Journal of Mechatronics and Artificial Intelligence in Engineering
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Influence of angular speed of tedder on kinematic parameters of linter machine drive
Full text linkThis article investigates the influence of the tedder’s angular speed on the kinematic and power characteristics of the drive system of the 5LP linter machine. The linter machine is a complex technological unit used to remove residual fibres from the surface of cotton seeds. One of the key factors determining linting efficiency is the interaction between the tedder and the seed roller inside the machine’s working chamber. A detailed kinematic and force analysis is presented, taking into account the resistance forces generated by the seed roller during its movement and processing. Particular attention is given to the development of a calculation model that describes the interaction between the tedder blades and the seed roller. In this model, each blade is treated as a cantilever beam subjected to variable loads resulting from the non-uniform mass and density distribution of the seed material. The analysis demonstrates that variations in the mass and density of the seed roller significantly affect the load transmitted to the drive and the stability of the saw cylinder. The obtained results enable more accurate selection of drive parameters and optimisation of the operating modes of the linter machine. These findings are crucial for improving the productivity and reliability of the equipment, as well as for accounting for both transient and steady-state operating conditions in real industrial environments
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
Advancing industrial gas turbine field performance testing: a review of procedures and key considerations with emerging technologies
Full text linkThis review explores the possibility of enhancing the efficiency and accuracy of Industrial Gas turbine Performance testing by critically assessing the traditional methods, their limitations, and how modern technologies can be used to complement the existing traditional testing approaches, optimize data acquisition, and predict operational failures. A systematic and comprehensive search strategy was employed to identify relevant academic and industry literature. Studies on traditional testing practices were reviewed to highlight their constraints, while researches involving the application of emerging technologies for performance diagnostics were also reviewed to illustrate their benefits. Findings show that measured data such as turbine inlet temperature, compressor pressure ratio, exhaust temperature, fuel flow, shaft speed, and vibration remain essential for both traditional and AI-enhanced methods. These parameters, typically obtained through standardized testing procedures, provide the foundational input for AI models such as machine learning algorithms and digital twins. The study revealed that AI technologies thrive in data-rich, repeatable environments by enhancing processes like instrumentation, data logging, and normalization. The study also revealed that machine learning, deep learning, artificial neural networks, and digital twins can be used for more effective planning, reduce redundant testing, and mitigate delays caused by variable factors like weather or load conditions
Prediction of wharf subsidence deformation degree based on deep learning technology
Full text linkThis paper presents an algorithm that combines a convolutional neural network (CNN) with a gated recurrent unit (GRU) to predict the wharf subsidence deformation. First, the digital elevation model (DEM) image features of the wharf area were extracted using the CNN, and then the patterns of change in wharf settlement were captured using the GRU. Moreover, the wharf in the Longtan Port area of Nanjing Port, located in Jiangsu Province, was analyzed. When the CNN comprised three convolutional layers and the activation function was set to sigmoid, the prediction performance of the proposed algorithm was the best. In both short-term and long-term scenarios, the CNN+GRU algorithm had better prediction performance than long short-term memory and GRU models
Research on the mechanism of bending and torsional vibration of rotor induced by winding inter-turn short-circuit in large synchronous condenser excitation
Full text linkThe synchronous condenser plays a crucial role in reactive power compensation and voltage support in ultra-high voltage direct current power grids. Vibration is a key bottleneck for the safe and stable operation of the synchronous condenser, especially rotor vibration resulting from electromagnetic torque caused by inter-turn short-circuit. This article takes a 300 MV large synchronous condenser as the research object, and studies the influence of electromagnetic torque caused by inter-turn short-circuit on the vibration of the rotor. Through theoretical analysis and finite element simulation, a theoretical model and a finite element model of rotor bending-torsional coupled vibration were established, and the accuracy of the theoretical model was validated experimentally. The results show that: The first three bending frequencies are 11.93 Hz, 31.27 Hz, and 89.46 Hz; Under both static and dynamic imbalance conditions, the vibration amplitude of the rotor increases with the increase of the shorted turn ratio; The vibration amplitude before and after being stimulated under dynamic unbalance is larger than that under static imbalance. The research results can provide a theoretical basis for the design and safe operation and maintenance of the synchronous condenser
Lightweight design of double-head machine tool beam based on the adaptive multi-objective method
Full text linkDouble-head machine tool has the advantages of high efficiency and high degree of automation. In order to reduce the weight of double-head machine tool and improve the stiffness of the entire machine. An optimization design method combining topology optimization, sensitivity analysis and adaptive multi-objective method is used. Firstly, simplify the model in SolidWorks and import it into ANSYS Workbench software to carry out finite element analysis on the entire double-head machine tool to find out the weak component as the beam. Afterwards, carry out topological optimization on the beam and redesign the beam structure, and complete the first optimization. Then, through sensitivity analysis of the input parameters, key parameters that significantly impact the objective function are identified. Subsequently, a multi-objective optimization function is constructed for these key parameters and the objective function. Finally, an adaptive multi-objective method is used to solve the problem and obtain a Pareto optimal solution set, completing the second optimization. The results show that the weight of the beam is reduced by 8.88 %, the deformation of the beam is reduced by 11.29 %, and the equivalent stress of the entire machine is reduced by 28.33 %. This design not only yields significant economic benefits but also serves as a valuable reference for the lightweight design of large machine tool crossbeams
Simulation analysis of coupling mechanism between transient flow field characteristics of bubble collapse and metal deformation based on surface micromorphology
Full text linkIn the process of modifying titanium alloy oral implants using cavitation water jet, the collapse of bubbles releases significant energy. This phenomenon is accompanied by micro-jets and shock waves, which induce changes in the three-dimensional microscopic morphology of the implant surface. The loose and porous surface of the implant will increase the adhesion area of the cells, which is more conducive to the combination of the oral implant with the surrounding bone tissue. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, based on different flow field and solid field model parameters, the numerical analysis software Ansys and the fluid-structure coupling simulation method are used to establish the numerical simulation model of single bubble collapse on the near curved wall. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, the bubble growth process is ignored. Based on different flow field and solid field model parameters, this paper adopts the numerical analysis software Ansys and the fluid-structure coupling simulation method to establish the numerical simulation model of single bubble collapse on the near curved wall. The effects of flow field parameters and wall morphology on the transient flow field of bubble collapse and the effect of metal surface modification are revealed. The results show that when the initial bubble diameter is 180 μm, the instantaneous collapse high pressure reaches 7.24 GPa, and the maximum stress on the titanium surface is 689 MPa, which is 1.57 times higher than that under the bubble diameter of 60 μm. When the bubble collapses away from the wall, due to the weakened constraint of the wall, more intense energy is released, but the energy decays rapidly in the propagation process, and the energy loss when it reaches the wall is more serious. In this paper, the surface micromorphology is simplified into a near-curved shape. After the modification, the flow obstruction on the near-curved concave wall inhibits bubble collapse, resulting in an increase in bubble collapse time. The stress and deformation caused by a single bubble collapse are concentrated within a radius of 1mm and a depth of 5 μm
A novel problem and algorithm for solving permuted cordial labeling of corona product between two graphs
Full text linkThis study has come up with a new application of permuted cordial labeling initiated by two graphs based on their corona product, furthering the cause of a better comprehension of and research into specific types of graphs. The Permuted cordial labeling construction for the corona product of graphs consisting of paths, cycles, second power of a path and second power of cycle graphs may facilitate the consideration of the properties and structures of the graphs. It helps us to study its topological properties, connectivity images, symmetries and other properties
Design peculiarities and kinematic analysis of a shaking conveyor with multiple transporting and screening trays
Full text linkThe paper focuses on the design peculiarities and kinematic analysis of a novel shaking conveyor equipped with three interconnected transporting and screening trays. The goal is to develop a comprehensive mathematical model to describe the system’s motion and analyze the interplay between the trays, providing a basis for improved design and optimization. The scientific novelty lies in the detailed kinematic study of this specific multi-tray configuration, particularly the interaction of the dual beam systems actuating the intermediate tray, leading to complex coupled motion profiles. The practical value of the research is substantial for designing and optimizing such multi-functional vibratory equipment, as the kinematic data (displacements, velocities, accelerations) provide critical insights into material-tray interaction, aiding in predicting and enhancing material processing efficiency, estimating inertial loads for robust structural design, and informing vibration isolation strategies. The methods employed include the development of a kinematic diagram and corresponding motion equations for the multi-loop linkage mechanism, followed by numerical modeling of the system’s motion using Wolfram Mathematica software. The main results characterize the complex motion profiles for a steady-state operational frequency of 10 Hz, revealing distinct amplitudes and near-linear inclined trajectories for key hinges representing each tray. Notably, the upper tray exhibited the most significant displacements and accelerations, with horizontal accelerations reaching approximately 3 g and vertical accelerations around 1.3 g, indicating a motion profile conducive to effective material lifting, “throwing”, and bed stratification. Scopes of further research include a complete dynamic analysis incorporating mass properties and driving forces, experimental validation of the models, optimization of geometric and operational parameters, integration with Discrete Element Method (DEM) simulations for detailed material flow analysis, and investigations into wear, fatigue life, and advanced control strategies
Permeability test of geotextile-soil system under different sand filling heights
Full text linkGeotube dams are constructed by stacking geotubes, which are non-homogeneous structures composed of geotextiles and filled sand. Therefore, studying the permeability characteristics of the geotextile-soil system is of great significance for seepage analysis in geotube dams. While the permeability characteristics of geotextiles and filled sand have been extensively studied individually, there has been relatively little research on the permeability characteristics of the geotextile-soil system formed by the combination of geotextiles and soil. In this study, a self-designed permeameter was used to investigate the permeability characteristics of the geotextile-soil system under different sand filling heights. The test results indicate that the permeability coefficient of the geotextile-soil system decreases continuously with the increase in permeation time and eventually stabilizes. The permeability coefficient of the geotextile-soil system increases with the sand-filling height and finally approaches but remains slightly smaller than that of pure sand with the same gradation. The influence of geotextiles on the permeability of the geotextile-soil system is significant within the range of 0 to 5 cm. Additionally, the water permeability of geotextiles affects the permeability performance of the geotextile-soil system. Specifically, a larger porosity corresponds to higher water permeability, and a greater permeability coefficient of the geotextile leads to a higher permeability coefficient of the geotextile-soil system