Incipience of Plastic Flow in Aluminum with Nanopores: Molecular Dynamics and Machine-Learning-Based Description

Incipience of plastic flow in nanoporous metals under tension is an important point for the development of mechanical models of dynamic (spall) fracture. Here we study axisymmetric deformation with tension of nanoporous aluminum with different shapes and sizes of nanopores by means of molecular dynamics (MD) simulations. Random deformation paths explore a sector of tensile loading in the deformation space. The obtained MD data are used to train an artificial neural network (ANN), which approximates both an elastic stress-strain relationship in the form of tensor equation of state and a nucleation strain distance function. This ANN allows us to describe the elastic stage of deformation and the transition to the plastic flow, while the following plastic deformation and growth of pores are described by means of a kinetic model of plasticity and fracture. The parameters of this plasticity and fracture model are identified by the statistical Bayesian approach, using MD curves as the training data set. The present research uses a machine-learning-based approximation of MD data to propose a possible framework for construction of mechanical models of spall fracture in metals.

Automated summary

By studying the plastic flow behavior of nanoporous metals under tension using molecular dynamics simulations and artificial neural networks, a possible framework for constructing mechanical models of spall fracture in metals has been proposed.