Search Results (851)

This work presents a first-iteration bio-faithful dragonfly-inspired wing designed for future flapping micro air vehicle (MAV) applications. Using high-resolution imaging, the natural venation pattern of fore- and hindwings was reconstructed in CAD and reproduced through high-precision stereolithography at 1:1 and 3:1 scale. The printed polymeric wings successfully preserved the anisotropic stiffness distribution of the biological structure, enabling realistic bending and torsional responses. Modal analysis and dynamic testing confirmed that the lightweight designs operate within the biologically relevant 20–40 Hz range and that geometry and material choices allow predictable tuning of natural frequencies. Preliminary aerodynamic estimates captured the characteristic anti-phase lift behavior of four-wing flapping, while schlieren and infrared thermography demonstrated that heat dispersion and flow features follow the vein-driven structural pathways of the printed wings. Together, these results validate the feasibility and functional relevance of bio-faithful venation architectures and establish a solid foundation for future iterations incorporating membranes, full kinematic actuation, and higher-fidelity aeroelastic modeling. Full article

To address trajectory tracking of underactuated unmanned surface vessels (USVs) under disturbances and model uncertainty, we propose a hierarchical control framework that combines model predictive control (MPC) with proximal policy optimization (PPO). The outer loop runs in the inertial reference frame, where an MPC planner based on a kinematic model enforces velocity and safety constraints and generates feasible body–fixed velocity references. The inner loop runs in the body–fixed reference frame, where a PPO policy learns the nonlinear inverse mapping from velocity to multi–thruster thrust, compensating hydrodynamic modeling errors and external disturbances. On top of this framework, we design a Proactive–Reactive Adaptive Reward (PRAR) that uses the MPC prediction sequence and real–time pose errors to adaptively reweight the reward across surge, sway and yaw, improving robustness and cross–model generalization. Simulation studies on circular and curvilinear trajectories compare the proposed PRAR–driven dual–loop controller (PRAR–DLC) with MPC–PID, PPO–Only, MPC–PPO and PPO variants. On the curvilinear trajectory, PRAR–DLC reduces surge MAE and maximum tracking error from 0.269 m and 0.963 m (MPC–PID) to 0.138 m and 0.337 m, respectively; on the circular trajectory it achieves about an 8.5% reduction in surge MAE while maintaining comparable sway and yaw accuracy to the baseline controllers. Real–time profiling further shows that the average MPC and PPO evaluation times remain below the control sampling period, indicating that the proposed architecture is compatible with real–time onboard implementation and physical deployment. Full article

Ester-based lubricants have been widely used owing to their excellent overall performance. In this study, the quantitative structure–property relationship (QSPR) approach was combined with molecular descriptors, a genetic algorithm (GA), and an artificial neural network (ANN) to systematically predict the key properties—kinematic viscosity at 40 °C and 100 °C, viscosity index, pour point, and flash point—of 64 diester-based lubricants. Quantum chemical calculations were first performed to obtain the equilibrium geometries and electronic information of the molecules. Geometry optimizations and frequency analyses were carried out using the Gaussian 16 software at the B3LYP/6-31G (d, p) level, providing a reliable foundation for molecular descriptor computation. Subsequently, topological, geometrical, and electronic descriptors were calculated using the RDKit toolkit, and the optimal feature subsets were selected by GA and used as ANN inputs for property prediction. The results showed that the ANN models exhibited good performance in predicting viscosity and flash point, with R² values of 0.9455 and 0.8835, respectively, indicating that the ANN effectively captured the nonlinear relationships between molecular structure and physicochemical properties. In contrast, the prediction accuracy for pour point was relatively lower (R² = 0.6155), suggesting that it is influenced by complex molecular packing and crystallization behaviors at low temperatures. Overall, the study demonstrates the feasibility of integrating quantum chemical calculations with the QSPR–ANN framework for lubricant property prediction, providing a theoretical basis and data-driven tool for molecular design and performance optimization of ester-based lubricants. Full article

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

To enhance the adaptability and human-machine coordination of intelligent prosthetic knees, this study proposes a motion intention mapping direct myoelectric control method based on an LSTM network and a human-machine coupling model. Multichannel surface electromyography (sEMG) and knee joint angle data were collected during level-ground walking. Time-domain features were extracted to construct an LSTM prediction model, enabling temporal mapping between muscle activity and joint kinematics. Experimental results show that the LSTM model outperforms traditional neural networks in terms of prediction accuracy and temporal consistency. Furthermore, by integrating the human-machine coupling dynamics model with a hydraulic actuation system, a direct myoelectric control framework for a variable-damping prosthetic knee was established, achieving continuous damping adjustment and smooth gait transition. The results verify the feasibility and effectiveness of the proposed method in human-machine coordinated control. Full article

Satellite clock bias exhibits complex, time-varying periodic characteristics due to environmental disturbances. Accurate modeling and prediction of periodic terms play a crucial role in improving the precision and stability of short-term predictions. Traditional models such as spectral analysis model (SAM) estimate the frequency, amplitude, and phase of periodic terms through global fitting, which limits their ability to adapt to abrupt changes at the prediction boundary. To address this limitation, this paper proposes an improved spectral analysis model (IM-SAM) based on the inaction method (IM). The model employs IM to extract the instantaneous frequency, amplitude, and phase parameters of periodic terms precisely at the data endpoint, and utilizes the parameters of periodic terms at the data endpoint for prediction, effectively suppressing periodic fluctuations in prediction errors. Experimental results based on real GPS clock bias data demonstrate that the root mean square (RMS) of IM-SAM prediction errors is reduced by 19.14%, 14.39%, and 10.48% for 3 h, 6 h, and 12 h prediction tasks, respectively, compared with SAM. Furthermore, a kinematic precise point positioning experiment was performed using IM-SAM-predicted clock products and compared with the predicted half of IGS ultra-rapid clock products. The RMS of position error was reduced by 14.3%, 12.6%, and 7.9% in the east, north, and up directions, respectively. These results demonstrate the practical effectiveness and accuracy of IM-SAM in real-time clock prediction and GPS positioning applications. Full article

Drilling in aerospace one-up assembly demands high positional accuracy, strong clamping forces, and precise angular compensation to ensure quality in multi-layered stacks. Existing robotic solutions achieve these requirements but are costly, bulky, and unsuitable for flexible or collaborative environments. This work introduces the Advanced Collaborative Multifunctional End-Effector (ACME), a lightweight robotic drilling end-effector designed for integration with collaborative robots (cobots). ACME incorporates vacuum-assisted clamping capable of generating high forces, a passive self-normalization mechanism for angular alignment on double-curvature surfaces, and a compact 5-DoF positioning system for precise positioning and orientation. The system’s kinematics and dynamics were modeled and experimentally verified through frequency response function (FRF) testing, enabling precise behavior prediction. The tool is integrated within a cyber–physical system (CPS) featuring an interactive digital twin that, unlike passive monitoring systems, allows operators to configure workpieces, select drilling locations directly from rendered CAD, and supervise execution without programming expertise. Experiments demonstrated average positional errors of 0.19 mm and normality deviations of 0.29°, both within aerospace standards. The results confirm that ACME effectively extends cobot capabilities for aerospace-grade drilling while improving flexibility, safety, and operator accessibility. Full article

Heat exchangers–adsorbers (HEX-As) are emerging as innovative technologies in many applications (CO₂ capture, gas purification and separation, thermal energy storage, etc). This review addresses the theoretical challenges within computational fluid dynamics (CFD) in modeling and simulating coupled heat and mass transfer within gas separation by using adsorbing porous media in fixed beds. Conservation equations of mass, momentum, and energy from different studies (1D, 2D-CFD, and 3D-CFD models) are presented and discussed with an emphasis on their ability to predict the complex multi-physics multi-scale heat and mass transfer phenomena involved, such as the adsorption kinematics, the thermal front propagation, and the multi-component fluid flow dynamics inside the beds. For the fist time, we show that mathematical theoretical modeling in CFD has been differently developed and applied by many authors in the literature in order to model the same physical phenomena. This sheds light on the present challenges and bottlenecks in theoretical and computational fluid dynamics when it comes to complex coupled heat and mass transfer in multi-component gas dynamics in porous media. This review make it easier for readers to understand the different models that exist in the literature for modeling and simulating HEX-As. It also opens questions on how accurately one can model multi-functional heat exchangers–adsorbers using CFD, e.g., physics multi-scale extrapolation from nano- to meso- and then to macro-scale behavior. Full article

This paper presents a study that investigates the operational reliability of directional control valves used in hydraulic press systems by applying the Taguchi method and regression analysis. The research focuses on key hydraulic parameters—kinematic viscosity, internal leakage, pressure, and temperature—to identify their influence on valve reliability. Three valves (DCV1–DCV3) were tested under identical conditions using an L8 orthogonal array to optimize the experimental design while maintaining statistical validity. The Taguchi analysis revealed that internal leakage is the dominant factor affecting valve reliability, consistently confirmed across all statistical evaluations, including signal-to-noise (S/N) ratios and ANOVA results. Regression models were developed for each valve to quantify the effect of each factor and showed excellent predictive accuracy (R² > 98%). The study concludes that minimizing internal leakage, maintaining lower temperatures, and applying higher operating pressures significantly enhance valve reliability, while viscosity had negligible effect within the tested range. Valve DCV2 demonstrated the highest predicted reliability. These findings offer valuable insights for the optimization of hydraulic valve design and maintenance strategies, contributing to the improved performance and longevity of industrial hydraulic systems. Full article

In the present study, numerical simulations were conducted to investigate the behavior of a 60° inclined dense jet discharged onto horizontal (0°) and sloped (5°) bottoms in a stagnant environment. The objective was to evaluate the capability of Large Eddy Simulation (LES) in capturing both the kinematic and mixing characteristics of inclined dense jets interacting with different bottom boundaries. A Reynolds-Averaged Navier–Stokes (RANS) model was also included for comparison. The LES simulations successfully reproduced the key kinematic and mixing characteristics, including the jet trajectory, centerline peak location, impact point, and terminal rise height, and showed strong agreement with the experimental observations. LES also predicted the concentration distributions and variations along both the horizontal and sloped bottoms, whereas the RANS model tended to underestimate both geometrical and dilution properties. A Gaussian fitting function was proposed to estimate the concentration distribution under both bottom conditions. Analysis of the spreading layer indicated that the concentration profiles exhibited self-similarity. Energy spectrum analysis showed that the sloped bottom enhanced shear-induced turbulence, thereby improving the mixing efficiency. Results confirm the reliability of LES for describing jet–bed interactions and emphasize the influence of bed slope on jet dilution and mixing behavior. Full article

Studies on autonomous underwater vehicles (AUVs) have gained momentum in recent years, and a special type of AUV, the robotic fish, has become a significant topic, with a superior maneuverability to traditional AUVs. In this paper, a prediction strategy for the hydrodynamic performance of a robotic fish to analyze undulatory swimming behaviors is proposed. The two-dimensional robotic fish model for computational fluid dynamics (CFD) simulations is constructed, and a dynamic network method is applied to orient the generated network based on the wavy motion. For the thrust force of the fin, a body traveling wave is derived. In the simulations, the effects of kinematic parameters such as flapping frequency and speed on swimming efficiency and drag are analyzed, and thrust force production, power expenditure, and overall efficiency of swimming are examined. Later, a deep learning-based prediction model is designed from the obtained parameters, and force predictions are performed. Long short-term memory (LSTM)-, convolutional neural network (CNN)-, and gated recurrent network (GRU)-based time series prediction models are used, and their variations are compared. In these experiments, while the CNN-GRU achieves the higher prediction performance for the root mean square error, with 0.0228, other approaches give a lower performance, between 0.0233 and 0.0359. The proposed method demonstrates a superior performance in CNN and LSTM models and exhibits lower prediction errors. Full article

Reliable fault diagnosis in industrial robots is essential for minimizing downtime and ensuring safe operations. Conventional model-based methods often require detailed system knowledge and struggle with unmodeled dynamics, while purely data-driven approaches can achieve good accuracy but may not fully exploit the underlying structure of robot motion. In this study, we propose a feature-informed machine learning framework for fault detection in robotic manipulators. A multi-layer perceptron (MLP) is trained to estimate robot dynamics from joint states, and SHapley Additive exPlanations (SHAP) values are computed to derive discriminative feature representations. These attribution patterns, or SHAP fingerprints, serve as enhanced descriptors that enable reliable classification between normal and faulty operating conditions. Experiments were conducted using real-world data collected from industrial robots, covering both motor brake faults and reducer anomalies. The proposed SHAP-informed framework achieved nearly perfect classification performance (0.998 ± 0.003), significantly outperforming baseline classifiers that relied only on raw kinematic features (0.925 ± 0.002). Moreover, the SHAP-derived representations revealed fault-consistent patterns, such as enhanced velocity contributions under frictional effects and joint-specific shifts for reducer faults. The results demonstrate that the proposed method provides high diagnostic accuracy and robust generalization, making it well suited for safety-critical applications and predictive maintenance in industrial robotics. Full article

As the use of industrial robotic arms (RAs) increases, effective energy management has become a critical requirement for manufacturing competitiveness and sustainability. However, existing power prediction models are often based on complex kinematic or dynamic formulations, limiting their applicability on the shop floor. To address this challenge, this study develops an evaluation framework for regression-based RA power prediction models that integrates accuracy, explainability, and practical considerations. Specifically, 162 statistical and machine-learning models are evaluated in terms of model type, movement type, training data size, and training time. The results show that the support vector machine (SVM) consistently outperforms other models in both accuracy and computational efficiency, while the multilayer perceptron (MLP) performs the worst. Using Shapley additive explanations (SHAP), the framework also clarifies how the most effective models capture the physical characteristics of RA movements embedded in power data. Moreover, the analysis reveals that similar movement patterns, such as along the X and Y axes, can result in distinct power demands. These findings highlight the need for explainable and practical prediction models to support energy-efficient RA operations and provide shop-floor engineers with actionable insights into the physical mechanisms driving power demand. Full article

Autonomous driving, a transformative automotive technology, is currently a major research focus. Trajectory planning, one of the three core technologies for realizing autonomous driving, plays a decisive role in the performance of autonomous driving systems. The key challenge lies in planning an optimal trajectory based on real-time environmental information, yet significant research gaps remain, particularly for dynamic driving scenarios. To address this, our study investigates lane-changing trajectory planning in dynamic scenarios based on model predictive control (MPC) theory and proposes a novel dynamic lane-changing trajectory planning method. First, kinematic models for both the host vehicle and surrounding vehicles are established. Then, following the core components of MPC theory, we construct a prediction model, define an objective function, and formulate constraints for the rolling optimization step. Finally, the optimal control sequence derived from the optimization is processed using a least-squares fitting method to generate a lane-changing trajectory that demonstrates real-time adaptability in dynamic environments. The proposed method is validated through simulation studies of three typical driving conditions on a co-simulation platform. The results confirm that the planned trajectory exhibits excellent dynamic real-time adaptability, thereby contributing a foundation for achieving full-scenario autonomous driving. Full article

This study presents a data-driven, machine learning-based approach to the dimensional synthesis of planar four-link slider–crank function generators. The proposed methodology integrates kinematic analysis to generate physically feasible datasets that capture the relationship between linkage dimensions and the precision points of slider–crank linkages. To synthesize valid, defect-free linkages for an arbitrary number of user-defined precision points, a customized Long Short-Term Memory (LSTM)-based model is developed and trained on the generated dataset. A parameterization scheme for the linkage dimensions is introduced to ensure prediction-level validity, enabling stable convergence and physically realizable predictions. Numerical results demonstrate high accuracy and robustness under both absolute and relative precision-point specifications, despite the model being trained solely on absolute precision points without any initial configuration estimation. In addition to deriving feasible linkage dimensions, the proposed method offers a practical and scalable framework for engineering design applications. Full article