Development of an ABAQUS Plugin Tool for Periodic RVE Homogenisation

Open Access through Springer Compact Agreement.Peer reviewedPublisher PD

Development of a Multi-scale Surrogate-based Tool for Composite Property Estimation

Peer reviewedPublisher PD

A Fractional Viscoelastic Model Of The Axon In Brain White Matter

Traumatic axonal injury occurs when loads experienced on the tissue-scale are transferred to the individual axons. Mechanical characterization of axon deformation especially under dynamic loads however is extremely difficult owing to their viscoelastic properties. The viscoelastic characterization of axon properties that are based on interpretation of results from in-vivo brain Magnetic Resonance Elastography (MRE) are dependent on the specific frequencies used to generate shear waves with which measurements are made. In this study, we aim to develop a fractional viscoelastic model to characterize the time dependent behavior of the properties of the axons in a composite white matter (WM) model. The viscoelastic powerlaw behavior observed at the tissue level is assumed to exist across scales, from the continuum macroscopic level to that of the microstructural realm of the axons. The material parameters of the axons and glia are fitted to a springpot model. The 3D fractional viscoelastic springpot model is implemented within a finite element framework. The constitutive equations defining the fractional model are coded using a vectorized user defined material (VUMAT) subroutine in ABAQUS finite element software. Using this material characterization, representative volume elements (RVE) of axons embedded in glia with periodic boundary conditions are developed and subjected to a relaxation displacement boundary condition. The homogenized orthotropic fractional material properties of the axon-matrix system as a function of the volume fraction of axons in the ECM are extracted by solving the inverse problem.Comment: Accepted for publication at the 12th International Conference on Mathematical Modeling in Physical Science

Representative volume element (Rve) analysis for mechanical characterization of fused deposition modeled components

Additive manufacturing processes have evolved considerably in the past years, growing into a wide range of products through the use of different materials depending on its application sectors. Nevertheless, the fused deposition modelling (FDM) technique has proven to be an eco-nomically feasible process turning additive manufacture technologies from consumer production into a mainstream manufacturing technique. Current advances in the finite element method (FEM) and the computer-aided engineering (CAE) technology are unable to study three-dimensional (3D) printed models, since the final result is highly dependent on processing and environment parame-ters. Because of that, an in-depth understanding of the printed geometrical mesostructure is needed to extend FEM applications. This study aims to generate a homogeneous structural element that accurately represents the behavior of FDM-processed materials, by means of a representative volume element (RVE). The homogenization summarizes the main mechanical characteristics of the actual 3D printed structure, opening new analysis and optimization procedures. Moreover, the linear RVE results can be used to further analyze the in-deep behavior of the FDM unit cell. Therefore, industries could perform a feasible engineering analysis of the final printed elements, allowing the FDM technology to become a mainstream, low-cost manufacturing process in the near future

modeling the influence of stress triaxiality on the failure strain of nodular cast iron microstructures

Abstract In this study the fracture behavior of different cast iron microstructures subjected to tensile loading under different triaxialities is simulated by a finite element, 3-D Reference Volume Element approach. Three ferritic/pearlitic heterogeneous matrixes are considered which are representative of the class material grades for strength and ductility. Isotropic ductile and shear damage models are considered for the matrix constituents as concurrent damage mechanisms at the microscale, while graphite nodules are considered as voids acting as stress concentrators. Numerical results confirm experimental findings about local strain distribution and damage accumulation, and reproduce the engineering macroscopic behavior. The stress triaxiality is found to play a strong effect on the failure strain, extending the potentialities of this RVE modeling approach

Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 μm to 1.5 μm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs

A finite element based orientation averaging method for predicting elastic properties of short fiber reinforced composites

Short fiber reinforced composites have a variety of micro-structural parameters that affect their macro-mechanical performance. A modeling methodology, capable of accommodating a broad range of these parameters, is desirable. This paper describes a micro-mechanical model which is developed using Finite Element Analysis and Orientation Averaging. The model is applicable to short fiber reinforced composites with a wide variety of micro-structural parameters such as arbitrary fiber volume fractions, fiber aspect ratios and fiber orientation distributions. In addition to the Voigt and Reuss assumptions, an interaction model is developed based on the self-consistent assumption. Comparisons with experimental results, and direct numerical simulations of Representative Volume Elements show the capability of the model for fair predictions