Search Results (4,289)

Accurate performance prediction in the design phase of permanent magnet synchronous motors (PMSMs) is essential for optimizing efficiency and functionality. While 2-D finite element analysis (FEA) is commonly used due to its low computational cost, it overlooks important 3-D flux components such as axial leakage flux (ALF) and fringing flux (FF) that affect motor performance. Although 3-D FEA can account for these flux components, it is computationally expensive and impractical for rapid design iterations. To address this challenge, we propose a performance prediction method for interior permanent magnet synchronous motors (IPMSMs) that incorporates 3-D flux effects while reducing computational time. This method uses deep transfer learning (DTL) to transfer knowledge from a large 2-D FEA dataset to a smaller, computationally costly 3-D FEA dataset. The model is trained in 2-D FEA data and fine-tuned with 3-D FEA data to predict motor performance accurately, considering design variables such as stator diameter, axial length, and rotor design. The method is validated through 3-D FEA simulations and experimental testing, showing that it reduces computational time and accurately predicts motor characteristics compared to traditional 3-D FEA approaches. Full article

Currently, in the domain of surface defect detection on hot-rolled strip steel, detecting small-target defects under complex background conditions and effectively balancing computational efficiency with detection accuracy presents a significant challenge. This study proposes CTL-YOLO based on YOLO11, aimed at efficiently and accurately detecting blemishes on the surface of hot-rolled strip steel in industrial applications. Firstly, the CGRCCFPN feature integration network is proposed to achieve multi-scale global feature fusion while preserving detailed information. Secondly, the TVADH Detection Head is proposed to identify defects under complex textured backgrounds. Finally, the LAMP algorithm is used to further compress the network. The proposed algorithm demonstrates excellent performance on the public dataset NEU-DET, achieving a mAP50 of 77.6%, representing a 3.2 percentage point enhancement compared to the baseline algorithm. The GFLOPs is reduced to 2.0, a 68.3% decrease compared to the baseline, and the Params are reduced to 0.40, showing an 84.5% reduction. Additionally, it exhibits strong generalization capabilities on the public dataset GC10-DET. The algorithm can effectively improve detection accuracy while maintaining a lightweight design. Full article

This paper introduces a novel diagnostic approach called partial-discharge-activated impulse frequency response analysis (PD-IFRA), developed to overcome the limitations of conventional frequency response analysis (FRA) in detecting partial discharges (PDs) in power transformers. While traditional FRA with low-impulse-voltage excitation (LIVE) effectively identifies mechanical deformations, inter-turn shorts, and insulation faults, it fails to detect incipient PD activity since PD phenomena require excitation beyond the PD inception voltage (PDIV) to initiate. This study proposes, for the first time, the extension of IFRA to moderate impulse voltage levels—without exceeding insulation limits—enabling the early and non-destructive detection of PDs. Experimental validation on a 315 kVA, 11 kV/420 V Dyn11 transformer shows that PD-IFRA effectively identifies PD-related impedance deviations within the 10 kHz–2 MHz frequency range, especially for PD sources associated with floating metal parts. Furthermore, the comparative analysis between normal, short-circuited, and PD-induced conditions demonstrates that PD-IFRA significantly enhances the precursory diagnosis of PDs where conventional FRA fails. This contribution advances transformer condition assessment by integrating PD sensitivity into FRA-based methods without compromising equipment safety. Full article

Spalling alters a gear’s time-varying meshing stiffness (TVMS), thereby affecting its vibration characteristics. However, most studies focus on single-spalling gears and overlook the possibility of multi-spalling gears. Additionally, because most spalls are irregular, traditional analytical models neglect the torsional effects that are caused by asymmetric spalling. In this study, a shape-independent model for calculating the TVMS of multi-spalling gears, which considers torsional stiffness, was developed. A 16-degree-of-freedom dynamic model was established to analyze the dynamic response, incorporating the multi-spalling TVMS. The model was then validated through experiments. The results show that the proposed method accurately calculates the TVMS of a multi-spalling spur-gear system. Changes in the relative position of the spalling can significantly affect the TVMS. Multiple-tooth spalling influences the TVMS over several meshing cycles, while single-tooth multiple spalling affects the TVMS based on the specific spalling parameters. Different spalling patterns lead to substantial differences in the system’s dynamic behavior. Multiple spalling teeth generate several pulses, whereas a single tooth with multiple spalls only generates one significant pulse. This study provides a solid foundation for understanding the dynamic behavior of spalled gear systems, revealing their dynamic characteristics and failure mechanisms. Full article

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near distances. The user interface (UI) of the e-portal application creates an environment for customers to input their eye prescription data and geometric parameters to visualise the result of the designed freeform varifocal lens surface, which includes interactive 2D contour plots and 3D-rendered diagrams for both left and right eyes simultaneously. The e-portal provides a unified interactive platform where users can simultaneously access both the specialised Copilot demo web for lenses and the main Shiny/R version 4.4.0 programming app, ensuring seamless integration and an efficient process flow. Additionally, the data points of the 3D-designed surface are automatically saved. In order to check the performance of the designed varifocal lens before production, it is remodelled in the COMSOL Multiphysics 6.2 modelling and analysis environment. Ray tracing is built in the environment for the lens design assessment and is then integrated with the lens additive manufacturing (AM) using a Formlabs 3D printer (Digital Fabrication Center (DFC), London, UK). The results are then analysed to further validate the e-portal-driven personalised design and manufacturing approach. Full article

This study explores the in situ measurement of contact temperature in thermo-elastohydrodynamic lubrication (TEHL) within cylindrical roller thrust bearings (CRTBs) utilizing vapour-deposited resistive thin-film sensors. The sensors, optimized for compactness and high spatial resolution, were strategically embedded on the stationary bearing raceways near the outer, inner, and mean radius. This configuration enabled a precise measurement of temperature variations in both pure rolling and rolling–sliding regions of the CRTBs. The experimental results revealed a consistent decrease in temperature from the inner and outer radius zones towards the mean radius as the slip-to-roll ratio (SRR) decreased in these regions. Temperature profiles showed an early rise in the inlet zone attributed to thermal inlet shear. At higher speeds, a secondary temperature peak indicative of full-film lubrication was observed in the outlet zone immediately following the Hertzian contact. The study further shows the influence of surface pressure, shear rates, sliding friction, and circumferential speed on contact temperature dynamics, offering insights into their complex interplay. Additionally, viscosity variations due to different oil temperatures were found to critically affect the rate of temperature rise and the propensity for mixed friction phenomena. A higher viscosity resulted in an earlier onset of the temperature rise in the contact, while a lower viscosity and higher speeds promote mixed lubrication, leading to reduced contact film temperatures. These findings provide valuable insights into the behaviour of CRTB-lubricated contacts under various operating conditions and serve as crucial validation data for advanced TEHL computational models. Full article

To improve the thermal performance of air-cooled panel-type radiators for transformers, a multi-fan horizontal blowing method was designed in this paper, and the thermo-hydraulic performance of the oil-side and air-side of the panel-type radiator was investigated with a simplified numerical method and experiments. The uniform air distribution and zoned heat dissipation ideas were used for three blowing methods, which can increase the proportion of air supply for the high-temperature area of the radiator and apply multiple fans for zoned heat dissipation of the insulating oil in the radiator. Then, the effect of different insulating oil flow rates on the heat dissipation performance of the panel-type radiator was investigated. It was shown that the computational time for the simplified numerical simulation method used for an air-cooled panel-type radiator could be effectively shortened with a small relative error. Due to a more uniform air supply and prioritized air distribution for the high-temperature areas using the multi-fan horizontal blowing method, the overall heat dissipation efficiency was improved. Among the three blowing methods, the best heat dissipation performance was obtained by using the six-fan horizontal blowing scheme, which can improve the performance by about 10.42% and 15.44% in experimental and numerical studies, respectively, as compared with the traditional blowing method. Full article

Traditional design and shaping methods of helical gears may have difficulties in meeting the requirements of multiple performance indicators simultaneously, such as tooth surface accuracy, load-carrying capacity, and transmission efficiency. This study attempts to overcome these limitations through a multi-objective optimization method and achieve the comprehensive optimization of multiple performance indicators. This paper aims to boost gear system power transmission and cut vibration and noise. It assesses gear shaping impacts via normal load per unit length of the helical gear surface and gear vibration amplitude. Traditional gear shaping schemes were first determined using classic theories and formulas. Then, an improved genetic algorithm was applied to seek optimal helical gear shaping parameters. An eight-degree-of-freedom lumped mass model of the helical gear transmission system, considering bending–torsion–axial coupling, was developed based on Newton’s second law and solved via the fourth-order Runge–Kutta method. Comparisons showed that the traditional shaping scheme reduced the maximum normal load per unit length by 20.6% and the system’s vibration amplitude by 18.3%. In contrast, the improved genetic algorithm achieved greater reductions of 26.34% and 27.2%, respectively. Both methods effectively decreased the maximum normal load per unit length and system vibration amplitude, with the improved genetic algorithm yielding superior results. This work offers a key theoretical basis and reference for enhancing load transmission, reducing costs, and mitigating vibration and noise in gear transmission systems. Full article

In recent years, the increasing construction of high-rise buildings has led to the widespread use of glass curtain walls. Regular cleaning is essential to maintain their aesthetic appeal and functionality. However, manual cleaning methods pose significant safety risks, necessitating the development of façade-cleaning robots. This paper presents a 3-Degree-of-Freedom Modular High-Rise Façade-Cleaning Robot (3-DOF-MHRFCR), consisting of a lifting module, an XYZ motion module, and a cleaning module. The robot employs a synchronous belt lifting mechanism for vertical movement, ensuring high positioning accuracy and safety. The XYZ motion module enables precise cleaning and obstacle traversal, while the cleaning module combines high-pressure water jets, rotating brushes, and squeegees for effective contaminant removal. Experimental results demonstrate a maximum glass transmittance enhancement of 72.4% and a 21.8% reduction in water consumption compared to manual cleaning, validating the robot’s efficiency and stability. Full article

This study investigates the performance of biobased and nano-additive lubricants for the sustainable machining of Al6082 alloy. The experiments were conducted in five different cutting environments: dry cutting, olive oil-based minimum quantity lubrication (MQL), sunflower oil-based MQL, olive oil-based MQL with nano-SiO₂ additives, and sunflower oil-based MQL with nano-SiO₂ additives. The machining performance was evaluated in terms of key parameters such as surface roughness, cutting forces, tool wear, cutting temperature, and chip morphology. The results show that nano-additive lubricants reduce friction, reduce tool wear, and reduce cutting forces, thus providing lower surface roughness. The nano-SiO₂-additive olive oil-based MQL method showed the optimum performance by providing the lowest cutting force and temperature values. It was also determined that nano-additive lubricants contributed to more regular chip formation. The study reveals that the use of biobased nano-lubricants in sustainable machining processes offers environmental and economic advantages. In the future, it is recommended to examine different types and concentrations of nanoparticles, conduct long-term tool wear analyses, and evaluate the effects on other machining methods. Full article

This paper presents the design of a four omni-wheeled mobile robot consisting of four omni wheels, with each wheel connecting to a separate DC motor. Additionally, the presence of a telescopic leg with a linear RC servo actuator enables the robot to adapt to various landscape changes, including obstacle overcoming. We have designed and manufactured the physical prototype of the robot based on the simulation results. The proposed robot can traverse in both vertical and horizontal directions without altering its orientation, thereby enhancing its stability during operation. The experimental results confirm the robot’s effectiveness in autonomously adapting its position in response to sudden changes in the landscape, enabling it to navigate and climb steps successfully. Full article

Extended operation in complex environments characterized by high temperatures, pressures, and hydrogen exposure can lead to performance degradation for S32168 stainless steel welds of hydrogenation reactors, which significantly impacts the reliability of hydrogenation reactors. The impact of the grinding process on the grinding temperature and force of S32168 steel welds is studied in this paper based on the W-M fractal dimension. A multi-grain grinding simulation model was built, and grinding experiments were conducted. The results show that the grinding speed and depth increased as the grinding temperature increased. At a speed of 20 m/s and depth of 15 μm, the temperature peaked at 1073 °C. Increasing the grinding depth increased both the temperature and force, while increasing the speed increased the temperature but reduced the force. When the depth was 15 μm and speed was 20 m/s, the maximum temperature was 939.1 °C. At a 15 μm depth and 10 m/s speed, the normal and tangential grinding forces peaked at 11.68 N and 9.33 N, respectively. When the depth was 5 μm and the speed was 20 m/s, the grinding forces were the lowest with normal and tangential forces of 0.93 N and 1.72 N, respectively. Comparing the simulated and experimental temperature results through nine sets of experiments, the error range was 6.97–14.2% with an average of 9.37%. The simulation model effectively simulated the grinding process. Full article

Traditional tool wear identification methods are usually based on the framework of “feature extraction + machine learning”, but these methods often have problems of low efficiency and low recognition accuracy. To address these problems, this paper proposes a tool wear state identification model based on particle swarm optimization (PSO) and least squares support vector machine (LS-SVM), namely the PSO-LS-SVM model. By integrating data collected by multiple sensors, key feature information reflecting the tool wear state is extracted; dimensionality reduction techniques such as principal component analysis (PCA) are used to optimize feature vectors to improve the distinguishability of features. The model parameters are optimized by the two-dimensional coordinates (c and g) of the particle swarm algorithm to adapt to the given training sample set. During the training process, the fitness of each particle is calculated and compared with its historical optimal fitness to update the optimal fitness of the particle. This process is iterated until the global optimal solution is found, thereby achieving accurate identification of the tool wear state. Experimental results show that the PSO-LS-SVM model shows high accuracy and good performance in tool wear state identification, which verifies the effectiveness of the algorithm in improving tool efficiency and extending tool life. The study is the first to combine PSO and LS-SVM for tool wear prediction in multi-sensor data fusion. This advanced recognition technology can significantly reduce the waste of resources caused by premature tool replacement, while improving the stability of the machining process and the consistency of the product. Full article

Mobile manipulators have the potential to replace manual labor in various scenarios. However, current mobile base designs have limitations when it comes to accommodating complex movements that involve both high-altitude tasks and ground-based composite tasks. This paper presents a new design for the mobile manipulator base, which utilizes a time-sharing drive with gears and differential wheels. Additionally, a new foldable mechanical gear-track system has been developed, enabling the robot to effectively operate on both the ground and the mechanical gear-tracks. To address the challenges of power distribution and localization caused by the mechanical characteristics of the designed track, this study proposes a precise pose estimation method for the robot on the mechanical gear-track, along with a compliance control method for the gears. Furthermore, a segmented multi-sensor fusion navigation approach is introduced to meet the high-precision motion control requirements at the entrance of the designed track. Experimental results demonstrate the effectiveness of the proposed new mobile manipulator base, as well as its corresponding control methods. Full article

In modern industries, bearings are often subjected to challenges from environmental noise and variations in operating conditions during their operation, which affects existing fault diagnosis methods that rely on signals from single types of sensors. These methods often fail to provide comprehensive and stable fault information, thereby affecting the diagnostic performance. To address this issue, this paper introduces a multi-source and multi-domain information fusion method for the fault diagnosis (M2IFD) of bearings, integrating an attention mechanism to enhance the diagnosis process. The proposed method is structured into three main stages: initially, the original signal undergoes transformation into frequency and time–frequency domains using envelope spectral transform (EST) and Bessel transform (BT) to extract richer fault features. In the second stage, features are extracted independently from each transformed domain and combined with a channel attention mechanism for feature fusion, preserving the unique information from each signal source. Finally, multi-domain features are further fused through an attention mechanism to improve fault classification accuracy. Extensive comparison experiments conducted on the Paderborn dataset illustrate that the proposed M2IFD method significantly enhances fault recognition accuracy across various operating conditions, showcasing its adaptability and robustness. This approach presents new avenues for bearing fault diagnosis, with significant implications for both theoretical and practical applications. Full article