With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

High-entropy alloys (HEAs) stand out from multi-component alloys due to their attractive
microstructures and mechanical properties. In this investigation, molecular dynamics (MD) …

The current paper presents a machine learning method based on artificial neural network
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …

The purpose of this work is the development of a trained artificial neural network for
surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures …

Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide
applications in the automotive industry. A basic plasticity and fracture characterization of a …

High entropy alloys (HEAs) are potential materials for coating due to their high hardness and
wear resistance. This study builds molecular dynamics models to explore the mechanical …

We compare two machine-learning-based approaches, artificial neural network (ANN) and
micromechanical model with automatic Bayesian identification of the model parameters, in …

This research characterizes the dynamic hardening behavior of an aluminum alloy sheet of
5182-O for the coupling effect of strain rate and temperature. Tests are carried out for …

This paper describes the synthesis of tungsten-containing high-entropy alloys (HEAs). The
synthesis method involves a powder metallurgy process, and spark plasma sintering (SPS) …

TSV-Cu is widely used for chip interconnects, where high testing costs and complex crystal
plasticity finite element (CPFE) limit the research of its deep microplastic evolution process …