Search Results (27)

For eyes-free operation of input interfaces, tactile feedback is increasingly recognized as an important means of transmitting intuitive information. In particular, auxiliary keypads designed for creators such as illustrators and designers can cause fatigue and input errors during prolonged use. To address these issues, we propose a tactile device that delivers input feedback directly through a single keytop. Conventional haptic actuators, such as eccentric rotating mass motors (ERMs) and linear resonant actuators (LRAs), have limitations, including vibration of the entire structure in which they are installed and operational noise. Therefore, in this study, we adopted shape memory alloy (SMA) wire actuators to achieve localized stimulation and silent operation. By integrating three SMA actuators into a keytop, the proposed tactile keytop can present various types of feedback to users. The vibration characteristics of the SMA actuator were analyzed using a high-speed camera, and the results confirmed stable micro-vibration control. User experiments confirm high recognition accuracy in the tactile presentation of both spatial directional patterns and temporal rhythm patterns. In addition, qualitative evaluations demonstrate that driving frequency adjustment enables the presentation of a diverse range of tactile sensations. These findings indicate that the proposed tactile keytop has potential as a localized tactile feedback interface for future eyes-free input systems. Full article

Excessive vibration during critical speed traversal remains a primary challenge in assembling multi-stage rotors of aero-engines. Conventional assembly optimization methods, which target static geometric and mass eccentricity errors or vibration at a fixed operating speed, are inadequate to ensure smooth passage through multiple critical speeds. To address this gap, we propose a novel, vibration-suppression-oriented assembly optimization model. A normalized objective function is formulated to minimize the overall vibration response across multiple rotor nodes specifically at the first and second critical speeds. This function integrates an assembly error propagation model with a rotor dynamic model that considers flexible dynamic deflection. The optimal assembly angle sequence is solved using a genetic algorithm. Experimental validation on a four-stage rotor demonstrates that the proposed method reduces the maximum vibration displacement amplitude at the first and second critical speeds by 74.7% and 11.9%, respectively, significantly outperforming conventional objectives based on geometric error, unbalanced mass, or single-speed vibration. This work provides a practical and effective strategy to enhance rotor dynamic safety by ensuring low-vibration operation across the critical speeds encountered before reaching the operating speed through optimal assembly. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

The precise prediction of vibration response is crucial for optimizing the assembly quality of multi-stage rotors. Existing models possess two key limitations: they neglect the geometric displacement excitation from spigot eccentricity error and oversimplify rotor behavior by not accounting for the excitation redistribution caused by significant dynamic deflection at high speeds, particularly near critical speeds. To overcome these shortcomings, this study establishes a novel dynamic model based on the synchronous excitation of both mass and spigot eccentricity errors, which simultaneously incorporates the coupling mechanism of dynamic deflection. System dynamics equations are developed using a finite element approach combined with Timoshenko beam theory and solved via the Newmark-β method. Simulations and experiments on a four-stage rotor demonstrate that the proposed model provides significantly improved accuracy. At sub-critical, first, and second critical speeds, it reduces the maximum prediction error for nodal displacement amplitudes by 6.1%, 9.2%, and 36.4%, respectively, compared to a model neglecting dynamic deflection. Furthermore, analysis confirms that the targeted assembly error excitation exists solely at the fundamental frequency. The developed model, which uniquely integrates dual eccentricity sources with dynamic deflection coupling, is essential for reliable high-speed vibration prediction and assembly optimization, especially for flexible rotors operating near critical speeds. Full article

Affordable high-frame-rate cameras and open-source software, such as Kinovea (ver. 2025.1.0), have expanded the potential for conducting kinematic assessments outside laboratory settings. This study examined the reliability and validity of Kinovea’s semi-automated linear kinematics tracking tool by comparing its outputs with those from a 3D marker-based motion capture system (Qualisys). Ten recreationally active male basketball players (x̄ ± SD: age 23.7 ± 1.7 years; height 183 ± 5 cm; body mass 76.8 ± 9.8 kg) performed three CMJ trials, simultaneously recorded using both systems. Reflective markers placed on the shoulder, hip, and knee were tracked in Kinovea by two raters with different levels of experience to extract core CMJ variables (total take-off time and maximum vertical displacement) and complementary variables (eccentric and propulsion duration, and minimum vertical displacement). Inter-rater reliability and concurrent validity were evaluated using intraclass correlation coefficients (ICCs), coefficients of variation (CV%), standard error of measurement (SEM), and Bland–Altman analysis. Results showed excellent inter-rater reliability (ICC = 0.73–0.99) across all markers, with the hip and knee demonstrating the highest consistency. Strong validity relative to Qualisys was observed for both raters (ICC = 0.68–0.99; r > 0.80), with small systematic biases primarily in temporal variables. Collectively, these findings demonstrate that Kinovea’s semi-automated 2D analysis yields reliable and valid CMJ measurements comparable to 3D motion capture, even for less experienced users. As a free and easily deployable tool, it offers a widely accessible alternative for field-based performance monitoring and applied biomechanics research where laboratory-grade equipment is not available. Full article

Rubber bearings play a critical role as core components within the transmission systems of marine equipment. Investigating the evolution of their water-film stiffness coefficient under deep-sea conditions can provide deeper insights into the dynamic characteristics of water-lubricated transmission systems. Employing a viscoelastic mixed-lubrication framework designed for water lubricated rubber bearings, this paper examines the necessity of accounting for rubber hyperelasticity and extreme subsea conditions (high pressure and low temperature) when analyzing the water-film stiffness coefficient of such bearings (at a depth of 1000 m, the relative error in the k_xz component between the linear viscoelastic model and the visco-hyperelastic model reaches as high as 18.41%.). On this basis, the influence of subsea environments together with rotational velocity on the water-film stiffness coefficient is further investigated, and the dependence of the dimensionless critical mass on the eccentricity ratio for water-lubricated rubber bearings operating under deep-ocean conditions is explored. The results provide a theoretical analysis tool for evaluating the water-film stiffness coefficient of subsea rubber bearings, and offer guidance for the forward design of water-lubricated rubber bearings applied in deep-sea service. Full article

Gear transmission error (GTE) is a critical factor influencing the performance and service life of gear systems, as it directly contributes to vibration, noise generation, and premature wear. The present study introduces a combined theoretical and experimental approach to characterizing GTE in a single-stage spur gear system. A six-degree-of-freedom nonlinear dynamic model was formulated to capture coupled lateral–torsional vibrations, accounting for gear mesh stiffness, bearing and coupling characteristics, and a harmonic transmission error component representing manufacturing and assembly imperfections. Simulations and experiments were conducted under healthy and eccentricity-faulted conditions, where a controlled 890 g eccentric mass induced misalignment. Frequency domain inspection of faulty gear data showed pronounced sidebands flanking the gear mesh frequency near 200 Hz, as well as harmonics extending from 500 Hz up to 1200 Hz, in contrast with the healthy case dominated by peaks confined to 50–100 Hz. STFT analysis revealed dispersed spectral energy and localized high-intensity regions, reinforcing its role as an effective fault diagnostic tool. Experimental findings aligned with theoretical predictions, demonstrating that the integrated modelling and time–frequency framework is effective for early fault detection and performance evaluation of spur gear systems. Full article

This study presents a machine learning-based framework for predicting the electrical output of a vibration energy harvesting system (VEHS) integrated with a Jeffcott rotor model. Vibration induced by rotor imbalance is converted into electrical energy via piezoelectric elements, and the system’s dynamic response is simulated using the fourth-order Runge–Kutta method across varying mass ratios, rotational speeds, and eccentricities. The resulting dataset is validated experimentally with a root-mean-square error below 5%. Three predictive models—Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost)—are trained and evaluated. While DNN and LSTM yield a high predictive accuracy (R² > 0.9999), XGBoost achieves comparable accuracy (R² = 0.9994) with significantly lower computational overhead. The results demonstrate that among the tested models, XGBoost provides the best trade-off between speed and accuracy, achieving R² > 0.999 while requiring the least training time. These results demonstrate that XGBoost might be particularly suitable for real-time evaluation and edge deployment in rotor-based VEHS, offering a practical balance between speed and precision. Full article

In hub-motor electric vehicles (HMEVs), performance is adversely affected by the mechanical-electromagnetic coupling effect arising from deformations of the air gap in the Permanent Magnet Brushless Direct Current Motor (PM BLDC), which are exacerbated by varying road conditions. In this paper, a Model Predictive Control (MPC) strategy for HMEVs equipped with air suspension (AS) is introduced to enhance ride comfort. Firstly, an 18-degree of freedom (DOF) full-vehicle model incorporating unbalanced electromagnetic forces (UEMFs) induced by motor eccentricities is developed and experimentally validated. Additionally, a Minimum Model Error Extended Kalman Filter (MME-EKF) observer is designed to estimate unmeasurable state variables and account for errors resulting from sprung mass variations. To further improve vehicle performance, the MPC optimization objective is formulated by considering the suspension damping force and dynamic displacement constraints, solving for the optimal suspension force within a rolling time domain. Simulation results demonstrate that the proposed MPC approach significantly improves ride comfort, effectively mitigates coupling effects in hub driving motors, and ensures that suspension dynamic stroke adheres to safety criteria. Comparative analyses indicate that the MPC controller outperforms conventional PID control, achieving substantial reductions of approximately 41.59% in sprung mass vertical acceleration, 14.29% in motor eccentricity, 1.78% in tire dynamic load, 17.65% in roll angular acceleration, and 16.67% in pitch angular acceleration. Full article

To establish a high-precision prediction model for the material removal rate (MRR) of TC4 titanium alloy material in vertical vibratory finishing equipment, an orthogonal experiment was conducted using TC4 titanium alloy plate as the experimental specimen. We performed variance analysis of the impact of vibration frequency, the phase difference, the mass of upper eccentric block, and the mass of lower eccentric block on the MRR. We then drew the main effect diagram and analyzed the influence of various process parameters on the MRR. Mathematical regression and a neural network were used to construct predictive models for the MRR with respect to process parameters, and a genetic algorithm (GA) was coupled to optimize the neural network to improve the predictive performance of the model. By calculating the R², validating the set sample prediction error, and averaging the absolute percentage error (MAPE) of each model, it was found that the neural network model had better prediction performance than the mathematical regression model, with an accuracy of 82.2%. After coupling with the GA, the prediction accuracy reached 95.5%. The research results indicated that, compared with mathematical regression and the original neural networks, the neural network coupled with the GA had better predictive performance, providing an effective method for predicting the MRR in vertical vibratory finishing. Full article

To meet the high-performance demands of electric vehicle reducers, the authors present a novel approach for parameter matching in electric drive systems for electric vehicles. Based on fuzzy evaluation, the method identifies the optimal performance parameters for the entire vehicle and determines the best transmission ratio scheme for the reducer. The gear geometrical parameters of the reducer are derived using the transmission ratio scheme as the constraint condition. An 18-degree-of-freedom dynamic system model, incorporating factors such as time-varying mesh stiffness, the backlash, time-varying transmission error and mass eccentricity, has been built using Matlab. The dynamic response of the gear system under the designed gear parameter scheme have been analyzed and compared with the gear parameter results obtained using KISSsoft, demonstrating the accuracy and rigour of the design. This research has practical implications for the design and performance optimization of electric vehicle reducers. Full article

Borehole imaging technology is a critical means for the meticulous measurement of rock mass structures. However, the inherent issue of probe eccentricity significantly compromises the quality of borehole images obtained during testing. This paper proposes a method based on grayscale feature analysis for reverse positioning of imaging probes and image restoration. An analysis of the response characteristics of probe eccentricity was conducted, leading to the development of a grayscale feature model and a method for reverse positioning analysis. By calculating the error matrix using the probe’s spatial trajectory, this method corrects and restores grayscale errors caused by probe eccentricity in images. Quantitative analysis was conducted on the azimuthal errors in borehole images caused by probe eccentricity, establishing a method for correcting image perspective errors based on probe spatial-positioning calibration. Results indicate significant enhancement in the effectiveness and measurement accuracy of borehole images. Full article

In rotating machinery with a multi-stage assembled rotor, such as is found in aero engines, any unbalance present will undergo unknown changes at each stage when rotating the assembly phases of the rotor. Repeated disassembly and adjustments are often required to meet the rotor’s residual unbalance specifications. Therefore, developing a prediction model of this two-sided unbalance for a multi-stage assembled rotor is crucial for improving the first-time assembly pass rate and assembly efficiency. In this paper, we propose a prediction model of the two-sided unbalance seen in the multi-stage assembled rotor of an aero engine. Firstly, a method was proposed to unify the mass feature parameters of each stage’s rotor into a geometric measurement coordinate system, achieving the synchronous transmission of geometric and mass feature parameters during the assembly process of the multi-stage rotor. Building upon this, a linear parameter equation of the actual rotation axis of the multi-stage rotor was established. Based on this axis, the mass eccentricity errors of the rotor were calculated at each stage, further enabling the accurate prediction of two-sided unbalance and its action phase in a multi-stage rotor. The experimental results indicate that the maximum prediction errors of the two-sided unbalance and its action phase for a four-stage rotor are 9.6% and 2.5%, respectively, when using this model, which is a reduction of 53.0% and 38.1% compared to the existing model. Full article

The combined application of steel–FRP composite bars (SFCBs) and seawater sea-sand concrete (SSSC) in marine engineering not only solves the problem of resource scarcity and reduces the construction cost but also avoids the problems of chloride corrosion of steel reinforcement in seawater sea-sand concrete and the lack of ductility of FRP bars. At the same time, the addition of glass fiber (GF) and expansion agent (EA) in appropriate amounts improves the crack resistance and seepage resistance of concrete. However, the durability of SFCB with GF- and EA-reinforced SSSC in freezing–thawing environment remains unclear, which limits its potential application in cryogenic marine engineering. This study investigates the bonding properties between SFCB and GF-EA-SSSC interfaces using eccentric pullout experiments under different thicknesses of concrete protective cover and a number of freezing–thawing cycles. The results showed that the compressive strength and dynamic elastic modulus of SSSC decrease, while the mass loss increases with an increasing number of freezing–thawing cycles. Additionally, the bond strength and stiffness between SFCB and SSSC decrease, leading to an increase in relative slip. However, the rate of bond strength and stiffness loss decreases with an increase in the thickness of the concrete protective cover. Furthermore, formulas for bond strength, relative slip, and bond stiffness are established to quantify the effects of the thickness of the concrete protective cover and the number of freezing–thawing cycles. The experimental values obtained verify the accuracy of these formulas, with a relative error of less than 5%. Moreover, a bond stress–slip constitutive model is developed for SFCB and GF-EA-SSSC, and the fitting results closely resemble the experimental values, demonstrating a high level of model fit. Full article

The deflection dynamic load allowance (DLA) of stiff bridges with high piers requires sub-millimeter accuracy. New technologies such as the vision-based optical method and GNSS are not yet recognized for use in DLA measurements due to their smaller SNR. Presently, the scaffolding method is widely utilized for dynamic deflection measurements in dynamic load tests owing to the reliability of employing rigid contact. When scaffolding is not available, engineers have to resort to a suspension hammer system. However, the mass eccentricity of the hammer, stretched-wire length, and wind will decrease the measurement accuracy. To overcome these drawbacks of the suspension hammer method (SHM), a preloaded spring method (PSM) and the related stretched-wire-spring system (SWSS) are proposed in this paper. The dynamic deflection of the coupled vehicle-bridge-SWSS was obtained by vehicle-bridge interaction (VBI) analysis. The sensitivity parameters of the PSM were analyzed and optimized to minimize the measurement error. Indoor experiments and field dynamic load tests were conducted to validate the feasibility and accuracy of the PSM. Additionally, the differences in dynamic deflection measurements between the PSM and SHM in windy environments were compared. The results show that, in a windless environment, the DLAs of the PSM are affected by the spring stiffness, stretched-wire length, and stretched-wire section stiffness, independently of the preload force. When the wind speed is less than or equal to 8 m/s and the pier height is less than 30 m, the maximum deflection measurement error of the PSM is −2.53%, while that of the SHM is −15.87%. Due to its low cost and high accuracy, the proposed method has broad application prospects in the dynamic deflection measurement of stiff bridges with high piers. Full article