Search Results (1,685)

Pelvic organ prolapse (POP) is a prevalent condition, affecting women all over the world, and is commonly treated through surgical interventions that present limitations such as recurrence or complications associated with synthetic meshes. In this study, biodegradable poly($ϵ$-caprolactone) (PCL) cog threads are proposed as a minimally invasive alternative for vaginal wall reinforcement. A custom cutting tool was developed to fabricate threads with varying barb angles (90°, 75°, 60°, and 45°), which were produced via Melt Electrowriting. Their mechanical behavior was assessed through uniaxial tensile tests and validated using finite element simulations. The results showed that barb orientation had minimal influence on tensile performance. In simulations of anterior vaginal wall deformation under cough pressure, all cog thread configurations significantly reduced displacement in the damaged tissue model, achieving values comparable to or even lower than those of healthy tissue. A ball burst simulation using an anatomically accurate model further demonstrated a 13% increase in reaction force with cog thread reinforcement. Despite fabrication limitations, this study supports the biomechanical potential of 3D-printed PCL cog threads for POP treatment, and lays the groundwork for future in vivo validation. Full article

To enhance the anti-overturning performance of poles and prevent tilting or collapse, a prefabricated foundation for distribution lines is developed. Field tests are conducted on five groups of foundations. Based on the test results, finite element analysis (FEA) is employed to investigate the influence of different factors—such as pole embedment depth, foundation locations, soil type, and soil parameters—on the anti-overturning performance of pole prefabricated foundations. The results indicate that under ultimate load conditions, the reaction force distribution at the base of the foundation approximates a triangular pattern, and the lateral earth pressure on the pole follows an approximately quadratic parabolic distribution along the depth. When the foundation size increases from 0.8 m to 0.9 m, the bearing capacity of the prefabricated foundation improves by 8%. Furthermore, when the load direction changes from 0° to 45°, the foundation’s bearing capacity increases by 14%. When the foundation is buried at a depth of 1.0 m, compared with the ground position, the ultimate overturning moment of the prefabricated foundation increases by 10%. Based on field test results, finite element simulation results, and limit equilibrium theory, a calculation method for the anti-overturning bearing capacity of prefabricated pole foundations is developed, which can provide a practical reference for the engineering design of distribution line poles and their prefabricated foundations. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

This review presents a comprehensive synthesis of recent advances in the tensile performance of adhesively bonded joints, focusing on applied aspects and modeling developments rather than providing a full theoretical analysis. Although many studies have addressed individual joint types or modeling techniques, an integrated review that compares joint configurations, modeling strategies, and performance optimization methods under tensile loading remains lacking. This work addresses that gap by examining the mechanical behavior of key joint types, namely, single-lap, single-strap, and double-strap joints, and highlighting their differences in stress distribution, failure mechanisms, and structural efficiency. Modeling and simulation approaches, including cohesive zone modeling, extended finite element methods, and virtual crack closure techniques, are assessed for their predictive accuracy and applicability to various joint geometries. This review also covers material and geometric enhancements, such as adherend tapering, fillets, notching, bi-adhesives, functionally graded bondlines, and nano-enhanced adhesives. These strategies are evaluated in terms of their ability to reduce stress concentrations and improve damage tolerance. Failure modes, adhesive and adherend defects, and delamination risks are also discussed. Finally, comparative insights into different joint configurations illustrate how geometry and adhesive selection influence strength, energy absorption, and weight efficiency. This review provides design-oriented guidance for optimizing bonded joints in aerospace, automotive, and structural engineering applications. Full article

The interaction between thermal fields and mechanical loads in thin-walled cylindrical shells introduces complex dynamic behaviors relevant to aerospace and mechanical engineering applications. This study investigates the axial stress wave propagation in a circular cylindrical shell subjected to combined thermal gradients and time-dependent harmonic compression. A semi-analytical model based on Donnell–Mushtari–Vlasov (DMV) shells theory is developed to derive the governing equations, incorporating elastic, inertial, and thermal expansion effects. Modal solutions are obtained to evaluate displacement and stress distributions across varying thermal and mechanical excitation conditions. Empirical Mode Decomposition (EMD) and Instantaneous Frequency (IF) analysis are employed to extract time–frequency characteristics of the dynamic response. Complementary Finite Element Analysis (FEA) is conducted to assess modal deformations, stress wave amplification, and the influence of thermal softening on resonance frequencies. Results reveal that increasing thermal gradients leads to significant reductions in natural frequencies and amplifies stress responses at critical excitation frequencies. The combination of analytical and numerical approaches captures the coupled thermomechanical effects on shell dynamics, providing an understanding of resonance amplification, modal energy distribution, and thermal-induced stiffness variation under axial harmonic excitation across thin-walled cylindrical structures. Full article

This article presents a non-linear MEC for a linear PM motor, and its experimental validation. In the MEC model, winding flux leakage and iron saturation are considered. In addition, two different linear PM motor models (bread-loaf and surface-type) are examined for linear PM motors. An iterative method is used to predict the magnetic behavior of saturated magnetic steel. The proposed MEC for linear PM motors is compared with finite element analysis (FEA) to determine its accuracy and suitability. FEA is widely regarded as a highly accurate and reliable tool for analyzing linear PM motors. However, its primary limitation lies in its considerable computational time requirement. This disadvantage becomes particularly problematic during the early stages of the design process. Therefore, the proposed model addresses this limitation. Also, experimental results validate the practicality of the MEC. Finally, the proposed model can be a tool for different slot/pole combinations. Thus, the model can be considered suitable for both bread-loaf and surface-type PM motors. Full article

A concentric double-flange butterfly valve (DN-500, PN-10) was analyzed to examine its dynamic behavior and internal fluid flow across multiple opening angles. Finite Element Analysis (FEA) was employed to determine natural frequencies, mode shapes, and effective mass participation factors (EMPFs) for valve positions at 30°, 60°, and 90°. The valve geometry was discretized using a curvature-based mesh with linear elastic isotropic properties for 1023 carbon steel. Lower-order vibration modes produced global deformations primarily along the valve disk, while higher-order modes showed localized displacement near the shaft–bearing interface, indicating coupled torsional and translational dynamics. The highest EMPF in the X-direction occurred at 1153.1 Hz with 0.2631 kg, while the Y-direction showed moderate contributions peaking at 0.1239 kg at 392.06 Hz. The Z-direction demonstrated lower influence, with a maximum EMPF of 0.1218 kg. Modes 3 and 4 were critical for potential resonance zones due to significant mass contributions and directional sensitivity. Computational Fluid Dynamics (CFD) simulation analyzed flow behavior, pressure drops, and turbulence under varying valve openings. At a lower opening angle, significant flow separation, recirculation zones, and high turbulence were observed. At 90°, the flow became more streamlined, resulting in a reduction in pressure losses and stabilizing velocity profiles. Full article

This paper presents the analytical model, feasibility assessment, and testing of a novel synchronous reluctance tubular machine, whose mover is manufactured using additive techniques. This approach enables the maximization of the machine’s saliency. The analytical model traditionally used for rotating machines was adapted to match the geometric characteristics of the innovative tubular design proposed in this work. The analytical results were validated through 2D finite element analysis (FEA). Subsequently, several mock-ups were 3D-printed using iron metal powder to evaluate the manufacturing feasibility of the proposed machine. Finally, the machine was tested to verify the accuracy of the analytical model. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

This study evaluated the effect of various adhesive systems, particularly silane application, on the repair bond strength of a nanofill resin composite and associated stress distribution using finite element analysis (FEA). A total of 105 composite specimens (4 × 6 mm) were aged by thermal cycling (10,000 cycles), roughened, etched with phosphoric acid, and assigned to seven groups (n = 15): G1. control—no adhesive; G2. Single Bond Universal Adhesive; G3. composite primer; G4. PQ1; G5. Silane + PQ1; G6. Clearfil Universal Bond; G7. All-Bond Universal. Shear bond strength was measured using a universal testing machine (1 mm/min), and failure modes were microscopically classified. FEA was conducted under static and fatigue conditions using 3D models built in Fusion-360. Mechanical properties were obtained from technical data and the literature. A 300 N load was applied and contact detection (0.05 mm) and constraint zones were defined. Statistical analysis was performed using one-way ANOVA and Tukey’s HSD (p = 0.05). Pearson’s correlation was used to assess the relationship between bond strength and von Mises stress. The highest bond strength was found in G2 (21.54 MPa) while G1 showed the lowest (8.86 MPa). Silane-treated groups exhibited favorable stress distribution and a strong correlation between experimental and simulated outcomes. Silane applications significantly enhance composite repair performance. Full article

In a power transformer short-circuit, transient current and magnetic flux interactions create strong electromagnetic forces that can deform windings and the core, risking failure. Accurate calculation of these forces during design is critical to prevent such outcomes. This paper employs two-dimensional (2D) and three-dimensional (3D) finite-element analysis (FEA) to model a 110 kV, 40 MVA three-phase transformer, calculating magnetic flux density, short-circuit current, and electromagnetic forces. The difference in force values at inner and outer core window positions, reaching up to 40%, is analyzed. The impact of physical winding displacement on axial forces is also studied. Simulation results, validated against analytical calculations, show peak short-circuit currents of 6963 A on the high-voltage (HV) winding and 70,411 A on the low-voltage (LV) winding. Average radial forces were 136 kN on the HV winding and 89 kN on the LV winding, while average axial forces were 8 kN on the HV and 9 kN on the LV. This agreement verifies the FEA models’ reliability. The results provide insights into winding behavior under severe faults and enhance transformer design reliability. Full article

This paper presents optimization of peak temperatures achieved during friction stir welding (FSW) of Al-6061-T6 alloys. This research work employed a novel approach by investigating the effect of FSW welding process parameters on peak temperatures through the implementation of finite element analysis (FEA), the Taguchi method, analysis of variance (ANOVA), and machine learning (ML) algorithms. COMSOL 6.0 Multiphysics was used to perform FEA to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. The influence of these parameters was investigated using an L32 Taguchi array and analysis of variance (ANOVA), revealing that axial force and tool rotational speed were the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material’s melting point, indicating the need for improved thermal control. This was achieved by using three machine learning (ML) algorithms, i.e., Logistic Regression, k-Nearest Neighbors (k-NN), and Naive Bayes. A dataset of 324 data points was prepared using a factorial design to implement these algorithms. These algorithms predicted the welding conditions where the temperature exceeded the melting temperature of Al-6061-T6. It was found that the Logistic Regression classifier demonstrated the highest performance, achieving an accuracy of 98.14% as compared to Naive Bayes and k-NN classifiers. These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality. Full article

This study presents a methodology for developing and validating digital models of tooth-inlay systems, aiming to trace the complete workflow from clinical procedures to simulation by involving dental professionals—dentists for manual cavity preparation and dental technicians for restoration modelling—while integrating micro-computed tomography (micro-CT) imaging with finite element analysis (FEA). The proposed workflow includes (1) the acquisition of high-resolution 3D micro-CT scans of a non-restored tooth, (2) image segmentation and reconstruction to create anatomically accurate digital twins and mesh generation, (3) the selection of proper resin and the 3D printing of four typodonts, (4) the manual preparation of cavities on the typodonts, (5) the acquisition of high-resolution 3D micro-CT scans of the typodonts, (6) mesh generation, digital inlay and onlay modelling and material property assignment, and (7) nonlinear FEA simulations under representative masticatory loading. The approach enables the visualisation of stress and deformation patterns, with preliminary results indicating stress concentrations at the tooth-restoration interface integrating different cavity alternatives and restorations on the same tooth. Quantitative outputs include von Mises stress, strain energy density, and displacement distribution. This study demonstrates the feasibility of using image-based, tooth-specific digital twins for biomechanical modelling in dentistry. The developed framework lays the groundwork for future investigations into the optimisation of restoration design and material selection in clinical applications. Full article

Pedicle screw fixation is a critical technique for stabilizing lumbar fractures in canines, yet the biomechanical implications of insertion angles remain underexplored. This study aims to identify optimal screw trajectories by analyzing stress distribution and deformation patterns in beagle lumbar segments (L6-L7) using finite element analysis (FEA). A 3D finite element model was reconstructed from CT scans of a healthy beagle, incorporating cortical/cancellous bone, intervertebral disks, and cartilage. Pedicle screws (2.4 mm diameter, 22 mm length) were virtually implanted at angles ranging from 45° to 65°. A 10 N vertical load simulated standing conditions. Equivalent stress and total deformation were evaluated under static loading. The equivalent stress occurred at screw–rod junctions, with maxima at 50° (11.73 MPa) and minima at 58° (3.25 MPa). Total deformation ranged from 0.0033 to 0.0064 mm, with the highest at 55° and the lowest at 54°. The 58° insertion angle demonstrated optimal biomechanical stability with minimal stress concentration, with 56–60° as a biomechanically favorable range for pedicle screw fixation in canine lumbar fractures, balancing stress distribution and deformation control. Future studies should validate these findings in multi-level models and clinical settings. Full article

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article