Khalil Khanafer

This study investigates the fatigue behavior of 3D-printed ABS subjected to cyclic torsional loads, with a focus on the effects of infill density and raster angle on torsional fatigue performance. A total of 50 test specimens representing 25 unique combinations of infill density (20%, 40%, 60%, 80%, 100%) and raster angle (25°/−65°, 45°/−45°, 75°/−15°, 0°/90°) were fabricated and tested using the cyclic torsion system. Fatigue failure was defined as a 75% reduction in torsional strength, recorded through cycle-by-cycle torque monitoring. The twist angle was cyclically varied between ±10° at a frequency of 5 Hz until failure occurred. The results indicate that increasing infill density significantly improves fatigue life by reducing internal porosity and enhancing load transfer, with the greatest gains observed at high infill levels (≥80%). Raster angle has a minimal effect at low infill densities but becomes critical at higher densities, where optimized filament orientations substantially extend fatigue life. Intermediate raster angles, particularly 25° and 75°, outperform orthogonal layouts by enabling better stress redistribution and inter-layer load sharing, while a 90° orientation leads to premature failure due to stress concentration and inter-layer debonding. When normalized by mass, specimens with 100% infill and intermediate raster angles achieve the highest fatigue endurance, highlighting the synergistic role of infill density and raster orientation in optimizing the durability and mass efficiency of 3D-printed components under cyclic torsional loading.

This paper discusses the potential application of computational design and additive manufacturing in veterinary prosthetics, demonstrated with the example of a feline limb. A finite element analysis (FEA)-based design optimization framework was used to develop eight prosthetic leg geometries in CAD and analyze them in ANSYS (2024R) under a static loading condition, to evaluate their performance under the loading condition. Structural variations were in the form of grooves, holes and shell reinforcements, which were investigated to see how they affect stiffness, stress distribution and viability of the 3D-printed prosthetics. Design variations were introduced through the inclusion or exclusion of structural attributes such as grooves, holes, and shell reinforcements. The simulations evaluated total deformation, von Mises stress, and equivalent elastic strain to determine the mechanical efficiency of each configuration. Results showed that designs incorporating shells and holes provided the best overall performance, as groove-free designs minimized stress concentrations and achieved the highest stiffness. Notably, the configuration featuring a hole and shell without grooves achieved the lowest mean deformation (14.09 µm) and stress values, making it the most structurally viable and suitable for real-life application. This study highlights the potential of 3D printing to produce cost-effective, patient-specific prosthetics and underscores the importance of structural optimization for improving biomechanical compatibility and mobility. The next stage of this work will involve fabricating the optimized design using 3D printing, followed by mechanical testing to validate the simulation results and assess real-world performance. Future work will also incorporate topology optimization to further reduce weight while maintaining structural integrity.

This study investigates the mechanical performance of two prosthetic forelimb designs for dogs—one with a solid structure and the other with a perforated structure—using Finite Element Analysis (FEA). Both models were analyzed under static loading conditions representing approximately 60% of a dog’s body weight, the typical load borne by the forelimbs. The prosthetics were modeled with ABS plastic, a widely used 3D printing material, and evaluated for Von Mises stress, total deformation, elastic strain, and factor of safety. The analysis showed that both models remained within the elastic limit of the material, indicating that no permanent deformation would occur under the applied loads. The Solid Model demonstrated a significantly higher factor of safety (14) and lower deformation, confirming its structural strength but also highlighting excessive rigidity, increased material use, and higher cost. In contrast, the Perforated Model exhibited slightly higher localized stresses and a lower factor of safety (3.01), yet it still met essential safety requirements while providing greater compliance, flexibility, and material efficiency. These attributes are desirable for comfort, adaptability, and practicality in veterinary applications. Although its long-term durability requires further evaluation, the Perforated Model strikes a more effective balance between safety, comfort, and sustainability. Based on these findings, the perforated design is considered the more suitable option for canine prosthetic development. Future work will extend the analysis to dynamic loading scenarios, such as walking and running, to better simulate real-world performance.

This study investigates the mechanical behavior of ASTM D638-02a standard uniaxial tensile test specimens fabricated from 3D-printed acrylonitrile butadiene styrene (ABS) using fused deposition modeling (FDM) with a grid infill pattern at varying densities of 20%, 40%, 60%, and 100%. The research aims to provide a deeper understanding of how infill density influences the mechanical properties of FDM-printed ABS, an area critical for optimizing structural performance in additive manufacturing applications. Experimental uniaxial tensile tests reveal that as the infill density increases from 20% to 60%, the strain at break decreases from 4.7% to 3.9%; however, at 100% infill, the strain at break rises to 5.8%. Meanwhile, the average Young’s modulus exhibits an exponential increase from 513.78 MPa at 20% infill to 2394.8 MPa at full density, indicating greater stiffness with higher infill. Due to the inherent nonlinear elastic deformation of 3D-printed ABS, this study further explores the material’s behavior through finite element analysis (FEA) using Ansys Mechanical. Four hyperelastic material models—Neo-Hookean, Mooney–Rivlin (two-parameter), Mooney–Rivlin (three-parameter), and Yeoh (third order)—were evaluated using inverse analysis to determine material constants. The results indicate that while all models exhibit good correlation with experimental data, the three-parameter Mooney–Rivlin and Yeoh models achieve the highest accuracy (higher R² values) across all infill densities. However, the Neo-Hookean model, despite being a single-parameter approach, demonstrates a consistent trend where its parameter value increases with infill density. This study provides novel insights into the nonlinear elastic properties of 3D-printed ABS and establishes a foundation for selecting appropriate hyperelastic models to accurately predict mechanical behavior in FDM-printed structures.

A numerical study focuses on the temporal evolution of fractional-order convective nanofluid flow along with entropy generation characteristics within a wavy square porous enclosure containing a circular cylinder. The application of fractional derivatives facilitates a more accurate representation of fluid flow dynamics, thermal transport, and entropy production. The governing equations are formulated as fractional partial differential equations, with momentum transport modeled using the Darcy–Brinkman–Forchheimer approach. The complete mathematical framework is solved using a robust numerical technique that integrates the implicit finite difference scheme (L1-scheme) for temporal discretization and the penalty finite element method for spatial discretization. The numerical investigation is carried out for various emerging parameters, including fractional-order parameters (α), Rayleigh number (Ra), Darcy number (Da), and porosity (ε). The results are displayed through contour plots of streamlines, isotherms, and local entropy generation, along with graphical plots of the mean Nusselt number, Bejan number, and total entropy generation. These findings offer valuable insights into the interplay between fractional-order parameter and flow parameters in influencing flow dynamics, thermal transport, and entropy generation. The study reveals that the fractional-order parameter (α) plays a pivotal role in governing the system's temporal evolution, with higher values of α significantly accelerating the rate of evolution.

This study investigates the dynamic fluid–structure interactions and thermal performance of wall-mounted flexible vertical microcantilevers in a confined channel under varying flow conditions. The results reveal that the microcantilevers exhibit significant deformation in response to changes in flow velocity, aligning with the dominant flow direction to minimize resistance and stabilize the system. The interaction between microcantilever deflection and fluid flow leads to the formation of vortices in the wake region, with their intensity modulated by the velocity profile and microcantilever positioning. The thermal analysis highlights the interplay of conduction and convection, with conduction dominating at low Reynolds numbers and convection becoming prevalent at higher flow velocities. The tip deflection of the microcantilevers and the temporal average Nusselt number at the top wall are found to increase significantly with Reynolds number, revealing enhanced fluid–structure interaction and convective heat transfer efficiency. For instance, the tip deflection and temporal average Nusselt number increase by 90.4% and 63.4%, respectively, as the Reynolds number rises from 50 to 100. Channel geometry also plays a critical role; reduced channel heights amplify horizontal velocity, shear stresses, and heat transfer, emphasizing the influence of geometric constraints on system dynamics. The introduction of a second vertical microcantilever and variations in microcantilever spacing reveal negligible effects on the temporal average Nusselt number at the top wall but complex structural responses, such as increased backward deflection during reverse flow. These findings provide insights into optimizing microfluidic systems, heat exchangers, and other applications involving fluid–structure interactions. This study highlights the importance of coupling structural flexibility with fluid dynamics to achieve superior mechanical and thermal performance under varying operating conditions.

Additive manufacturing (AM) or 3D printing technology creates a tangible object by adding successive layers of materials. Nowadays, 3D printing is used for developing both metal and non-metal products. In the advancement of 3D printing technology, material specimen design, modification, and testing become very simple, especially for non-metal materials, such as hyperelastic, thermoplastic, or rubber-like materials. However, proper material modeling and validation are required for the analysis of these types of materials. In this study, 3D printed poly lactic acid (PLA+) material behavior is analyzed numerically for validation in the counterpart of experimental analysis to evaluate their behavior in both cases. The specimen was designed in SolidWorks by following ASTM D638 dimension standards with proper infill densities and raster angle or infill orientation angle. These infill layer densities and angles of orientation play an important role in the mechanical behavior of the specimen. This paper aims to present a numerical validation of five infill densities (20%, 40%, 60%, 80%, and 100%) for a ±45-degree infill angle orientation by incorporating a nonlinear hyperelastic model. Results indicate that infill densities affect the mechanical behavior of PLA+ material. The result also suggested that neo-Hookean and Mooney–Rivlin are the best-fitted hyperelastic material models for these five separate linear infill densities. However, neo-Hookean is easier to analyze, as it has only one parameter and a new equation is developed in this study for determining the parameter for different infill densities.

Machine learning-based predictions of heat transfer characteristics in lid-driven cavities are transforming the field of computational fluid dynamics (CFD). Lid-driven cavities are a fundamental problem in fluid mechanics, characterized by the motion of a fluid inside a square cavity driven by the motion of one of its walls. The goal of this study was to develop multiple machine-learning regression models and highlight the discrepancies between the predicted and actual average Nusselt numbers. Additionally, the study utilized physics-informed neural networks (PINNs) to model the flow and thermal behavior at both low and high Reynolds numbers. The results were compared among actual data from computational fluid dynamics (CFD) simulations, PINN models trained with CFD data, and purely PINN models created without any prior data input. The findings of this study showed that the random forest model exhibited an exceptional stability in its predictions, consistently maintaining low errors even as the Reynolds number increased compared with other machine-learning regression models. Further, the results of this study in terms of flow and thermal behavior within the cavity were found to depend significantly on the PINN method. The data-driven PINN exhibited a much lower mean average errors at both Reynolds numbers, while the physics-based PINN showed lower physics loss.