Search Results (109)

Passive and fixed-damping transfemoral prostheses cannot adapt their resistance to the phase-dependent demands of human gait, and microprocessor-controlled commercial knees remain out of reach for most amputees. We present a fuzzy logic controller that modulates a magnetorheological (MR) damper directly from gait phase and knee joint angle, since linear state-feedback and discrete PI designs are valid only at a single linearization point and require retuning across the gait cycle. The controller is formalized as a fuzzy-basis-function expansion with established coverage and Lipschitz continuity; the universal-approximation property of Mamdani systems grounds fuzzy logic theoretically but does not certify this 8-rule controller’s performance, established empirically instead. A dissipativity-based Lyapunov argument and a numerical sweep establish local closed-loop stability and bounded, rate-limited actuation. The damper couples to the knee through a shaft–bearing–housing assembly sized by free-body and Goodman fatigue analysis and verified by finite-element analysis, with the control pipeline embedded on an ESP32 microcontroller in a 2 kg prototype, corresponding to Technology Readiness Level (TRL) 5–6. In a single-subject case study with one transfemoral amputee, the controller achieved the lowest mean RMSE (0.0557 over three trials) against a non-disabled gait reference among five compared conditions, improving on the best fixed voltage by 20.2% and a passive prosthesis by 6.8×; these are single-subject feasibility results, not a claim of generalizable performance. Full article

The reciprocating cutterbar is evolving from a conventional harvesting mechanism into an intelligent end-effector for crop harvesting, mechanical weeding, and selective cutting. However, plant anisotropy, moisture-dependent fracture, root-soil constraints, vibration, and wear still hinder low-energy cutting, long service life, and robust control. This review integrates harvesting and mechanical weeding within a unified analysis of reciprocating cutterbar technologies. It first links plant tissue structure and dynamic fracture to blade penetration, fiber stretching, crack propagation, and energy dissipation. It then examines how cutting speed, sliding-cut angle, blade clearance, and root-soil anchorage jointly affect performance. Advanced testing, response surface methodology, discrete element method, finite element method, and multiphysics simulations are compared for failure analysis, parameter optimization, and contact modeling. The review further assesses bionic blade design, surface strengthening, composite coatings, novel transmissions, multisource perception, and adaptive control. Key barriers include inconsistent plant-mechanics datasets, computationally intensive models, limited field robustness, and conflicts among performance objectives. We therefore identify digital twins, modular electric cutterbars, and closed-loop control as priorities for translating mechanistic insight into reliable field performance. Full article

Implementing Historic Building Information Modelling (HBIM) for heritage structures is challenged by incomplete knowledge of hidden or inaccessible elements, as well as limited information on construction history, original design, and structural details, making geometric definition inherently uncertain. Simultaneously, applications such as structural analysis often require the same missing information. This study proposes an adaptive parametric Scan-to-BIM-to-FEM workflow that explicitly incorporates geometric uncertainty by generating multiple plausible and complete reconstructions from survey data and typological inference, enabling their use in parametric structural analysis. Starting from TLS survey, adaptive families were used to link measured and inferred dimensions through geometric constraints. The methodology is applied to the 19th-century masonry arch bridge of Montoggio (Genoa, Italy), currently characterized by a hybrid structural system resulting from subsequent retrofitting. Eight geometrical configurations were tested by varying uncertain parameters, including vault thickness and backing height, and were analyzed through modal and static finite element simulations. The results show a limited but non-negligible sensitivity of the structural response to these parameters, highlighting the influence of geometric uncertainties on analysis outcomes. In this light, the proposed framework provides a bridge between survey, modelling, and structural analysis, enabling HBIM to support interpretative and predictive structural assessment. Full article

Validating wireless power transfer (WPT) systems for electric vehicles (EVs) is a challenge due to efficiency variations caused by coil misalignments and height differences arising from various vehicle designs. Traditional simulation methods, such as finite element analysis (FEM), provide high accuracy but entail significant computational costs and calculation times, limiting the number of case studies and their optimization. This paper presents a methodology that integrates Machine Learning (ML) and Genetic Algorithms (GA) to overcome these limitations. An ML model rapidly and accurately predicts key electromagnetic parameters across a wide range of positions and frequencies. These predictions feed into a GA that optimizes control variables (voltages and frequency) with the objective of maximizing power transfer efficiency, while simultaneously ensuring component integrity at each operating point. Beyond drastically reducing simulation time and experimental effort, this methodology will enable knowledge extraction and its use for formulating design rules. These rules can lay the groundwork for developing simplified, real-time adaptive control strategies, facilitating the reduction of control variables and the narrowing of search ranges. Full article

The dynamic response and material failure of concrete under hypervelocity impact are critical for assessing the performance of protective structures. This study investigates the depth of penetration and damage mechanisms of concrete targets subjected to ovoid long-rod tungsten alloy projectiles at hypervelocity regimes ranging from 1000 m/s to 1600 m/s. Three numerical algorithms within LS-DYNA, the traditional Finite Element Method (FEM), fixed-coupling FEM-SPH, and adaptive FEM-SPH, were systematically evaluated and validated against experimental data and established empirical formulas. The results reveal a significant transition in algorithmic performance at hypervelocities compared to high-velocity regimes. The fixed-coupling FEM-SPH model demonstrates superior predictive accuracy in the 1000–1600 m/s range, with an average error of 5.6% and a maximum error of 10.4%, effectively capturing the near-rigid penetration characteristics and stable projectile morphology observed in experiments. In contrast, the adaptive FEM-SPH algorithm, which is typically robust at lower velocities, exhibited the lowest precision with an average error of 32.6% (maximum 36.6%), likely due to the instability of SPH conversion criteria under extreme strain rates. While traditional FEM remains the most computationally efficient, requiring only 14.6% of the processing time of the fixed-coupling model, it suffers from substantial deviations (average error of 29%) as the projectile transitions into semi-broken penetration modes with significant mass abrasion, which increased from 10% to 27% in the simulations. The comparative analysis reveals that numerical stress oscillations in traditional FEM and the limitations of current adaptive conversion criteria make fixed-coupling SPH formulations the most reliable scheme for hypervelocity long-rod penetration assessments. This study provides critical guidelines for selecting appropriate numerical schemes for extreme loading scenarios, balancing the requirements for physical fidelity, accuracy, and computational efficiency in protective structural design. Full article

To address the problems of large terrain variation, limited foundation construction, and poor adaptability of conventional supports in mountain photovoltaic projects, this study establishes finite element models of four flexible photovoltaic support systems, including one single-layer cable system and three double-layer cable systems. Under the same span, module layout, and loading conditions, the deflection, cable force, support reaction, equivalent steel consumption, and torsional performance of the different systems are compared under symmetric and asymmetric loading. The results show that the single-layer cable system has the lowest material consumption and better constructability in mountain terrain, but its torsional stiffness is relatively weak. The double-layer systems provide better overall stiffness and torsional resistance, but require more steel and impose larger foundation reactions. The single-layer cable system is then selected for further analysis. To ensure geometric nonlinear convergence, the structural analysis relies on established catenary cable and beam element formulations under incremental loads. Based on the parameter analysis, a combination of 40° for the bottom ground cable and 50° for the top ground cable is recommended to reduce structural reaction moments. Finally, as a simplified structural analysis approach to avoid exhaustive global modeling, a two-span equivalent model for intermediate columns, a single-span equivalent model for edge columns, and a table-based selection procedure are proposed, providing a reference for the rapid design of mountain multi-span flexible photovoltaic supports. Full article

Masonry tunnels built between late 19th and early 20th century constitute a widespread asset of the existing infrastructure network and currently require systematic condition assessment, monitoring, and maintenance interventions. Despite some existing regulatory frameworks, performing detailed assessments on tunnels with masonry linings remains a difficult task due to the significant uncertainties and the complex behavior of masonry. To address this gap, this work proposes a Simplified Approach (SA) for the structural assessment of masonry tunnels. A formulation adapted from classic static methods is proposed for the rapid assessment of the structural capacity of masonry linings. The proposed approach was applied and evaluated through a well-documented case study, in which the actual stress states were obtained with on-site measurements, that were employed to calibrate the model parameters by means of best fitting. The SA was employed to conduct a detailed stress verification along the entire lining, demonstrating its effectiveness as a calibrated tool for the large-scale safety assessment of historical tunnels, for the identification of critical sections that may require subsequent non-linear Finite Element Method analysis for Ultimate Limit State verification. Full article

Cemented sand and gravel (CSG) dams have been widely applied due to their simple construction and use of local materials. With the increasing occurrence of extreme weather events, temperature has become an important factor affecting the safe operation of dams. To investigate the temperature stress response of CSG dams under low-temperature conditions and achieve cross-scale analysis, an adaptive macro–meso finite element method is proposed. Through an iterative “solution–evaluation–mesh adjustment” procedure, meso-scale modeling is performed in high-stress regions, and the results are compared with those obtained using the conventional submodeling method. The results show that, under low-temperature conditions, temperature gradients and thermal stresses are mainly concentrated near the dam surface, with limited influence on the interior, while hydraulic load remains the dominant controlling factor. The local stress distribution patterns obtained by the two methods are generally consistent, and both can reflect stress concentration near the aggregate–mortar interfaces. The proposed method can characterize local meso-scale responses within a global computational framework, providing a reference for cross-scale analysis of the temperature response and the identification of local unfavorable stress regions in CSG dams. Full article

This study addresses the lack of integrated methodologies for qualifying additively manufactured polymer lifting attachments for UAV cargo operations under the EASA Specific category. A Design–Verify–Validate framework was developed to combine operational requirements, regulatory mapping to SORA Operational Safety Objective #05, material and manufacturing considerations, nonlinear finite element analysis, and experimental validation. The framework was demonstrated through the complete development of a 241 g FDM-printed PLA+ dual-bill gravitational hook for 50 kg Working Load Limit operations on the DJI Agras T50 platform (DJI, Shenzhen, China). Nonlinear finite element analysis was used to identify critical stress concentrations, while quasi-static testing of three identical specimens yielded an average failure load of 183 ± 9 kg, corresponding to an experimental safety factor of 3.66 ± 0.17. Functional testing on a suspended UAV platform confirmed reliable kinematic performance at incremental loads of 5 kg, 25 kg, and 50 kg. The results demonstrate that the proposed framework can generate coherent, standards-aligned verification evidence under quasi-static loading conditions. Structural validation in this study was limited to this loading regime. While demonstrated on a 50 kg WLL gravitational hook using unreinforced PLA+ as a proof-of-concept material, the methodology can be adapted in future work to other UAV platforms, geometries, and higher-performance materials. Full article

This study compares the biomechanical impact and mechanical distribution of SiC-reinforced PEEK implants to titanium implants using three-dimensional finite element analysis (FEA). Five different implant materials were investigated: titanium, pure PEEK, and PEEK reinforced with silicon carbide (SiC) particles at 2%, 4%, and 6% ratios. A wide range of oblique forces (45°) from 100 N to 900 N was applied to simulate physiological to extreme masticatory loads. The distribution of maximum von Mises stress and total deformation within the implant was examined. Structural integrity metrics linked to yield strength, including factor of safety (FOS), were also investigated. The biological evaluation included an analysis of the behavior of the bone tissue surrounding the implant by assessing maximum principal strain and maximum principal stress in cortical bone. The results show that titanium exhibited the highest stiffness and FOS (>1 up to 500 N) but induced the lowest bone strains (755–2275 µε at 100–300 N), indicating potential stress shielding. Pure PEEK resulted in excessive bone strains exceeding 4000 µε at moderate loads, suggesting bone overload risk. Among reinforced groups, PEEK + 4%SiC demonstrated the most balanced performance, reducing maximum principal bone stress by 28% compared to pure PEEK at 200 N, while maintaining bone strains within the physiological adaptive range (1000–3000 µε) under moderate loads. PEEK + 6%SiC showed increased stiffness but reduced ductility and safety factor. Within the limitations of this computational study, PEEK reinforced with 4% SiC appears to offer an optimal trade-off between mechanical stability and biomechanical compatibility. Further in vitro and clinical studies are warranted to validate these findings. Full article

This article presents a systematic analysis of the application of advanced mathematical and computational approaches in dental bioengineering, with a focus on biomaterials processing and machining-related technologies. The aim is to critically synthesize current knowledge on the use of numerical simulations, statistical modeling, and algorithm-based methods in the analysis and optimization of technological processes in dentistry. The review was conducted following the PRISMA framework to ensure a transparent and reproducible selection of relevant studies addressing the intersection of dental applications, manufacturing processes, and computational modeling. The results reveal that the current research does not constitute a unified modeling framework, but rather a heterogeneous set of approaches targeting specific aspects of biomaterial processing. The analyzed studies demonstrate the application of finite element analysis, empirical statistical models, and geometry-based computational methods, particularly in processes such as drilling and grinding of ceramic dental materials. These approaches enable detailed analysis of mechanical and thermal loading conditions, as well as partial optimization of process parameters. However, their applicability is often limited by their empirical nature, lack of integration, and insufficient linkage to real-time process control. The synthesis highlights a significant research gap in the development of integrated and multiphysics modeling frameworks capable of combining mechanical, thermal, and geometrical aspects of machining processes. Future research should focus on the implementation of digital twins, adaptive process control, and personalized modeling strategies to enhance the accuracy, efficiency, and predictability of dental biomaterial processing. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Osteoarthritis is the most prevalent age-related joint disease, yet the limited regenerative capacity of articular cartilage severely constrains spontaneous repair. Here, we present a freeze–thaw polyvinyl alcohol (PVA)/polyethylene glycol (PEG) hydrogel platform featuring a hybrid open–closed macroporous architecture that enables cartilage-mimetic load dissipation for artificial cartilage applications. The hybrid porous structure provides synergistic advantages, where closed pores enhance load-bearing stiffness while open pores facilitate energy dissipation. By systematically tuning polymer composition and processing conditions, clear structure–property relationships among porosity, water content, and mechanical performance are established. An optimized formulation (18 wt.% PVA, 85–124 kDa; 18 wt.% PEG; three freeze–thaw cycles) yields hydrogels with high water content (39.1 ± 7.8 wt.%), high compressive Young’s modulus (3.60 ± 0.67 MPa), and excellent resilience under cyclic loading. Notably, under dynamic compression (2 m/s), a frequently overlooked yet physiologically relevant mechanical property of hydrogels, the materials exhibit nearly twofold enhancement in compressive modulus compared to static conditions, demonstrating pronounced strain-rate-dependent stiffening. Finite element analysis reveals efficient load redistribution across the interconnected porous network, providing mechanistic insight into the observed mechanical robustness. Compared with native cartilage and recently reported hydrogel systems, the developed hydrogels exhibit superior stiffness while maintaining mechanical and structural resilience. In vitro cytotoxicity and direct-contact assays confirm excellent cytocompatibility. These results establish a scalable and cost-effective design strategy for engineering mechanically robust, rate-adaptive hydrogels, advancing the development of next-generation artificial cartilage substitutes. Full article

This study introduces and characterizes a family of quilling-inspired S-shaped unit-cell architectures intended as building blocks for mechanical metamaterials. In contrast to conventional lattice designs based mainly on straight struts, the proposed geometries use continuous curved elements inspired by paper quilling, enabling deformation mechanisms dominated by bending, rotation, and progressive opening of the curved members. By translating quilling’s coiled and spiraled patterns into engineered geometries, nine distinct S-shaped unit cells were fabricated by fused deposition modeling and tested experimentally under uniaxial tensile loading. Finite element analysis was performed to reproduce the tensile response and to assess the influence of geometry on stiffness, stretchability, and energy absorption. The results show that relatively small changes in radii, span lengths, angular distribution, and symmetry produce significant differences in mechanical response. Compact configurations such as S2, S3, and S5 exhibit high stiffness and limited elongation, whereas S9 shows the highest compliance and stretchability. The results indicate that these quilling-inspired architectures provide a tunable design space and have strong potential for applications in energy absorption, adaptive structures, and lightweight load-bearing systems. Full article

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article