Search Results (546)

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

This article provides a comprehensive overview of recent studies concerning laser heat treatment (LHT) of structural and tool steels, with particular attention to the 21NiCrMo2 steel used for carburized gear wheels. Analysis includes the influence of critical laser processing conditions—including power output, motion speed, spot size, and focusing distance—on surface microhardness, hardening depth, and microstructure development. The findings indicate that the energy density is the dominant factor that affects the outcomes of LHT. Optimal results, in the form of a high surface microhardness and a sufficient depth of hardening, were achieved within the energy density range of 80–130 J/mm², allowing for martensitic transformation while avoiding defects such as melting or cracking. At densities below 50 J/mm², incomplete hardening occurred with minimal microhardness improvement. On the contrary, densities exceeding 150–180 J/mm² caused surface overheating and degradation. For carburized 21NiCrMo2 steel, the most effective parameters included 450–1050 W laser power, 1.7–2.5 mm/s scanning speed, and 2.0–2.3 mm beam diameter. The review confirms that process control through energy-based parameters allows for reliable prediction and optimization of LHT for industrial applications, particularly in components exposed to cyclic loads. 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 contribution studies the control of the yaw motion of large wind turbines. Two aspects are considered: the first is maximising the energy conversion by yawing the rotor in accordance with the wind direction. The other aspect is synchronising the control of all yaw actuators, which are affixed to the yaw gear rim. In a first phase, P and PI controllers are used in all control loops. Later on, the yaw controller and the synchronisers are replaced with nonlinear PI (NPI) controllers. Moreover, all actuator position P controllers are changed using nonlinear P (NP) controllers. Simulation experiments are carried out on the NREL 5 MW reference wind turbines. The results are very promising. Full article

The aim of the present study is to investigate the influence of Nickel–Hexagonal Boron Nitride (Ni-hBN) nanocomposite coatings, deposited using the pulse reverse current electrodeposition technique. This experimental study focuses on assessing the tribological and corrosion properties of the produced coatings on the SS304 substrate. The microhardness of the as-deposited (AD) sample and heat-treated (HT) sample were 49% and 83.8% higher compared to the control sample. The HT sample exhibited a grain size which was approximately 9.7% larger than the AD sample owing to the expansion–contraction mechanism of grains during heat treatment and sudden quenching. Surface roughness reduced after coating, where the Ni-hBN-coated sample measured a roughness of 0.43 µm compared to 0.48 µm for the bare surface. The average coefficient of friction for the AD sample was 42.4% lower than the bare surface owing to the self-lubricating properties of nano hBN. In particular, the corrosion rate of the AD sample was found to be 0.062 mm/year, which was lower than values reported in other studies. As such, findings from the present study can be particularly beneficial for applications in the automotive and aerospace industries, where enhanced wear resistance, reduced friction, and superior corrosion protection are critical for components such as engine parts, gears, bearings and shafts. Full article

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment. Full article

The quantification of solid debris in used lubricating oil is essential for assessing transmission system wear and optimizing maintenance strategies. This study introduces a low-cost capacitive proximity sensor for monitoring total solid particle contamination in lubricants, with a focus on ferrous (Fe), non-ferrous (Al), and non-metallic (SiO₂) debris. Controlled tests were performed using five mixing ratios of large-to-small particles (100:0, 75:25, 50:50, 25:75, and 0:100) at a fixed debris mass of 0.5 g per 25 mL of SAE 85W-140 automotive gear oil. Cubic regression analysis yielded high predictive accuracy, with average R² values of 0.994 for Fe, 0.943 for Al, and 0.992 for SiO₂. Further dimensionality reduction using Principal Component Analysis (PCA), along with Linear Discriminant Analysis (LDA) of multivariate statistical analysis, effectively classifies debris types and enhances interpretability. These results demonstrate the potential of capacitive sensing as an offline, non-invasive alternative to traditional techniques for wear debris monitoring in transmission systems. These results confirm the potential of capacitive sensing, supported by statistical modeling, as a non-invasive, cost-effective technique for offline classification and monitoring of wear debris in transmission systems. Full article

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article

With engineering plastics increasingly replacing traditional materials in various drive and control gear systems across numerous industrial sectors, material selection for any gearwheel critically impacts its mechanical and thermal properties. This paper investigates the engagement of steel and Polyvinylidene Fluoride (PVDF) gear pairs tested under several load conditions to determine polymer gears’ characteristic service life and failure modes. Furthermore, recognizing that the application of polymer gears is limited by insufficient data on their temperature-dependent mechanical properties, this study establishes a correlation between the tribological contact, meshing temperatures, and wear coefficients of PVDF gears. The results demonstrate that the flank surface wear of the PVDF gears is directly proportional to the temperature and load level of the tested gears. Several distinct load-induced failure modes have been detected and categorized into three groups: abrasive wear resulting from the hardness disparity between the engaging surfaces, thermal failure caused by heat accumulation at higher load levels, and tooth fracture occurring due to stiffness changes induced by the compromised tooth cross-section after numerous operating cycles at a specific wear rate. Full article

With the rapid development of electric vehicles, the issue of wiper–windshield friction noise has become more prominent. However, limitations in the hardware and software configurations of existing experimental systems restrict in-depth studies of frictional vibration and noise mechanisms. This study develops an experimental system with functions for working condition adjustment, data acquisition, and analysis of wiper–windshield frictional vibration and noise. First, the overall design of the wiper–windshield experimental system is described. The system allows adjustment of the motion gear and friction coefficient and facilitates data collection and analysis of pressure, vibration, and noise. The design includes the mechanical structure, electronic and electrical components, and software system of the experimental setup. A PLC control program (lower computer) and human–computer interaction software (upper computer) based on LabVIEW are developed to drive and control the mechanical structure, enabling working condition adjustment, data acquisition, and analysis. Finally, an experimental scheme is implemented to verify the feasibility of the wiper–windshield experimental system. Mechanical property, vibration, and noise data from the wiper are collected by simulating the operating conditions of a real vehicle. The experimental results demonstrate that the designed wiper–windshield experimental system can adjust various working conditions and support the collection and analysis of diverse data, facilitating theoretical research on the generation mechanism, influence rules, and control methods for wiper–windshield frictional vibration and noise. Full article

An engine-propeller cooperative control based on model predictive control (MPC), which takes a deep neural network (DNN) as the prediction model, is studied, and a two-layer optimization method is proposed to improve the economy and maneuverability of the COGAG system. The engine-propeller matching characteristic of the COGAG system is studied, and the economy of the COGAG system is analyzed. In the system planning layer, when the vessel speed command is given, the economic optimal point can be identified. In the local control layer, the DNN-MPC control for different dynamic processes is designed. Moreover, the DNN model has the ability to run in ultra-real time. Compared with parallel control based on PI and parallel power feedback control based on PID, the optimal control based on DNN-MPC can improve the maneuverability of the COGOG pattern by 31.82% and 16.67% in the process of accelerating from 1st to 8th gear and improve the maneuverability of the COGAG pattern by 50% and 23.08% in the process of accelerating from 1st to 10th gear. Moreover, DNN-MPC control can effectively avoid the overshoot of propeller speed caused by the change in pitch adjustment. It provides the theoretical basis for multi-objective optimization of the COGAG system. Full article

This study presents the design and experimental testing of a two-degrees-of-freedom (2DOF) elbow prosthesis prototype designed to replicate the movement patterns of a native or normal human elbow. Two methods of the control of the prosthesis, namely, the proportional–integral–derivative method (PID; a well-established method) and a combination of sliding mode control with a time base generator strategy (SMC + TBG; an advanced method), were compared on the basis of various performance metrics of the prosthesis, as obtained in laboratory tests. Among these metrics were the angular displacement and velocity as a function of time. The mechanical design combined 3D-printed components with custom-designed joints, featuring a worm gear transmission with a crown gear for flexion–extension, enhanced by torsional springs, and a pinion gear with a crown gear for pronation–supination and control. Sensors for voltage and current data acquisition enabled real-time monitoring and control. The prosthesis was tested in the laboratory with a range of motion of 100–120° for flexion–extension, 50° for supination, and 75° for pronation, demonstrating the adaptability of the actuators and validating their autonomy through battery-powered operation. The results showed that control using SMC + TBG resulted in biomimetic patterns for angular displacement and angular velocity of the prosthesis, whereas control using PID did not. Thus, the prosthesis with control provided using an SMC + TBG strategy may have been promised for use by people who have undergone transhumeral amputation. Full article

The mechanical connection between a transmission system and an electric motor gives rise to a strong interaction between their respective dynamics. In particular, the coupling between an electric motor and a nonlinear spur gear transmission significantly influences the overall dynamic behavior of the integrated system. This study presents a detailed investigation into the electromechanical coupling effects between a permanent magnet synchronous machine (PMSM) and a nonlinear spur gear transmission. To focus on these effects, three configurations are analyzed: (i) a standalone gear pair model without motor interaction, (ii) a combined gear–motor system without dynamic coupling, and (iii) a fully coupled electromechanical system where the mechanical feedback influences motor control. The dynamic interaction between the motor’s torsional vibrations and the gear transmission is captured using the derivative of the transmission error as a feedback signal, enabling a closed-loop electromechanical model. Numerical simulations highlight the critical role of this coupling in shaping system dynamics, offering insights into the stability and performance of electric drive–gear transmission systems under different operating conditions. It also underscores the limitations of traditional modeling approaches that neglect feedback effects from the mechanical subsystem. The findings contribute to a more accurate and comprehensive understanding of coupled motor–gear dynamics, which is essential for the design and control of advanced electromechanical transmission systems in high-performance applications. Full article

As the core component of modern mechanical transmission, the precision rotary motion mechanism and its drive system have wide applications in aerospace, robotics, and other fields. This article systematically reviews the design principles, performance characteristics, and research progress of various rotational motion mechanisms and their driving technologies. The working principles, advantages, disadvantages, and applicable scenarios of gears, drive belts, sprockets, camshafts, ratchet claw mechanisms, and linkage mechanisms were analyzed in terms of traditional mechanisms. In terms of new mechanisms, we focused on exploring the innovative design and application potential of intermittent indexing mechanisms, magnetic gears, 3D-printed spherical gears, and multi-link mechanisms. In addition, the paper compared the performance differences of electric, hydraulic, pneumatic, and piezoelectric drive methods. Research has shown that through material innovation, structural optimization, and intelligent control, there is still significant room for improvement in the load capacity, accuracy, and reliability of precision rotary motion mechanisms, providing theoretical support and practical reference for innovative design and engineering applications of future mechanical transmission technologies. Full article