Search Results (2,169)

(1) Background: Peripheral nerve repair requires structural guidance and reliable functional assessment, yet current commercial conduits lack internal microarchitecture and do not integrate real-time monitoring capabilities. This study presents an engineering-focused feasibility assessment of a dual system combining a microstructured 3D-printed nerve guidance conduit with a portable neuromuscular monitoring device. (2) Methods: Two conduit variants were digitally designed based on median nerve anatomical dimensions and fabricated using FDM (PLA) for large-scale prototypes and SLA for high-resolution miniaturized models. Structural behavior was evaluated through simplified finite element analysis (FEA) under physiological pressure ranges (1000–5000 Pa). A portable monitoring system incorporating surface EMG electrodes, an AD620 instrumentation amplifier, and an ESP32 microcontroller was assembled and tested non-invasively on a healthy adult volunteer to verify signal acquisition functionality. Results: Both conduit designs were successfully fabricated with accurate reproduction of internal microchannels. FEA indicated negligible deformation (1.41 × 10⁻¹¹–1.69 × 10⁻¹⁰ mm) and low stress values (0.0129–0.155 N/m²), confirming structural stability under the simplified loading model. The monitoring system recorded stable EMG signals (3200–3500 ADC units), demonstrating correct operation of the acquisition chain during controlled stimulation. (3) Conclusions: This work provides a design-stage engineering feasibility demonstration of an integrated platform combining a microstructured 3D-printed conduit with a portable neuromuscular monitoring device. The study does not include biological validation; prototypes were evaluated solely for geometric and mechanical fidelity; and the monitoring system was tested only for functional signal acquisition. Future work will address biocompatibility, in vitro assays, and in vivo evaluation. Full article

Electric vehicle (EV) wireless power transfer (WPT) systems face two fundamental challenges that hinder their widespread adoption: sensitivity to coil misalignment and the need for battery-friendly fast charging protocols. This paper presents a comprehensive and integrated framework that addresses both challenges through a three-pronged approach combining advanced coil geometry optimization, multi-stage current method (MSCM) charging, and reinforcement learning (RL)-based adaptive control. First, a memetic algorithm hybridizing global exploration and local refinement is employed to design coil geometries that are inherently resilient to misalignment. The optimized coils maintain strong magnetic coupling under lateral displacements up to ±75 mm by strategically sizing the secondary coil’s outer diameter to be smaller than the primary coil’s, ensuring it remains within the optimal magnetic flux region. Second, an MSCM charging protocol is developed and optimized with the objective of balancing charging speed against battery thermal stability and state of health (SoH). The proposed strategy determines optimal current levels for each charging stage, reducing temperature rise compared to conventional CC-CV charging. Third, a novel Deep Q-Network (DQN) RL agent is implemented for real-time adaptive control of the WPT system. The RL controller dynamically adjusts phase shift in response to varying coupling conditions, load disturbances, and battery state, outperforming traditional PI controllers with 23% faster settling time and improved efficiency under dynamic misalignment scenarios. Finite element analysis (FEA) simulations validate the electromagnetic performance of the optimized coils, while an experimental prototype demonstrates the integrated system’s performance. Results show that the combined approach achieves 91.2% DC-DC efficiency under nominal conditions and maintains over 83% efficiency under lateral misalignments up to ±75 mm, fully complying with SAE J2954 alignment tolerance requirements. The MSCM charging protocol, guided by the memetic algorithm, limits battery temperature rise during a full charge cycle, while the RL controller ensures stable power delivery under real-world dynamic conditions. This work establishes a new paradigm for holistic WPT system design, demonstrating that synergistic optimization of magnetic structures, charging protocols, and intelligent control can simultaneously achieve misalignment resilience, fast charging, and adaptive robustness. Full article

Finite element (FE) analysis that incorporates structural damages extracted using computer vision (CV) methods based on three-dimensional point-cloud data (PCD) is effective for evaluating the residual capacity of structures. In this study, observable surface damages are regarded as surface anomaly regions on the structure. Based on this assumption, a modular mesh-model updating framework is proposed to incorporate damage information into shell-element FE models through the anomaly detection from point-cloud data. The framework comprises three modules: a perception module for the anomaly detection and anomaly-region localization, a description module for the anomaly-region quantification, and a remeshing module for mapping the anomaly region onto the FE model. Its performance was evaluated using a steel angle member specimen with artificially introduced anomaly regions representing damage. The updating results were evaluated in terms of both geometric accuracy and FE analysis (FEA) applicability by comparison with the reference FE model manually constructed from the designed anomaly-region geometries. The Intersection over Union (IoU) between corresponding anomaly regions in the updated and reference models ranges from 0.44 to 0.87, including the individual plate results for cross-surface cases. Through static elastoplastic analysis, the predicted reductions in ultimate load-bearing capacity differed by approximately 1.2 percentage points between the two updated models. Moreover, local stress redistribution showed qualitative agreement, although quantitative discrepancies remained in the magnitudes and locations of stress concentrations. Together with several supporting components, the proposed framework provides an effective workflow for integrating PCD-based CV techniques into FE model updating and has potential for FEA-based damage assessment of structures. Full article

Submarine mast covers are critical cantilever appendages subjected to extreme hydrostatic pressure during submerged transit. To ensure structural reliability and control internal defects inherent in heavy-section casting, an integrated design-to-manufacturing framework sequentially linking structural finite element analysis (FEA), fluid volume method (FVM)-based casting simulation, and full-scale experimental sand casting was established. FEA under a 600 m submergence depth (7.0 MPa hydrostatic pressure) identified stress concentrations on the inner surface along the major-axis section. Introducing an optimized fillet radius at a sharp step reduced peak equivalent stress from 216.0 MPa to below 194.0 MPa, securing a safety factor exceeding 2.0 against the yield strength of nickel–aluminum–bronze (390 MPa). MAGMA5 casting simulations verified an unpressurized bottom-gating system (S:R:G = 1.00:2.88:4.80), achieving smooth laminar filling with gate velocities under 1.25 m/s without cold shuts. To mitigate predicted shrinkage porosity, process modifications enlarging riser diameters from Ø30 mm to Ø60 mm and placing chills were implemented in the actual casting trial, while top porosity was removed via machining allowances. Specimens harvested from the full-scale prototype yielded 757.8 MPa UTS, 392.5 MPa yield strength, 17.9% elongation, and 197 HB hardness. Full article

In car–pedestrian collisions (CPC), most studies have focused on injury analysis using youth models. However, the mechanical performance of bones and muscles naturally deteriorates with age. Compared with younger people, the elderly are more vulnerable to bone fracture when subjected to the same impact. In this study, a digital twin model of the elderly lower limb (DTM-ELL) and a digital twin model of the youth lower limb (DTM-YLL) for Chinese 50th-percentile males were developed based on CT images, in which 150 material properties were assigned to the bones according to their Hounsfield units. The digital twin model of the lower limb (DTM-LL) was validated through quasi-static three-point bending simulation and dynamic lateral loading simulation of the knee joint. A family-car lateral impact simulation was used to verify the consistency between the developed model and THUMS (Total HUman Model for Safety). Through collision simulations between each of three car models and the DTM-LL, it was found that the maximum average stress in the femur of the DTM-ELL is 62.64% of that of the DTM-YLL under the same CPC conditions. Surrounding soft tissues, acting as effective cushioning during CPC, reduce the maximum femoral stress by 29.56% relative to the model without surrounding soft tissues. Finally, a real CPC accident was reconstructed in finite element software using the DTM-LL developed from the CT images. The reconstruction results showed that the DTM-LL had acceptable biofidelity and can be used to study femoral injuries in elderly pedestrians involved in CPC. Full article

This paper proposes a physics-informed neural network (PINN)-based design optimization framework for a compact smart actuator motor in physical artificial intelligence (Physical AI) robotic systems, where artificial intelligence is integrated with sensing, actuation, and physical interaction to perceive and act in the real world. To improve integration density and joint compactness, a printed circuit board (PCB) stator axial-flux permanent magnet (AFPM) motor is adopted as a thin and highly integrated topology. Because its performance is strongly affected by coupled design variables, including outer diameter, PCB count, turns per slot, trace width, and magnet thickness, an efficient and physically consistent optimization method is required. The proposed framework constructs a finite element analysis (FEA)-based design database and trains a physics-reconstructed PINN surrogate model to predict torque and loss components. Unlike purely data-driven models, the proposed PINN reconstructs output power and efficiency using physical power-balance relations, thereby improving consistency among torque, loss, output power, and efficiency. The trained surrogate is coupled with the nondominated sorting genetic algorithm II (NSGA-II) to maximize torque and efficiency while minimizing AC loss under dimensional and performance constraints. The optimized candidates are further verified by high-fidelity FEA. The results demonstrate that the proposed framework provides an effective Physical AI-oriented design methodology for compact robotic smart actuators by integrating PCB stator AFPM motor topology, physics-informed learning, and multi-objective optimization. Full article

Reducing unsprung mass in high-performance braking systems is critical for vehicle dynamics, but applying topology optimization (TO) to conventional solid discs under combined thermo-mechanical loading often causes “thermal entrapment” and structural failure. This study addresses this limitation by proposing a floating disc architecture and a two-stage computational framework. First, TO via the SIMP algorithm was applied to the Aluminum 7075-T6 inner carrier strictly under mechanical loads, decoupling artificial thermal stresses and yielding a fixed, optimized geometry. Results show the TO achieved a drastic 53% mass reduction (from 0.218 kg to 0.102 kg) specifically for the inner carrier component. Second, a high-fidelity Surrogate Model (Digital Twin) of this new geometry was developed to bypass the immense computational cost of multi-parameter, non-linear thermo-mechanical Finite Element Analysis (FEA). Utilizing Latin Hypercube Sampling (LHS) across 15 design scenarios and the Genetic Aggregation algorithm, the surrogate model was trained to predict real-time responses. Subsequently, the Digital Twin predicted stress and temperature fields with near-perfect accuracy ($R^2\approx 0.9998$), rapidly identifying the limit braking scenario. Fatigue analysis confirmed the final component safely withstands ${10}^6$ extreme braking cycles (safety factor 1.65). Full article

Skin-interfaced thermal sensors offer a promising, portable, and cost-effective alternative for continuous and noninvasive measurements of skin condition and blood flow. Skin condition, especially skin hydration, is reflected by the tissue thermal conductivity. Blood flow is characterized by the average flow velocity through blood vessels in skin. However, the measurement accuracy of tissue thermal conductivity and blood velocity is hindered by the coupled heat conduction and convection in the tissue containing blood vessels. To overcome this bottleneck for precise measurements of tissue thermal conductivity and blood velocity simultaneously, we design a skin-interfaced thermal sensor consisting of a resistive heater and three thermistors. The resistive heater with a diameter of 4 mm consumes a low power of 0.05 W. The three miniature thermistors measure the steady-state temperatures on skin at the middle location of the heater center, upstream flow location, and downstream flow location. Using finite element analysis (FEA) of heat transfer in vascular skin, we optimize the three-thermistor layout, placing the upstream and downstream thermistors 6.0 mm and 2.7 mm from the heater center, respectively. FEA results reveal that the middle-thermistor temperature is predominantly sensitive to tissue thermal conductivity with relatively low flow interference, whereas the temperature difference between upstream and downstream thermistors maintains high sensitivity to blood velocity, and is less affected by tissue thermal conductivity. With the FEA results, we implement a polynomial machine learning model and a physics-informed thermal-resistance reduced-order model to analyze the thermal sensor temperature measurements and jointly predict both quantities. The relative prediction errors are typically below 4% for thermal conductivity and 10% for blood velocity using the machine learning model, and below 1% and 8% using the reduced-order model. This work provides a framework for the development of skin-interfaced thermal sensors capable of intelligent and noninvasive skin and vascular assessment. Full article

Structural design under severe handling loads can be formulated as a constrained optimization problem in which structural mass is minimized while prescribed safety requirements are satisfied. In this study, a mathematical size-optimization model was developed for the frame of an underwater walking robot subjected to extreme launch and recovery loads. The frame geometry was parameterized using cross-sectional lengths and regional thicknesses as design variables. The optimization problem was formulated by minimizing the frame mass subject to an inequality constraint on the maximum equivalent stress and prescribed bounds on the design variables. Finite element analysis (FEA) was employed to evaluate the structural response associated with each design, and sequential quadratic programming (SQP) was used to iteratively solve the resulting nonlinear constrained optimization problem. The computational behavior of optimization was examined through the evolution of the design variables, objective function, and stress constraint. The optimization converged to a feasible design that satisfied the prescribed stress requirement while reducing the structural mass. The design evolution also showed that the geometric variables changed differently according to their locations in the frame, resulting in a redistribution of the structural response during the optimization process. These results demonstrate that the coupled FEA–SQP framework provides a systematic numerical approach for solving stress-constrained structural size-optimization problems and illustrates the application of mathematical optimization to the design of engineering structures subjected to severe loading conditions. Full article

Direct winding places magnet wires in direct contact with soft magnetic composite (SMC) stator cores, making the winding-corner response important for component design. This study evaluated an Fe–5.0 wt.%Si SMC stator core through finite element analysis and corner-loading tests. Based on a wire tension of 1.44 N/turn and a 90° change in winding direction, the resultant load was 2.04 N per turn and 38.7 N for 19 turns. An engineering verification load of 177 N was defined from the mean nominal 0.2% proof load of the AI-EIW wire. The equivalent distributed model at 38.7 N and the representative one-turn circular-contact model at 2.04 N predicted elastic responses without effective strain. At 177 N, the maximum effective stress and strain were 876.9 MPa and 0.0132, and the maximum resultant displacement after unloading was 0.85 μm. Component-level tests showed continuous force increases to the prescribed loads. Optical microscopy at 20× magnification identified no indentation, cracking, edge chipping, particle detachment, or corner-profile change after unloading, although the FEA-predicted sub-micrometer localized deformation was not directly quantified by the optical evaluation. These results characterize the local mechanical response under the defined reference winding and engineering verification conditions. Full article

Titanium alloy bone scaffolds have been widely used in the clinical treatment of bone defects. However, conventional titanium alloy bone scaffolds exhibit a lack of porous structure and excessively high elastic modulus, resulting in poor osseointegration. In this study, mimicking the structural characteristics of human bone—namely dense exterior and porous interior—three types of bionic Gyroid titanium alloy bone scaffolds with a uniform porosity of 50% but distinct gradient properties were designed, including uniform lattice structure, linear gradient structure, and quadratic gradient structure. Process optimization of selective laser melting (SLM) and characterization of the as-fabricated microstructures were carried out. The tensile and compressive properties of additively manufactured titanium alloy bone scaffolds were investigated via mechanical testing and finite element analysis (FEA). The results demonstrate that optimized SLM parameters yield a matrix relative density of 98.56% for solid Ti-6Al-4V reference specimens. Using these parameters, bionic gradient-porosity Gyroid bone scaffolds were successfully manufactured. The bionic quadratic function gradient design achieves optimal modulus matching (10–30 GPa) and sufficient mechanical strength at the design level, satisfying the mechanical requirements for bone scaffolds and showing favorable application potential for load-bearing bone scaffolds. Full article

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the development of this computational optimization framework. This study involved generating a parametric dataset, using Latin Hypercube Sampling (LHS) with synthetic data, for the development of the artificial neural networks (ANNs). The networks use the surrogate model to optimize solutions through a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The ANN provided an overall test set of R² = 0.968 across the three objective functions. The hybrid optimization method produced 11 surrogate-predicted Pareto-optimal candidate designs, simultaneously minimizing the micro-displacement and stiffness loss while maximizing the fatigue life. The present results are based on analytically derived synthetic training data. Simcenter 3D Version 2506 Finite Element Analysis (FEA) integration and prototype validation constitute the planned next phase. The methods described here can be configured for other components of the suspension and are expected to be compatible with similar applications depending on future enhancements. Full article

Dual-stator single-rotor (DSSR) axial flux permanent magnet synchronous motors (AFPMSMs) offer high torque density but face reliability challenges due to unbalanced magnetic forces (UMF) and bending moments caused by eccentricity faults. This study investigates the electromagnetic performance of a DSSR AFPMSM under static, dynamic, axial, and radial eccentricities to reveal specific fault signatures and physical mechanisms. The methodology relies on three-dimensional transient finite element analysis (3-D FEA) and is validated by experimental tests on a 500 W prototype. Results indicate that while static and dynamic eccentricities have negligible effects on average torque, they induce significant bending moments where static eccentricity generates a constant moment and dynamic eccentricity produces an alternating one, both proportional to the eccentricity severity. Crucially, axial eccentricity disrupts magnetic symmetry, causing a 17.6% no-load back-EMF imbalance between stators and increasing net axial UMF to 53.2 N at a 40% eccentricity factor. Conversely, radial eccentricity shows minimal impact, confirming the topology’s robustness against radial misalignments. These findings provide essential baseline data for the vibration analysis and condition monitoring of DSSR AFPMSMs. Full article

Type IV composite overwrapped pressure vessels (COPVs) are widely used for high-pressure hydrogen storage. They require structural health monitoring techniques that provide accurate strain measurements without compromising structural integrity. This study presents an experimental and numerical investigation of filament-wound glass fiber/epoxy Type IV COPVs with embedded Fiber Bragg Grating (FBG) sensors. The FBG sensors were integrated into every hoop layer of the vessel during filament winding, and their placement was verified by computed tomography (CT). The influence of sensor integration on the composite microstructure was assessed by scanning electron microscopy (SEM), including quantitative evaluation of fiber volume fraction and void content. Hydrostatic burst tests and finite element analysis were performed to evaluate the structural response of the pressure vessel. CT confirmed the successful positioning and orientation of the embedded sensors, whereas SEM revealed that the optical fibers introduced only minor local disturbances comparable to typical manufacturing structural flaws. The numerical model predicted strains similar to those obtained experimentally. Embedded FBG sensors enabled through-thickness strain monitoring, revealing a non-monotonic strain distribution across the monitored hoop layers. Full article

Advanced engineering applications increasingly demand high-performance polymers with exceptional mechanical and thermal properties; however, predicting their processing behavior remains challenging due to complex rheological responses and the lack of integrated experimental–simulation frameworks. This study introduces a novel integrated experimental–computational methodology that combines comprehensive rheological characterization, multi-model fitting, injection molding simulation, and multiphysics finite element analysis (FEA) to investigate the processing capabilities of Polyether Ether Ketone (PEEK) for aircraft bearing applications. Unlike conventional approaches that treat rheological analysis, processing simulation, and structural assessment separately, our framework establishes a coupled material–process–performance relationship through: (i) systematic thermal and mechanical characterization, establishing PEEK’s high melting temperature (343 °C), degradation temperature (575 °C), and tensile strength (95 MPa); (ii) comparative rheological model fitting, demonstrating that the Carreau–Yasuda model accurately predicts non-linear flow behavior with R² = 0.97, outperforming simpler Power Law and Cross models; (iii) CAD-based injection molding simulation, revealing homogeneous flow distribution and optimized pressure profiles; and (iv) thermo-mechanical FEA, coupling thermal expansion with structural stress analysis to evaluate bearing integrity under operational conditions. The key novelty lies in the seamless integration of experimental rheology with multiphysics simulation, validated through rigorous statistical analysis achieving low RMSE (0.6854 MPa for stress, 0.003220 mm for deformation) and high correlation coefficients (R² = 0.97). The results confirm a uniform flow distribution, stable structural performance, and reliable thermo-mechanical response, establishing PEEK’s suitability for high-performance aerospace components. This work contributes a comprehensive, scalable, and transferable framework that bridges experimental analysis and advanced simulation, enabling the predictive optimization of polymer processing parameters and significantly enhancing manufacturing reliability for industrial applications. The findings demonstrate the applicability of the experimental–computational analysis to the investigated PEEK bearing configuration under the specified processing and simulation conditions. Its specific contribution is the application of comparative rheological model fitting and experimentally characterized PEEK properties to the selected bearing geometry and processing conditions. Full article