Journal of Mechanical Engineering, Automation and Control Systems
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Analysis of the influence of flexible ring fillet length on the contact characteristics and motion stability of power roll rings
Full text linkBased on the COMSOL Multiphysics simulation platform, this paper establishes a complete finite element model of a power roll ring assembly. The model consists of inner and outer conductive rings made of H62 brass and a flexible ring made of C17200 beryllium bronze. After meshing, a rotational speed was applied to the inner conductive ring according to actual working conditions, and consistent boundary conditions and friction coefficients were set at the contact pairs for transient dynamic analysis. The study focuses on investigating the influence of the flexible ring’s fillet length (0.15 mm, 0.20 mm, 0.25 mm, 0.30 mm, 0.35 mm) on the system’s contact characteristics and motion stability. By analyzing stress nephograms, motion trajectory diagrams, and quantitatively calculating contact pressure, the results show that the maximum contact stress of the flexible ring is concentrated at the contact points with the inner and outer conductive rings and increases with the fillet length. However, the contact pressure decreases as the fillet length increases, with small fluctuation amplitude, ensuring the operational stability of the power roll ring. This research provides a theoretical basis and an effective method for the structural optimization and performance evaluation of power roll rings
Structural optimization of bus chassis frame based on proxy model
No full textUnder the premise of ensuring modal and strength characteristics, achieving lightweight design of the structure simultaneously has become a key issue of concern for major automobile manufacturers and research institutions. To reduce the mass redundancy of the bus chassis frame, save production costs and energy consumption, a multi-objective optimization scheme based on surrogate model technology was proposed, which could maximize weight reduction without reducing the natural frequency or increasing the peak stress. According to the working principle, load characteristics and composition of the chassis frame, a parametric coupling model for modal and strength was constructed, and the stress, deformation, natural frequency and vibration mode characteristics of the overall structure were obtained. The dimensions of H-steel were determined as design variables, and the discrete mapping data sets of maximum stress, first-order natural frequency and mass were obtained through the Latin square design scheme. Parameters such as the coefficient of determination, adjusted coefficient of determination and root mean square error were selected as the standard evaluation indicators for the accuracy of the response surface model. The reliability of different surrogate models was compared and analyzed, and finally the Kriging model was adopted as the approximation function in the construction of the mathematical model. An optimized mathematical model was constructed to convert the modal and strength objectives into boundary conditions. The design variables meeting the optimization objectives were derived through the sequential quadratic programming algorithm. The results showed that, without reducing the requirements for strength and stiffness indicators, this optimization scheme could reduce the weight of the chassis frame by 9.94 %, which has good economic benefits and engineering value
Experimental study on dynamic load compensation of risers under ultra-low frequency vibration
Full text linkIn the event that a floating drilling platform is struck suddenly by a typhoon, preventing the complete retrieval of the riser, a compensation system is required to alleviate the considerable dynamic loads on the riser resulting from platform movement, thus keeping the riser tension within safe limits. Evaluation of the mathematical model for the conventional vibration isolation system indicated unsatisfactory performance under conditions of large displacement and ultra-low-frequency vibration. To address this, a new dynamic load compensation system for the riser has been developed, along with a dedicated experimental platform. In this setup, platform heave is simulated via the extension and retraction of a hydraulic cylinder, while the riser load is represented using multiple mass blocks. The experimental platform supports both manual and automatic control modes. Utilizing Visual Basic (VB) programming integrated with an Access database, the monitoring and control software provides capabilities for parameter configuration, data monitoring, and data archiving. Experiments performed on this platform, including heavy simulation and dynamic load compensation, demonstrated a compensation effect of 27.4 %. The successful mitigation of dynamic loads on the riser presents a novel approach for drilling platforms to cope with typhoon emergencies and suggests valuable applications for vibration isolation technology in other domains
Small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN)
Full text linkIn actual industrial environments, equipment failures often occur sporadically during operation, resulting in insufficient labeled data for training. To address the issues of difficult feature extraction and poor generalization caused by insufficient data in small-sample fault diagnosis, a small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN) is proposed. Firstly, dual convolution kernels are employed to extract signal features, with the large kernel capturing low-frequency components and the small kernel extracting additional features to enhance the network's expressiveness. Secondly, the channel attention mechanism adaptively adjusts the feature responses of each channel, enabling the network to focus on the most informative and relevant features while suppressing unimportant ones. Finally, the Temporal Convolutional Network (TCN) is utilized to capture dependency features within long time series, further improving the model's ability to process sequential data. Experimental results demonstrate that the DCK-CAM-TCN model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other comparison models in small-sample scenarios. The results indicate the significant advantages of the DCK-CAM-TCN model in small-sample fault diagnosis
Enhancing loess deformation resistance using waste tire rubber particles
Full text linkLoess, characterized by its large pore structure and vertical joints, is prone to collapsible deformation upon moisture infiltration and significant settlement under load, threatening the stability of buildings and infrastructure. This study systematically investigates the effects of rubber particle size (10, 20, 40, and 100 mesh), content (0 %, 5 %, 10 %, 15 %, and 20 % by volume), moisture content, and freeze-thaw cycles on the deformation properties of loess. This systematic investigation distinguishes itself by using waste tire rubber particles as the sole amendment to elucidate both the individual and coupled effects of these factors. Results demonstrate that incorporating rubber particles significantly reduces the compression coefficient of loess, with optimal compressibility achieved at a 5 % rubber particle content and 40 mesh particle size. The collapsibility coefficient is minimized at a 20 mesh particle size with the same 5 % content. Moisture content significantly influences deformation behavior, with both high and low levels increasing the compression and collapsibility coefficients. The study also reveals that rubber particle-loess mixtures exhibit superior freeze-thaw resistance, with smaller increases in deformation coefficients after multiple freeze-thaw cycles compared to remolded loess. The particle size and content of rubber particles are identified as the most important factors influencing the compressibility and collapsibility of loess. This research provides specific guidelines for optimizing rubber particle size and content, controlling moisture levels, and evaluating freeze-thaw impacts to enhance the engineering performance of loess. The findings offer a scientific basis for sustainable waste tire management and advance the application of rubber particles in geotechnical engineering
Experimental results of reducing harmful vibrodynamic effects caused by the interaction between rolling stock and track through the use of elastic under-sleeper pads in the rail joint zone
Full text linkIn the current era of independent development and market relations, the importance of railways continues to grow steadily. This, in turn, places great responsibility on the system of measures aimed at ensuring railway reliability. However, despite the advantages and advancements of the railway industry, it still faces technical complexities that can lead to track deterioration. In heavily loaded and high-speed railway sections, the interaction between the rolling stock and the track causes various issues in the rail joint zones – such as the development of defects and irregularities, deterioration of track geometry, reduction of track stability, as well as problems related to noise and vibration that must be mitigated. To address these challenges, scientific studies and experimental investigations have been conducted on the installation of elastic under-sleeper pads in the rail joint zones. These studies aim to modify the vertical stiffness transferred from the wheelsets of the rolling stock to the track structure, reduce harmful vibrations and oscillations, and thereby ensure uniform stability along the entire track. The conducted research, testing, and their results are presented in this article
Experimental and finite element analysis of the structural durability of special self-propelled rolling stock frames
Full text linkThe study presents an experimental-numerical assessment of the structural durability and residual life of the ADM-1 self-propelled railcar frame operating under cyclic and static loading conditions. A combined methodology integrating full-scale cyclic bench testing and finite element modeling (FEM) was developed to determine the frame’s stress–strain state and fatigue resistance. The experimental tests, performed at the accredited laboratory of “Quyuv Mexanika Zavodi” JSC using the ISRB-1000 hydraulic loading stand, simulated real operational loads up to 2×106 cycles, equivalent to approximately ten years of service. A detailed FEM model was created in SOLIDWORKS Simulation to replicate these loading conditions, analyze stress distribution, and validate experimental data. The numerical and experimental results showed strong correlation (r > 0.9) with a deviation below 8 %, confirming the accuracy of the proposed approach. The maximum equivalent (von Mises) stresses remained below 0.6σ0.2 for St3sp steel, indicating that the structure operated entirely within the elastic range and met the strength requirements of GOST 31846-2012. Fatigue life estimation using Miner’s cumulative damage rule yielded a damage factor of D= 0.72, corresponding to 8-12 years of effective service life, with a residual fatigue resource of approximately 35-40 %. The developed hybrid methodology provides a reliable framework for condition-based maintenance and life-extension of special self-propelled rolling stock
Digital solutions for the transition to a sustainable public transport system in Tashkent
Full text linkThe purpose of this study is to analyze the prospects for transitioning the city from automobile-dominated mobility to a public transport-oriented system. The methodological framework is based on the analysis of transport infrastructure. The research is conducted on the example of Tashkent – the capital of Uzbekistan – characterized by a high level of motorization and significant commuter migration. The study concludes that a successful transition to public transport requires a phased implementation, involving infrastructure modernization, digitalization, regulation of motorization, and transformation of citizens’ mobility behavior. The novelty of this study lies in developing a digital transition model for Tashkent that integrates international best practices (Berlin, London, Singapore) with the local transport and socio-economic conditions
Assessment of the influence of external dynamic factors on force loading of anchor bandage of traction electric motors
Full text linkA methodology has been proposed for calculating the force loading of anchor bandages of locomotive traction electric motors from the action of external dynamic factors, which makes it possible to determine dynamic stresses in each section of the anchor bandage along its entire length, depending on the operating modes of the traction electric motor, taking into account its design features and real operating conditions. It has been established that the most significant influence on the fluctuations of the armature shaft of traction electric motors of diesel locomotives is exerted by dynamic influences from the collision of wheels with joints and unevenness of the rail track, as well as from errors in the manufacture of the serrated broadcast (gears). The supposed economic effect from the creation of new glass bandage designs for the anchors of traction electric motors of diesel locomotives of the 2TE10M series is estimated at approximately 10.92 million soums for one such diesel locomotive. It is recommended to continue these studies in order to develop and justify rational geometric parameters of a new design of the anchor glass bandages of a traction electric motor with increased fatigue strength
Neural network-based ANC algorithms: a review
Full text linkActive Noise Control (ANC) technology is of great value in the field of noise mitigation. Recently, traditional linear adaptive control methods, represented by the FxLMS algorithm, are structurally simple and computationally efficient but often suffer from performance degradation or even failure in practical applications due to nonlinear system factors. For this reason, neural network-based ANC methods have attracted significant research interest for their strong nonlinear processing capabilities and have gradually emerged as a focal point for addressing nonlinear ANC problems. This paper systematically reviews the research progress of neural networks in the field of nonlinear ANC, focusing on two key dimensions: network architecture and training methods. In terms of architecture design, existing studies primarily enhance performance through topology optimization, improvements to functional link artificial neural networks, and innovative hidden layer designs. Advancements in training methods focus on the optimization of loss functions, innovation in weight update algorithms, and the introduction of other training strategies. In the future, neural network-based ANC algorithms will continue to deepen, with potential development paths including the integration of advanced network architectures such as Generative Adversarial Networks (GANs), optimization of utility functions, pruning of hidden layers, improvement in loss function design, and the adoption of more efficient training strategies. These efforts will further improve algorithm performance and ultimately provide robust support for achieving more precise and efficient active noise control