Search Results (160)

Fully integrated MITC4 (mixed interpolation of tensorial components) shells remain costly for large-deformation sheet-metal forming benchmarks at production mesh sizes. This paper presents a GPU-resident explicit MITC4 shell solver, implemented as a single CUDA pipeline in which co-rotational kinematics, assumed natural strain transverse shear, through-thickness J2 elasto-plasticity, and rigid-surface penalty contact remain in device memory. The study is positioned as computational verification and benchmarking for the Nakajima hemispherical-dome forming benchmark. Canonical shell tests verify the element kernel through membrane and bending patches and a force-driven cantilever, with the cantilever deflection agreeing with the MacNeal–Harder reference within about 2%. On the 10K-element Nakajima benchmark, the present solver agrees with Abaqus/Explicit with a mean von Mises error of 2.95% over 94% of specimen elements and a maximum shell thickness error of 2.08%. In the clamped/binder transition band, section-mean von Mises agrees to +1.0%, whereas section-maximum stress is under-predicted by 10.9%. A 50K-element Abaqus check remains bounded at 80 mm stroke, with section-mean von Mises differences of +0.6% globally and +0.4% in the transition band. For throughput, a separate 500K-element deck over 1.0 × 10⁻³ s and 15,808 steps give per-step speed-ups of 43.7×, 17.7×, and 13.5× versus 1-, 8-, and 32-core LS-DYNA MPP. Full article

As robotics technology advances, quadruped robots have become capable of operating in complex environments with varying elevation, including ramps and level changes that are challenging for conventional wheeled platforms. While this terrain adaptability opens new opportunities for inspection, rescue, and exploration tasks, the repetitive impacts, frequent ground-contact transitions, and abrupt postural changes inherent to legged locomotion pose significant challenges for LiDAR odometry. High-frequency gait vibrations and abrupt attitude changes introduce intra-scan motion distortion that conventional single-twist deskewing cannot adequately suppress. In addition, sparse vertical geometric constraints in elevation-varying environments weaken Z-axis observability, allowing vertical drift to corrupt the horizontal pose estimate through Hessian coupling. To address these failure modes within a LiDAR-only framework, we propose a Piecewise-Constant Velocity deskewing scheme that partitions each scan into multiple temporal segments with safety clamping on vertical and attitude components, together with a two-stage ICP that decouples SE(3) optimization into horizontal (x, y, yaw) and vertical (z, roll, pitch) stages and applies observability-aware weighting in the vertical update. The proposed odometry front-end is evaluated on four real-world sequences collected with a Unitree Go2 quadruped robot equipped with a Velodyne VLP-16 LiDAR. Experimental results show consistently lower Absolute Pose Error (APE) than ICP, KISS-ICP, and F-LOAM across all sequences. Vertical drift suppression is most pronounced in the ramp-containing sequences, where baseline methods exhibit substantial Z-axis divergence. Full article

This paper presents the process of gathering data for a flapping-wing micro air vehicle (FWMAV) using optical tracking and force sensors for subsequent dynamic modeling and simulation purposes. Tethered and clamped experiments were performed to track the vehicle’s overall motion, wing kinematic angles, and aerodynamic force patterns, while additional properties such as mass, inertia tensor, center-of-mass position, and short-period excitation frequency were also examined. The methodology includes the testing approaches, modeling choices, and error analyses applied to the measurements. The results demonstrate that both tethered and clamped configurations introduce key limitations, particularly for steady-state flight. Additional constraints include structural fragility (hindering high-frequency testing), over-simplified CAD geometry, and controller tuning issues on the tail. Based on the identified parameters and experimental datasets, a high-fidelity simulation model was developed in MATLAB to serve as a platform for future control and flight envelope studies. Overall, the combination of optical tracking and force sensing provides a structured framework for linking experimental data to physical models, laying the foundation for future improvements in ornithopter modeling and testing. Full article

Aiming at the problems of main cable geometry calculation and control accuracy in construction for long-span asymmetric suspension bridges, this paper proposes a practical method for main cable geometry calculation of asymmetric suspension bridges based on the Rushankou Bridge. Firstly, a hanger–pylon–girder model was established to obtain the constraint force at the hanger top. Then, with the mid-span sag of the main cable set as the control target, the coordinates and unstressed length of the main cable in the completed bridge state were obtained based on the pylon–cable model. Finally, the final main cable geometry and unstressed length were obtained based on the main cable–hanger–pylon–girder model. The reliability of the method in this paper was validated by engineering monitoring data. Using the simulation model, the influence laws and degrees of parameters including temperature, main cable elastic modulus, main cable weight, hanger force and main girder weight on the main cable geometry were investigated. It is indicated that the method in this paper is capable of accurately calculating the main cable shape of asymmetric suspension bridges. After the installation of cable clamps and hangers, the theoretical and measured deformations of the main cable are in good agreement. The theoretical and measured values at the mid-span L/2 of the main span are −233.9 cm and −234.7 cm, respectively, with a deviation of 8 mm. The largest discrepancy between the calculated and actual deformations of the main cable is located at 7L/8 of the main span, which is merely 2.2 cm. The deformation of the main cable is greatly affected by temperature changes; each 1 °C temperature variation leads to a mid-span deformation of about 2.4 cm in the main cable. If the influence of temperature variation on main cable geometry is ignored during construction, it will cause errors in the main cable elevation after installation. The effect of the main cable elastic modulus on its deformation cannot be neglected, and a 10% variation in the main cable elastic modulus leads to a 58 cm change in the main cable geometry. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

Brake-by-wire (BBW) systems face challenges such as structural complexity, high energy consumption, and control inaccuracies induced by nonlinear factors. This study develops a novel self-locking dual-side synchronously clamping electronic wedge brake (EWB) system as an advanced BBW architecture. This novel design consists of a single screw with opposite-handed threads to drive the wedge mechanism bidirectionally, leveraging the self-energizing effect and the self-interlocking effect to significantly reduce energy consumption while achieving hydraulic-free synchronous braking. Additionally, the inherent precise displacement control of the screw transmission offers a simplified solution for air gap management. A multi-domain coupled model integrating mechanical dynamics and control algorithms is developed based on the proposed architecture, with finite element analysis (FEA) validating the mechanical strength and thermal degradation resistance of key components under extreme conditions. A hybrid control algorithm combining model predictive current control (MPCC) and a PI controller is developed. Compared with the active disturbance rejection control (ADRC), the proposed method achieves a 55% improvement in dynamic response and a $69.1$% reduction in steady-state error. The vehicle braking performance is validated through a CarSim–Simulink co-simulation, while the rapid dynamic response and precise clamping force control of the key actuator are verified via bench testing, demonstrating the effectiveness of the proposed EWB system architecture and its control strategy, thereby laying a solid theoretical foundation for its future industrial implementation. Full article

To address the problem whereby traditional model predictive control suffers from mismatches between the model and actual parameters due to system parameter variations in the capacitor voltage-balancing control of a neutral-point-clamped (NPC) five-level inverter, an improved model-free predictive control strategy based on particle swarm optimization and the deadbeat principle is proposed. Firstly, an ultra-local model of the inverter is established, and a particle swarm optimization algorithm with an adaptive inertia coefficient is employed to self-tune the control gain of the ultra-local model, thereby reducing current control error. Secondly, the electrical angle of the reference voltage is calculated using the deadbeat principle, and a simplified vector set is constructed for voltage vector traversal. Control is applied only to the capacitor with the largest voltage deviation from the balance value, which reduces computational burden while achieving current tracking and capacitor voltage balancing. Finally, the simulation results show that under steady-state conditions, the output current total harmonic distortion (THD) is 0.28%, and the DC-side capacitor voltage fluctuation is 0.01%, demonstrating a significant improvement in control performance compared with the extremum-seeking control and Kalman filtering methods. Under transient conditions, the proposed control strategy achieves a response time of 0.7 ms while maintaining good control performance and strong robustness. These results verify the effectiveness of the proposed control strategy. Full article

Methods of single-point diamond turning and chemical mechanical polishing can achieve an ultra-flat substrate. However, these methods which rely on mechanical interactions to achieve material removal can easily lead to defects such as abrasive embedding and scratches on the surface. In addition, for low-rigidity and thin-plate workpieces, clamping deformation and force deformation are critical factors affecting the machining accuracy. This paper proposes a two-step polishing chain that uses controllable electrochemical and chemical etching to correct the shape error of the workpiece. With the optimized parameters, the jet electrochemical machining (Jet-ECM), which uses the electrochemical etching mechanism, is applied to the computer-controlled optical surfacing (CCOS) to achieve the rapid convergence of the shape accuracy. In addition, electrogenerated chemical polishing (EGCP) is implemented as a follow-up process which uses the mechanism of diffusion-controlled chemical etching to reduce the mid-spatial-frequency (MSF) error caused by the computer-controlled optical surfacing. Based on this two-step polishing chain and the self-developed devices, the peak-to-valley (PV) value of the φ 50 mm workpiece (valid dimensions = 90% of the central region) is reduced from 2.678 μm to 0.384 μm. This study has great implications for further understanding the mechanism of Jet-ECM and EGCP, which expands the applications of stress-free polishing to solve the processing problems of the low-rigidity workpiece. Full article

In structural engineering practice, the problem of thick plate bending occurs in designing shelters, foundations of high-rise buildings, counter-slabs, etc. In such cases, neglecting shear deformation can lead to significant errors in predicted behavior, especially when a plate is subjected to a concentrated force. In practice, neither a fully clamped nor an ideal simple support can be achieved during construction, so the plates are partially clamped, and this also applies to thick plates. Bending of thick rectangular plates with partially clamped edges has not been studied in the literature, so this paper addresses this issue. A comprehensive numerical analysis using a developed simple analytical model in the form of a Lévy-type solution based on Reissner theory has been carried out. The presented model is able to account for different degrees of rotational restraint in plates with two opposite edges simply supported and the other two partially clamped by introducing the fixity factor. The obtained results are compared with those available in the literature, as well as with a numerical FEM model, whereby good agreement is observed. The significant difference when using the proposed model to analyze a thick plate, as opposed to the models based on Kirchhoff theory, is underlined. Full article

This paper investigates a five-level active neutral-point-clamped (5L-ANPC) converter operating in rectifier mode with unbalanced DC-side loads, where neutral-point (NP) deviation may deteriorate grid-current quality. Conventional space-vector pulsewidth modulation (SVPWM) is typically derived under the split-capacitor-voltage symmetry assumption; when NP deviation occurs, fixed sector boundaries and ideal volt–second balance calculations can lead to sector misclassification and synthesis errors. To address this issue, an NP-aware SVPWM scheme is proposed by reconstructing sector criteria using real-time capacitor voltages and correcting the vector dwelling-time computation to improve modulation accuracy under imbalance. Based on the power-transfer mechanism, an average-power boundary condition is further derived to quantify the admissible upper/lower load power ratio that allows NP regulation without additional hardware, and its validity is examined under resistive-load cases. Moreover, for battery-type loads exhibiting voltage-source characteristics, the control objective is extended from voltage symmetry to controllable power/charge allocation by establishing a mapping between the small-vector duty ratio and the branch average-power ratio, with constrained online solution and smoothing to mitigate coefficient jitter. Experimental validation is conducted on an OPAL-RT OP5707-based hardware-in-the-loop platform, where both single-phase and three-phase 5L-ANPC systems are implemented according to different verification objectives. The derived boundary condition for resistive loads is examined in the single-phase system, while the proposed modulation and battery-load power-allocation strategy are verified in the three-phase system. The three-phase arrangement is adopted for the battery-load case in order to avoid the second-order power ripple inherent to single-phase operation. Full article

This paper presents a yarn tension sensor based on Surface Acoustic Waves (SAW). To enhance the detection accuracy of the sensor, an improved beam structure is designed for tension measurement, along with intelligent algorithms for temperature compensation. Firstly, regarding the sensor structure, a simply supported beam with a hyperbolic surface is designed to achieve stress concentration by reducing the section modulus at the beam’s midpoint. Secondly, by incorporating an unbalanced split-electrode Interdigital Transducer (IDT) design, the sensor effectively suppresses signal sidelobe interference and significantly improves the structure’s tension sensitivity. Finally, in terms of signal processing, to eliminate the influence of environmental temperature fluctuations on measurements, a temperature-compensation algorithm based on Bayesian Optimization Least Squares Support Vector Machine (BO-LSSVM) with Gaussian Process regression is proposed. Experimental results show that the tension sensitivity of the improved structure was 8.2% higher than that of the doubly clamped beam and 12.7% higher than that of the cantilever beam. For temperature compensation, the BO-LSSVM model reduced the Mean Relative Error (MRE) by 5.67 percentage points relative to raw data and by 2.04 percentage points relative to the fixed-parameter LSSVM model, lowering the temperature sensitivity coefficient from 4.09 $(\times {10}^{-3}/°\mathrm{C})$ to 0.41 $({10}^{-3}/°\mathrm{C})$. Full article

Most components fail under complex states of stress and for this reason the study of materials failure under these conditions is an important topic. The article presents the experimental study of the failure of a CFRP material, with a 0/90° cross-ply configuration, subjected to both simple loading conditions (tension, compression, and shear) and combined loading (tension–shear), using a modified Arcan testing method. The Arcan device and specimen geometry were redesigned to reduce experimental errors and the dispersion of results. It was found that there are significant differences between the strength values obtained for simple loads performed by the standardized methods and by the Arcan method, respectively. For this reason, it is recommended to use the Arcan method only for mixed loading modes. Specimens with steel tabs were used to reduce both hole ovality during testing and the number of clamping screws to only four. It was found that the experimental results under complex stress states are well described by the Tsai–Hill failure criterion and the failure envelope for the material studied was plotted. Recommendations are provided regarding the appropriate use of the Arcan method in order to obtain precise results for CFRP composites under multiaxial loading. Full article

This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R² values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R² value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R² of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity. Full article

This study proposes a methodology for selecting robust stable cutting conditions from a Receptance Coupling Substructure Analysis (RCSA)-based Stability Lobe Diagram (SLD) by considering tool clamping errors that may occur during operator tool setup. However, most existing RCSA studies have been conducted under the assumption of a constant tool clamping length and thus do not sufficiently reflect the clamping length variation observed in practical machining environments. Since the tool tip dynamic characteristics can be sensitive even to small variations in clamping length, operator-induced tool clamping errors in actual processes can introduce such variations and consequently degrade the prediction accuracy of the SLD. Moreover, uncertainty studies in milling stability have largely focused on variations in model parameters, such as cutting coefficients, damping, and modal parameters, whereas experimental quantification of operator-induced clamping length variability and its direct integration into RCSA-based tool tip Frequency Response Function (FRF) and SLD prediction has been relatively limited. Therefore, this study quantifies the distribution of tool clamping errors through clamping experiments and incorporates it into RCSA to derive an SLD band that accounts for tool clamping errors. The width of the SLD band is defined as a physical variation induced by clamping uncertainty, and the corresponding uncertainty range is set as an avoidance region. Robust cutting conditions are then selected from the remaining stable region while considering the physical variation width. The physical variation width was quantified as 60 rpm (minor axis) and 1.62 mm (major axis), representing the dispersion of the stability limit in the spindle speed and axial depth directions caused by clamping errors. As a result, stable cutting conditions that do not cross the stability limit can be determined even in the presence of process variations and disturbances. Full article

Kolmogorov–Arnold Networks employ learnable univariate activation functions on edges rather than fixed node nonlinearities. Standard B-spline implementations require $O\left(3KW\right)$ parameters per layer (K basis functions, W connections). We introduce shared Gaussian radial basis functions with learnable centers ${\mu }_k^{\left(l\right)}$ and widths ${\sigma }_k^{\left(l\right)}$ maintained globally per layer, reducing parameter complexity to $O(KW+2LK)$ for L layers—a threefold reduction, while preserving Sobolev convergence rates $O\left(h^{s-\mathrm{\Omega }}\right)$. Width clamping at ${\sigma }_{min}={10}^{-6}$ and tripartite regularization ensure numerical stability. On MNIST with architecture $[784,128,10]$ and $K=5$, RBF-KAN achieves $87.8\%$ test accuracy versus $89.1\%$ for B-spline KAN with $1.4\times$ speedup and 33% memory reduction, though generalization gap increases from $1.1\%$ to $2.7\%$ due to global Gaussian support. Physics-informed neural networks demonstrate substantial improvements on partial differential equations: elliptic problems exhibit a $45\times$ reduction in PDE residual and maximum pointwise error, decreasing from $1.32$ to $0.18$; parabolic problems achieve a $2.1\times$ accuracy gain; hyperbolic wave equations show a $19.3\times$ improvement in maximum error and a $6.25\times$ reduction in $L^2$ norm. Superior hyperbolic performance derives from infinite differentiability of Gaussian bases, enabling accurate high-order derivatives without polynomial dissipation. Ablation studies confirm that coefficient regularization reduces mean error by 40%, while center diversity prevents basis collapse. Optimal basis count $K\in [3,5]$ balances expressiveness and overfitting. The architecture establishes Gaussian RBFs as efficient alternatives to B-splines for learnable activation networks with advantages in scientific computing. Full article