This contribution presents a new physics-informed deep homogenization neural network model for identifying local displacement and stress fields, as well as homogenized moduli, of nanocomposites with periodic arrays of porosities under general loading conditions. Notably, it accounts for the surface elasticity effect, utilizing the Gurtin-Murdoch in...
It is well known that dislocation slip plays a major role in plastic deformation of polycrystals. Depending on the crystal’s symmetry, only a limited number of Slip Systems (SSs) are possible, and their activities depend on the crystal orientation with respect to the applied stress. High Resolution Digital Image Correlation (HR-DIC) can be used to ...
An extensive study of size effects on the small-scale behavior of crystalline materials is carried out through discrete dislocation dynamics (DDD) simulations, intended to enrich strain gradient crystal plasticity (SGCP) theories. These simulations include cyclic shearing and tension-compression tests on two-dimensional (2D) constrained crystalline...
This paper aims to assess the effect of layer thickness on the elastoplastic properties of the constituent materials of multilayer coating systems, as well as on the stress and strain fields in the vicinity of the coating/substrate interface. A methodology based on a trust-region reflective optimization algorithm, integrated with finite element ana...
We present a novel elasticity-inspired data-driven Fourier homogenization network (FHN) theory for periodic heterogeneous microstructures with square or hexagonal arrays of cylindrical fibers. Towards this end, two custom-tailored networks are harnessed to construct microscopic displacement functions in each phase of composite materials, based on t...
Industrial sheet molding compound (SMC) composite structures are susceptible to environmental degradation, primarily from moisture and temperature. Furthermore, these materials are subjected to fatigue loading. It is therefore necessary to generate Wohler curves for a range of service conditions, taking into account exposure time and temperature. G...
Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To thi...
We present an adaptive physics-informed deep homogenization neural network (DHN) approach to formulate a full-field micromechanics model for elastic and thermoelastic periodic arrays with different microstructures. The unit cell solution is approximated by fully connected multilayers via minimizing a loss function formulated in terms of the sum of ...
This article aims at proposing a new mean-field homogenization framework for the study of composites undergoing fully coupled thermomechanical processes. Strongly dissipative phenomena during high or moderate cyclic loading conditions in a structural component made of a composite material cause significant interplay between mechanical and thermal f...
Short Fiber Reinfored Thermoplastics (SFRTs) like PA66GF composites are highly heterogeneous materials with a rather complex microstructure such that the characterization and the prediction of their mechanical behavior remain quite challenging. So far, most of the research efforts have employed phenomenologigal and mean-field multi-scale models to ...
