Representative volume element based micromechanical modelling of rod shaped glass filled epoxy composites

Abstract

In this study, Representative Volume Element based micromechanical modeling technique has been implemented to assess the mechanical properties of glass filled epoxy composites. Rod shaped glass fillers having an aspect ratio of 80 were used for preparing the epoxy composite. The three-dimensional unit cell model of representative volume element was prepared with finite element analysis tool ANSYS 19 using the periodic square and hexagonal array with an assumption that there is a perfect bonding between the filler and the epoxy matrix. Results revealed that the tensile modulus increases and Poisson’s ratio decreases with increase in the volume fraction of the filler. To study the effect of filler volume fraction, the pulse echo techniques were used to experimentally measure the tensile modulus and Poisson’s ratio for 5% to 15% volume fraction of the filler. A good agreement was found between the RVE based predicted values and the experimental results.

A review on performance of near‐surface mounted‐carbon fiber‐reinforced polymer laminates bonded into slits

“…[ 50 ] The FEA analysis has continuously been used to observe structures of various geometries and aspect ratios to obtain maximum stiffness of strengthened elements. [ 93,130,131 ]…”

“…[86] In recent years, the ML has provided many alternative applications to analytical and empirical methods in composites, Geotech, and medical sectors. [87][88][89][90][91][92][93][94][95][96] The wide data of pullout tests for the NSM-CFRP bond strength are used in the predictive modeling developed on the artificial neural network (ANN) approach. [86] The model further maps the diverse data arrangement from the NSM-CFRP to the concrete joint.…”

“…[50] The FEA analysis has continuously been used to observe structures of various geometries and aspect ratios to obtain maximum stiffness of strengthened elements. [93,130,131] The effect of low groove spacing in the NSM technique is found to influence the bond activity in the nearer grooves/FRP strips. [132] The influence of bond activity due to closer cuts must be taken into account for a safe design of the NSM FRP technique.…”

Reviewing role of biochar in controlling soil erosion and considering future aspect of production using microwave pyrolysis process for the same

“…In recent years, there has been enormous advancement in Artificial and Machine Learning in various engineering fields such as geotechnical [8,11,47], material development [164,165] and failure characterization [166][167][168] etc. Furthermore, there are several other advanced numerical techniques which are capable of modelling the material behaviour effectively [165,[168][169][170][171][172][173]. These advance numerical techniques can be augmented with the experiments to optimize the MAP process for the desired optimum properties of biochar with a higher accuracy.…”

A Multi-Objective Optimization of Neural Networks for Predicting the Physical Properties of Textile Polymer Composite Materials

“…Using microstructural images as the input, the model visualizes the stress components, specifically , with high accuracy. The training data were obtained from the FEM analysis of short carbon fiber-filled specimens using a Representative Area Element (RAE) approach [ 50 ]. The study demonstrated the robustness of a pix2pix [ 51 ] deep learning Convolutional Neural Network (CNN) model in predicting the stress fields.…”