Mohammad Refatul Islam's blog

The mechanical behavior of athermal random fiber networks embedding particulate inclusions is studied in this work. Composites in which the filler size is comparable with the mean segment length of the network are considered. Inclusions are randomly distributed in the network at various volume fractions, and cases in which fibers are rigidly bonded to fillers and in which no such bonding is imposed are studied separately.

Many materials of everyday use are fibrous and their strength is important in most applications. In this work we study the dependence of the strength of random fiber networks on structural parameters such as the network density, cross-link density, fiber tortuosity, and the strength of the inter-fiber cross-links. Athermal networks of cellular and fibrous type are considered. We conclude that the network strength scales linearly with the cross-link number density and with the cross-link strength for a broad range of network parameters, and for both types of networks considered.

We study the mechanical behavior of mycelium composites reinforced with biodegradable agro-waste particles. In the composite, the mycelium acts as a supportive matrix which binds reinforcing particles within its filamentous network structure. The compressive behavior of mycelium composites is investigated using an integrated experimental and computational approach.

Mohammad Refatul Islam, Catalin Picu - Journal of Applied Mechanics, 2018

We proposed a computational methodology for generating microstructure models of random composites with cylindrical or sphero-cylindrical inclusions having high volume fraction and broad aspect ratio distribution. The proposed methodology couples the random sequential adsorption (RSA) algorithm and dynamic finite element (FE) simulations. It uses RSA to generate sparse inclusion assemblies of low volume fraction and subsequently utilizes dynamic FE simulation for inclusion packing to achieve high volume fractions.

This paper presents an effective numerical approach for welding process
parameter optimization to minimize weld-induced distortion in
structures. A numerical optimization framework based on coupled Genetic
Algorithm (GA) and Finite Element Analysis (FEA) is developed and
implemented for a low and a high fidelity model. Classical weakly
coupled thermo-mechanical analysis with thermo-elasto-plastic
assumptions is carried out for distortion prediction of numerical
models. The search for optimum process parameters is executed by direct
integration of numerical models and GA-based optimization technique. The
developed framework automatically inserts the process parameters into
the simulation models, executes the FE-based welding simulations and