Naji AlMashrafi
Graduate Researcher
Computational Materials Science and Engineering
University of Kentucky
Metallic Foam
Ligament aspect ratio effects on elastic properties of porous network materials
Porous network materials, specifically solid foam-based, are widely used in applications requiring lightweight and energy-dissipating properties. However, these materials present unique challenges due to their inherent structural randomness. To address these complexities, I utilized a high-throughput computational tool designed to model the microstructure using physics-based geometry seed description and compute the properties by solving boundary value problem via FEM. I investigated how specific geometric factors—beyond reduced density—affect the mechanical properties of the metal foam. Specifically, I explored how the aspect ratio of ligaments within node-ligament geometries influences the elastic response of the material. Results suggest that changes in aspect ratio of ligaments do affect the stiffness of the material, but this effect cannot be separated from changes in the reduced density of the porous material. These insights help in determining more accurate safety of factors, leading to optimized designs that maintain structural integrity while reducing material costs—an essential balance in high-performance industries such as aerospace and automotive.
Polycrystal
Predicting Failure via Grain Boundary Rupture Using a Stochastic FEM-based Approach
Microstructural complexity in additively manufactured materials makes prediction of the location and critical stress for failure initiation difficult. Here we explore correlations between local structural features and effective applied stresses necessary to initiate grain boundary rupture. Leveraging the Kentucky Random Structures Toolkit (KRaSTk) to generate model representative volume elements (mRVEs) from physically-motivated geometric seed descriptions combined with FEM, we quantify both bulk mechanical responses and localized fracture initiation in polycrystals. Specifically, we examine distributions of stresses on grain boundaries across stochastic sets of mRVEs to deduce (micro)structure-property relationships. Results demonstrate a strong correlation between specific microstructural features and probability of grain boundary rupture, providing insights into failure initiation and demonstrating a computational framework for predicting failure stresses in complex polycrystals.
Load Sensing
FEA-Guided Load Sensing for Underground Mining Shuttle Cars
Underground mining shuttle cars operate under highly variable loading conditions that complicate automation, payload estimation, and structural health monitoring. In this project, I developed a finite element–driven framework to identify mechanically sensitive locations for load sensing on a shuttle car wheel housing provided by Komatsu. Starting from manufacturer CAD geometry, I constructed a high-fidelity FEA model that captured realistic boundary conditions and multiple operational load cases, including vertical payload loading, non-axial and angled contact forces, and combined loading scenarios representative of uneven terrain and turning events. Rather than relying on strain measurements, I focused on extracting reaction forces transmitted through bolted interfaces, demonstrating that these forces exhibit stable, approximately linear relationships with applied loads. By systematically analyzing how load paths shift under different loading ratios and directions, I showed that bolt interfaces provide an efficient and repeatable signal for payload estimation, while stress hot spots elsewhere in the structure are highly load-dependent and less reliable. This work establishes a physics-based methodology for placing through-hole load sensors in underground mining equipment, directly linking real-world vehicle loads to measurable reaction forces and enabling safer, more reliable automation-ready sensing solutions.
About
I am passionately driven to solve technologically-relevant challenges by addressing fundamental materials design and processing questions using integrated multi-scale computational techniques and materials characterization. Always seeking opportunities to drive innovation and to apply multi-scale materials modeling in mechanical design.
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