Plastic collapse of nanopores in all cases occurred by the mechanism of emission of partial Shockley dislocation half-loops from the pore surface. The emission of initial loop occurred earlier in the case of cylindrical pore; however, this did not lead to an explosive increase in the number of dislocations in the system at this stage. In general, flat free surfaces of pores are less subjected to the dislocation nucleation than the rounded ones. A new physically-based micromechanical model of the plastic compaction of nanoporous metal is formulated with accounting of different pore shapes and anisotropy of the compaction process. Both tested machine-learning approaches show an adequate approximation of MD data. The developed ANN and parameterized micromechanical model are applied to simulate the propagation of a shock wave in nanoporous aluminum in comparison with direct MD simulations of this process; this comparison shows an adequate description of the shock wave structure by means of both approaches incorporated into continuum mechanics modeling. Thus, the developed machine-learning-based approaches can be applied as constitutive equations of nanoporous aluminum in macroscopic simulations of the dynamic compaction and shock- wave processes in this material.

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