Robotic Systems and Applications
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Numerical simulation of chloride ion transport in concrete based on a random aggregate model
Full text linkA three-dimensional stochastic aggregate model of concrete was established using the Monte Carlo method, and a numerical simulation of chloride ion diffusion at the microscopic level was conducted. The study investigated the migration behaviour of chloride ions in concrete regarding mixing proportions and temperature. The results showed that compared to the simulation results at an ambient temperature of 20 ℃, the chloride ion diffusion coefficient increased by 31 % and 70.5 % for concrete at 25 ℃ and 30 ℃ at 28 days, respectively. The chloride ion penetration depth increased by 17.3 % and 34.9 % for concrete at 25 ℃ and 30 ℃, respectively. With a slag content of 10.4 %, 20.8 %, and 27.1 %, the chloride ion diffusion coefficient at 28 days decreased by 1.4 %, 2.7 %, and 4.1 %, respectively. With a fly ash content of 8.3 %, 16.7 %, and 25 %, the chloride ion diffusion coefficient at 28 days decreased by 2.1 %, 5.4 %, and 9.2 %, respectively. Both slag and fly ash can reduce the chloride ion diffusion coefficient in concrete, with fly ash showing better effectiveness than slag. A water-to-binder ratio of 0.4, combined with 27.1 % slag and 25 % fly ash as cement replacements, can effectively improve the resistance of concrete to chloride ion attack. The micro-scale finite element model of concrete, developed through Monte Carlo simulation, offers enhanced visualization of chloride ion penetration processes under varying mix proportions and temperature conditions
Enhancing sound absorption of Helmholtz resonance metamaterials with extended microperforated neck
Full text linkTo enhance sound absorption of Helmholtz resonance metamaterials in low frequency region with simple structure and engineering practicability, according to the well-established acoustic absorption theory of micro-perforated panel, a novel designed Helmholtz resonance metamaterial with extended microperforated neck is proposed, and a theoretical modelling method is developed by using the transfer matrix method which is validated by finite element simulation. Both theoretical calculation and finite element simulation results show that sound absorption performance of proposed Helmholtz resonance metamaterial is improved significantly compared to that of Helmholtz resonator with normal neck, and the resonant absorption coefficient is close to 1. The influence of geometric parameters of microperforated neck is also investigated in detail, and some meaningful conclusions are drawn. This work provides a perfect solution for low-frequency noise control with Helmholtz resonance metamaterials
Multi-mode frequency response prediction of milling robot based on feature transferring with small sample sets
Full text linkIndustrial robots are increasingly used in machining due to their cost-effectiveness and larger work envelopes. However, their relatively low structural stiffness makes them vulnerable to machining chatter, which negatively impacts both process stability and surface quality. Accurate prediction of the multi-mode frequency response function (FRF) of robotic milling systems is crucial to ensure process stability. Traditional FRF prediction approaches, however, often require extensive experimental procedures, are complex, and are time-consuming. To address these challenges, this study proposes an innovative feature-transfer-based method for multi-mode FRF prediction in milling robots, requiring only a minimal set of impact tests. The method organizes measured FRFs into second-order complex tensors, facilitating the transfer of features between different postures. Multi-mode parameters of the tool-tip FRF under the source posture are extracted using the least-squares complex exponential (LSCE) method and assembled into a label vector. A complex-kernel extreme learning machine with augmented inputs (CKELM-AI) is then trained to predict the tool-tip FRF under the target posture. Additionally, a virtual sample generation strategy based on CKELM-AI and feature augmentation, including statistical, frequency, and time-frequency features, is applied to enhance prediction accuracy. Experimental validation on a milling robot demonstrates that the proposed method significantly improves both prediction efficiency and accuracy, establishing a new, more efficient approach for predicting multi-mode FRFs without the need for extensive testing
Study on the variation mechanism of non-linear stiffness of rubber O-ring
Full text linkO-ring dampers can be used as vibration-damping elements for short-life, low-cost engines, and the selection of a suitable rubber superelastic-viscoelastic ontological model to study their stiffness and damping is an important prerequisite for determining their vibration-damping characteristics. The superelastic-viscoelastic constitutive model consists of two models, superelastic and viscoelastic, in which the superelastic model reflects the static characteristics of the O-ring. Therefore, it is the basis of the study of dynamic characteristics to carry out the research on the static stiffness of the O-ring and to select an accurate superelastic model to describe its deformation and recovery characteristics under different working conditions. Based on the fact that the O-ring is in a small deformation range in the damper and the applicability of finite element simulation, the Mooney-Rivilin superelastic constitutive model is selected in this paper. Establish a three-dimensional finite element model of the O-ring damper, focusing on the analysis of the effect of temperature on the O-ring material properties and damper structure, to reveal the mechanism of non-linear stiffness change of the O-ring damper. At the same time, the accuracy of the hyperelastic model is verified by the test method, which lays a foundation for the study of the dynamic stiffness and damping characteristics of the O-ring. The results show that in the pre-compression state, there is a large contact pressure between the O-ring and the inner and outer rings of the damper. The contact pressure increases linearly during the compression process, and the stiffness of the O-ring changes linearly. In the non-pre-compression state, the contact pressure is 0, the contact pressure increases nonlinearly during the compression process, and the stiffness of the O-ring shows obvious nonlinear characteristics. In addition, the static stiffness of the O-ring increases with the increase of pre-compression amount, increases with the increase of material hardness, and decreases with the increase of temperature. The above research provides a reference for selecting the appropriate O-ring material size and installation conditions in the project to ensure that the O-ring can effectively withstand pressure during use
Structure design and sensitivity analysis of flexible ultrasonic transducer array
Full text linkTo investigate the influence of element parameters on the performance and acoustic field of flexible ultrasonic transducer arrays, this study employs finite element multiphysics simulation software to analyze various parameters of flexible ultrasonic transducers within a multiphysics coupled field. The analysis begins with simulating the width and thickness of piezoelectric materials in a single-element ultrasonic transducer structure. Simulation results indicate that the electromechanical coupling coefficient of the ultrasonic transducer exhibits a quasi-sinusoidal relationship with width. When the piezoelectric material width is 1.8 mm, the electromechanical coupling coefficient reaches its maximum at a thickness of 0.4 mm. Subsequently, simulations were conducted on various parameters of the flexible ultrasonic transducer array. Key investigations included the effects of piezoelectric unit count, inter-unit spacing, and frequency on the ultrasonic focusing performance of linear phased array transducers. Findings indicate that the focusing capability of flexible ultrasonic transducer arrays improves with reduced spacing and increased unit count. However, due to varying practical application requirements and manufacturing precision constraints, array parameters should be selected by comprehensively considering real-world factors. Overall, this study employs multiphysics coupling simulation to visually demonstrate how array element parameters influence the performance of flexible ultrasonic transducers. It provides valuable reference for advancing flexible ultrasonic technology from laboratory research toward commercial application
Analysis and synthesis of a controllable crank-slider mechanism with parallel springs for frame saws
Full text linkFrame saws suffer from large unbalanced inertia forces, limiting operating speed and requiring heavy construction. This study aims to overcome these limitations by synthesizing a dynamically balanced main drive mechanism using a novel approach based on prescribed motion laws. The methodology involves proposing a crank-slider mechanism featuring a cam-actuated variable-length crank. The mechanism configuration with parallel spring is analyzed allowing for balancing inertia forces, achieved using a prescribed cosine slider motion law. For the considered configuration, the required variable crank length function (cam profile) and associated mechanism parameters (connecting rod length, spring stiffness) are analytically synthesized. The results of the carried-out numerical modeling demonstrate successful synthesis of a near-circular cam profile and very low pressure angles for the case studied. These findings show that synthesizing the saw drive kinematics based on force balancing requirements can theoretically eliminate inertial loads, offering the potential for higher speeds of saw frames and reduced loads. The synthesized near-circular cam profile suggests a pathway towards simpler manufacturing. The implications of successfully implementing such dynamically balanced frame saw mechanisms are potentially transformative for the sawmilling industry. Eliminating the primary inertial forces removes the major obstacle to increasing operating speeds. This could allow frame saws to operate closer to the optimal cutting speeds for wood (e.g., 40-50 m/s), leading to significant gains in productivity
Scientific and practical substantiation of transient processes in asynchronous electric motors of mainline electric locomotives
Full text linkThe research work focuses on scientifically substantiating the operating conditions of small and medium-power auxiliary asynchronous electric motors used in mainline electric locomotives under JSC “Uzbekistan Railways”. The aim is to provide a scientific basis for the operational efficiency of auxiliary asynchronous electric motors and, based on the research findings, to conduct a practical investigation of their service life. This, in turn, will enable timely maintenance of auxiliary asynchronous electric motors in locomotives. Additionally, it will contribute to improving the performance indicators of auxiliary asynchronous electric motors
Vibration and noise performance analysis and optimal design of V-rotor in permanent magnet synchronous motor: a new strategy for high efficiency and low noise
Full text linkInterior Permanent magnet synchronous motors (IPMSMs) have become the preferred powertrain solution for electric vehicles due to their exceptional performance characteristics. However, the high-frequency electromagnetic noise generated during motor operation poses a significant challenge to occupant comfort within the vehicle. This study provides a comprehensive analysis of the electromagnetic forces, modal characteristics, and vibration noise for a 12-pole, 36-slot IPMSM, incorporating theoretical and simulation-based approaches as well as modal tests. By innovatively combining orthogonal experimental design with nonparametric regression techniques, a response surface model is developed to accurately characterize and optimize the radial electromagnetic force harmonics of the motor. The optimization results reveal a significant 37.7 % reduction in the motor’s surface vibration velocity and an 8.5 % decrease in peak noise levels, successfully meeting the engineering objectives for vibration and noise attenuation. This study not only contributes to the advancement of noise control technologies in electric vehicle power systems but also provides novel insights and methodologies for motor design, offering significant practical value and engineering relevance
Research on bearing equipment fault diagnoses via SAWOA-LSTM
Full text linkTo address the current low fault diagnosis accuracy problem for bearing equipment, and improve the detection methods, in this paper a sine-adapted whale optimization algorithm (SAWOA)-based optimization of a long short-term memory (LSTM) network is proposed as the equipment fault diagnosis method (SAWOA-LSTM). First, an optimization strategy based on sinusoidal population initialization and adaptive optimization is proposed for the whale optimization algorithm, which has the two drawbacks of slow convergence and easily falling into a local optimum. Second, to improve the accuracy and efficiency of fault diagnoses, the SAWOA is used to optimize the number of hidden units and the learning rate parameter of the LSTM. Compared with ACO-, PSO-, and WOA-based LSTM models, the proposed method improves diagnostic accuracy by 14.17 %, 15.03 %, and 4.32 %, respectively. In tests on 50 bearing samples, SAWOA-LSTM further improves accuracy for RBD, IRA, and ORD by 1.08 %, 1.62 %, and 1.10 %, respectively. Our algorithm provides an innovative solution for the health management of complex industrial bearing equipment
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