Spall Fracture of Solid and Molten Copper: Molecular Dynamics, Mechanical Model and Strain Rate Dependence

Abstract

In this study, we formulate a mechanical model of spall fracture of copper, which describes both solid and molten states. The model is verified, and its parameters are found based on the data of molecular dynamics simulations of this process under ultrahigh strain rate of tension, leading to the formation of multiple pores within the considered volume element. A machine-learning-type Bayesian algorithm is used to identify the optimal parameters of the model. We also analyze the influence of the initial size distribution of pores or non-wettable inclusions in copper on the strain rate dependence of its spall strength and show that these initial heterogeneities explain the existing experimental data for moderate strain rates. This investigation promotes the development of atomistically-based machine learning approaches to description of the strength properties of metals and deepens the understanding of the spall fracture process.

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

“…The study of the tensile loading of nanoporous metals along various deformation paths is insufficiently covered in the literature. This study is necessary for both a deep understanding of the dislocation nucleation and for further development of the mechanical models of dynamic (spall) fracture [48][49][50]. Activation of nanopore growth on pre-existing volumetric defects instead of homogeneous nucleation of nanopores is the main mechanism of fracture initiation at moderate strain rates as shown in [49,50].…”

“…This study is necessary for both a deep understanding of the dislocation nucleation and for further development of the mechanical models of dynamic (spall) fracture [48][49][50]. Activation of nanopore growth on pre-existing volumetric defects instead of homogeneous nucleation of nanopores is the main mechanism of fracture initiation at moderate strain rates as shown in [49,50]. Therefore, the influence of nanopores on the plasticity incipience is even more relevant.…”

“…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 fracture model are identified by means of a statistical Bayesian approach [50][51][52][53], using MD curves as the training data. 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.…”