Search Results (119)

This work presents an analytical 2D model for estimating eddy current losses in the permanent magnets (PMs) of a coaxial magnetic gear (CMG), with a focus on loss minimization through magnet segmentation. The model is applied under various operating conditions, including different rotational speeds, load levels, and segmentation configurations, to derive empirical expressions for eddy current losses in both the inner and outer rotors. A 1D lumped-parameter thermal model is then used to predict the steady-state temperature of the PMs, incorporating empirical correlations for the thermal convection coefficient. Both models are validated against finite element analysis (FEA) simulations. The analytical eddy current loss model exhibits excellent agreement, with a maximum error of 2%, while the thermal model shows good consistency, with a maximum temperature deviation of 5%. The results confirm that eddy current losses increase with rotational speed but can be significantly reduced through magnet segmentation. However, achieving an acceptable thermal performance at high speeds may require a large number of segments, particularly in the outer rotor, which could influence the manufacturing cost and complexity. The proposed models offer a fast and accurate tool for the design and thermal analysis of CMGs, enabling early-stage optimization with minimal computational effort. Full article

Some laser scanners utilize stepper motor-driven optomechanical assemblies to position the laser beam precisely during triangulation. In laser scanners such as the presented Technical Vision System (TVS), to enhance motion resolution, gear transmissions are implemented between the motor and the optical assembly. However, due to the customized nature of the mechanical design, errors in manufacturing or insufficient mechanical characterization can introduce deviations in the computed 3D coordinates. In this work, we present a novel method for estimating the degrees-per-step ratio at the output of the laser positioner’s transmission mechanism using a stereovision system. Experimental results demonstrate the effectiveness of the proposed method, which reduces the need for manual metrological instruments and simplifies the calibration procedure through vision-assisted measurements. The method yielded estimated angular resolutions of approximately 0.06° and 0.07° per motor step in the horizontal and vertical axes, respectively, key parameters that define the minimal resolvable displacement of the projected beam in dynamic triangulation. Full article

Electric vehicle (EV) drivetrains operate at high rotational speeds, which makes the noise, vibration, and harshness (NVH) performance of gear transmissions a critical design factor. Without the masking effect of an internal combustion engine, gear whine can become a prominent source of passenger discomfort. This paper provides the first comprehensive review focused specifically on gear tooth surface waviness, a subtle manufacturing-induced deviation that can excite tonal noise. Periodic, micron-scale undulations caused by finishing processes such as grinding may generate non-meshing frequency “ghost orders,” leading to tonal complaints even in high-quality gears. The article compares finishing technologies including honing and superfinishing, showing their influence on waviness and acoustic behavior. It also summarizes modern waviness detection techniques, from single-flank rolling tests to optical scanning systems, and highlights data-driven predictive approaches using machine learning. Industrial case studies illustrate the practical challenges of managing waviness, while recent proposals such as controlled surface texturing are also discussed. The review identifies gaps in current research: (i) the lack of standardized waviness metrics for consistent comparison across studies; (ii) the limited validation of digital twin approaches against measured data; and (iii) the insufficient integration of machine learning with physics-based models. Addressing these gaps will be essential for linking surface finish specifications with NVH performance, reducing development costs, and improving passenger comfort in EV transmissions. Full article

To extend the time between the overhauls of helicopters, a novel collaborative methodology that takes into account uncertain misalignment errors by considering the shape and performance of the gear is built. Firstly, the digital characteristics of contact patterns, such as the reference point and direction angle, are extracted. Secondly, an optimization model calculates the equivalent misalignment by minimizing deviations in the reference point and direction angle between two contact patterns. This equivalent misalignment accounts for uncertainty misalignment errors introduced by complex gear support deformation. Thirdly, the ease-off is utilized to derive the pinion target surface that can sustain meshing performance under an equivalent misalignment, similar to the original gear in real conditions. This way it integrates with the optimization theory for flank reconstruction to redesign the pinion surface. Simulations reveal that the critical digital characteristics of the contact path on the original gear under the equivalent misalignment mirror those of the original gear in real conditions. Moreover, the surface parameters of the redesigned pinion result in an identical surface under a different equivalent misalignment, maintaining similar contact and dynamic performance. This proposed collaborative design approach, considering the shape and performance while accounting for uncertain misalignment errors through ease-off, greatly improves the gear transmission behavior. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article

Cylindrical gears are used extensively due to their significant advantages including high efficiency, high load-bearing capacity, and long lifespan. However, the machining accuracy of cylindrical gears is significantly affected by motion errors and force-induced errors of machine tools. In this study, a motion error model of the machine tools was established based on multi-body system theory and homogeneous coordinate transformation method, quantifying the contributions and variation patterns of 12 key errors in the A and B-axes to workpiece geometric errors. Then, by using the stiffness analytical model and the spatial meshing theory, the influence of the force-induced elastic deformation of the shaft of rolling wheel and the springback of the workpiece tooth flank on the geometric error was revealed. Finally, taking the through rolling of a spur cylindrical gear with a module of 1.75 mm, a pressure angle of 20°, and 46 teeth as an example, the force-induced elastic deformation model of the shaft was verified by the rolling tests. Results show that for 40CrNiMo steel, the total profile deviation, total helix deviation, and single pitch deviation in the X-direction caused by rolling forces are 32.48 μm, 32.13 μm, and 32.13 μm, respectively, with a maximum contact rebound is δ_c = 28.27 μm. The relative error between theoretical and measured X-direction spindle deformation is 8.26%. This study provides theoretical foundation and experimental support for improving the precision of rolling process. Full article

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is crucial for ensuring high reliability and efficiency within wind farms. Early detection can be achieved though the development of a normal behavior model based on ANNs, which are trained with data from healthy conditions derived from selected SCADA variables that are closely associated with gearbox operations. The objective of this model is to forecast deviations in the gear bearing temperature, which serve as an early warning alert for potential failures. The research employs extensive SCADA data collected from January 2018 to February 2022 from a wind farm with multiple turbines. The study guarantees the robustness of the model through a thorough data cleaning process, normalization, and splitting into training, validation, and testing sets. The findings reveal that the model is able to effectively identify anomalies in gear bearing temperatures several months prior to failure, outperforming simple data processing methods, thereby offering a significant lead time for maintenance actions. This early detection capability is highlighted by a case study involving a gearbox failure in one of the turbines, where the proposed ANN model detected the issue months ahead of the actual failure. The present paper is an extended version of the work presented at the 5th International Conference of IFToMM ITALY 2024. Full article

This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions. Full article

This study aims to develop a new model for windage losses, building upon existing formulation, complemented by dedicated experimental campaigns and a specific methodology designed to isolate and quantify windage losses. The model relies on an analytical approach to flow characterization, incorporating a correction factor accounting for air density reduction. The experimental investigation was carried out on a dedicated test bench and includes both spur and helical gears. The results demonstrate good agreement between the proposed model and the experimental data, with and without the presence of nearby obstacles, such as side flanges, highlighting the model’s robustness across different configurations. The proposed windage loss model reproduces the experimental results with significantly greater accuracy than the original one, yielding relative deviations below 5% compared to almost 20% for spur gears, and below 9% compared to over 21%, and in some cases up to 50%, for helical gears. Full article

The manufacture of industrial parts using silicone molds is becoming more frequent due to their versatility, durability, and precision, particularly in the production of complex components. One specific application is the manufacture of gears, which play a fundamental role in high-performance mechanical systems, where geometric accuracy is essential. Gears produced from resins offer several advantages such as efficient tribological performance, load resistance, noise reduction, and non-magnetic properties. The main goal of this paper is to determine the main factors affecting the final quality of resin gears by analyzing two principal gear quality parameters: teeth profile (ff$\alpha$) and helix deviation (ff$\beta$). This work includes a global analysis of all contributing factors influencing the final quality of gears manufactured. One of the main conclusions obtained is that gear quality depends on a combination of factors, such as mold properties, choice of resin, the manufacturing process, and the quality of the original model. As a result, two regression equations have been developed, relating all influencing factors to the two gear quality parameters (ff$\alpha$ and ff$\beta$). Different response surfaces have been obtained, enabling the definition of the required quality level of the model to achieve reproductions with certain ff$\alpha$ and ff$\beta$ values suitable for the intended application conditions. Full article

Line laser measurement, as a typical method of laser triangulation, makes the acquisition of 3D tooth-surface data more accurate, efficient, and informative. Thus, a line laser 3D measurement model of gears is established, and a specialized polyhedral artifact with specific geometric features is invented to determine the pose parameters of the line laser sensor in measuring space. Based on this, a single-spindle gear-measuring instrument is developed and a series of experimental studies are conducted for gears with different module and flank directions in this instrument, including profile deviation, helix deviation, pitch deviation, topological deviation, etc. A comparative experiment with traditional contact measurement methods validates the correctness of the methods mentioned in this paper for the accurate evaluation of tested gears. In further research, the mining and utilization of big data obtained from the line laser 3D measurement of gears will be an important topic. Full article

The aircraft nose-wheel steering system serves as a critical component for ensuring ground taxiing safety and maneuvering efficiency. However, its dynamic control stability faces significant challenges under complex operational conditions. Existing research predominantly focuses on single-discipline modeling, with insufficient in-depth analysis of the coupling effects between hydraulic system dynamics and mechanical dynamics. Traditional PID controllers exhibit limitations in scenarios involving nonlinear time-varying conditions caused by normal load fluctuations of the landing gear buffer strut during high-speed landing phases, including increased control overshoot and inadequate adaptability to abrupt load variations. These issues severely compromise the stability of high-speed deviation correction and overall aircraft safety. To address these challenges, this study constructs a digital twin model based on real aircraft data and innovatively implements multidisciplinary co-simulation via Simcenter 3D, AMESim 2021.1, and MATLAB R2020a. A fuzzy adaptive PID controller is specifically designed to achieve adaptive adjustment of control parameters. Comparative analysis through co-simulation demonstrates that the proposed mechanical–electrical–hydraulic collaborative control strategy significantly reduces response delay, effectively minimizes control overshoot, and decreases hydraulic pressure-fluctuation amplitude by over 85.2%. This work provides a novel methodology for optimizing steering stability under nonlinear interference scenarios, offering substantial engineering applicability and promotion value. Full article

Reducing gear noise in electric vehicle (EV) drivetrains is crucial due to the absence of internal combustion engine noise, making even minor acoustic disturbances noticeable. Manufacturing parameters significantly influence gear-generated noise, yet traditional analytical methods often fail to predict these complex relationships accurately. This research addresses this gap by introducing a data-driven approach using machine learning (ML) to predict gear noise levels from manufacturing and sensor-derived data. The presented methodology encompasses systematic data collection from various production stages—including soft and hard machining, heat treatment, honing, rolling tests, and end-of-line (EOL) acoustic measurements. Predictive models employing Random Forest, Gradient Boosting (XGBoost), and Neural Network algorithms were developed and compared to traditional statistical approaches. The analysis identified critical manufacturing parameters, such as surface waviness, profile errors, and tooth geometry deviations, significantly influencing noise generation. Advanced ML models, specifically Random Forest, XGBoost, and deep neural networks, demonstrated superior prediction accuracy, providing early-stage identification of gear units likely to exceed acceptable noise thresholds. Integrating these data-driven models into manufacturing processes enables early detection of potential noise issues, reduces quality assurance costs, and supports sustainable manufacturing by minimizing prototype production and resource consumption. This research enhances the understanding of gear noise formation and offers practical solutions for real-time quality assurance. Full article

This study introduces a novel approach for minimizing assembly errors in wind turbine gearboxes using a hybrid optimization algorithm, Particle Swarm-Bacteria Foraging Optimization (PSBFO). By integrating error-driven task sequencing and real-time error feedback with the PSBFO algorithm, we developed a comprehensive framework tailored to the unique challenges of gearbox assembly. The PSBFO algorithm combines the global search capabilities of PSO with the local refinement of BFO, creating a unified framework that efficiently explores task sequencing, minimizing misalignment and torque misapplication assembly errors. The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. The 50 to 5 error reduction represents a significant decrease in assembly errors from an unoptimized (50) to an optimized (5) sequence, achieved through the PSBFO algorithm, by minimizing dimensional deviations, torque mismatches, and alignment errors across 26 critical gearbox components. While the primary focus is on wind turbine gearbox applications, this approach has the potential for broader applicability in error-prone assembly processes in industries such as automotive and aerospace, warranting further validation in future studies. Full article

Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min⁻¹. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article