To achieve coordinated control of multi-speed operation in the spiral concrete batching machine, this paper proposes a trajectory tracking-based coordinated control method. By analyzing the working process of the spiral concrete batching machine, the coordinated relationship between lateral movement and concrete conveying rate is investigated, and a two-degree-of-freedom kinematic model for the batching machine is established. The error model for the trajectory tracking system of the batching machine is developed and discretized to meet the requirements of computer numerical calculation and real-time control. Based on model predictive control theory, a prediction equation for the trajectory tracking system is established. The objective function for the batching machine trajectory tracking is formulated, and the optimal control strategy is derived. Simulation results demonstrate that the trajectory tracking-based control method effectively achieves coordinated control of multi-speed operation in the spiral concrete batching machine.

To thoroughly explore the extensive applications and prospects of knowledge graphs in the domain of intelligent manufacturing, aiming to support the sustained development of the manufacturing industry, the domain of intelligent manufacturing has been categorized into four dimensions: vertical industry applications, manufacturing process applications, domain graph construction technology, and intelligent services. Through this study, the significance of knowledge graphs in driving the evolution of intelligent manufacturing is reviewed. Furthermore, a framework for an intelligent manufacturing knowledge graph, rooted in manufacturing domain knowledge data, is proposed. This framework encompasses three key modules: manufacturing domain data, graph construction, and intelligent services, providing theoretical support for the continuous upgrading of intelligent manufacturing. Research findings emphasize the crucial role of knowledge graphs in advancing the intelligence of the manufacturing industry and the broad potential of knowledge graphs in the field of intelligent manufacturing. Additionally, an outlook on future research directions for knowledge graphs in the domain of intelligent manufacturing is suggested, with a focus on exploring the integration of domain graphs with next-generation artificial intelligence technologies to propel the continuous innovation and intelligent evolution of manufacturing.

During the operation of an air cushion belt conveyor, factors such as fan air volume, number of air holes, and film thickness have a significant impact on the flow field characteristics and bearing capacity of the air cushion. By establishing a simulation model for the air cushion flow field, analyze the changes in air cushion pressure under different working conditions. Based on experimental comparison and analysis, the variation law of air cushion pressure under different air volume and number of air holes, as well as the variation of air film thickness and air film pressure under different air volume were analyzed. The results show that as the air volume increases, the film pressure gradually increases, and the film thickness changes from 5% to 30%. As the number of pores increases, the pressure gradient of the air cushion changes faster and exhibits a parabolic distribution, and the degree of change in air film thickness decreases. The experimental and simulation results show that the changes in the air cushion flow field are consistent, and the fan air volume is 15-20 m³/m, the optimal value K₁ for the stability of the air cushion flow field is 1.31, and the optimal working condition is 5 exhaust holes, which meets the requirements of actual operating conditions.

In order to investigate the effect of tower slewing motion on tip displacement and to provide a basis for collecting tip displacement data to improve the accuracy of tower steel structure damage diagnosis, this paper adopts a tower physical structure experiment bench to select three different strokes of high, medium and low slewing speeds, and to collect tip displacement during slewing motion and static tip displacement. A characterization study of the tip displacement for slewing stroke and speed change was carried out; the structural response characteristics of the slewing motion were established and characterized by noise characterization coefficients. The experimental results show that the collected tip displacements are close to static displacements when the slewing stroke is larger, the slewing speed is slower, and the noise characterization coefficient is smaller than a set threshold. The purpose of this study is to avoid the influence of slewing motion on the real-time monitoring of structural damage, and to give a scientific basis for the setting of the data acquisition conditions for the required tower steel structure damage diagnosis.

A theoretical model of stress distribution and contact width is established by Hertz contact theory. The reaction of roller under specific load is analyzed. The results shows that linear profile have significant edge contact stress concentration, which increases with the increase of load. The roller with logarithmic profile can solve the stress concentration, but it can't completely eliminate it. Under the same load, with the increase of the bias factor, the maximum contact stress of the roller decreases sharply at first and then increases slowly. The position where the maximum contact stress occurs shifts from both ends of the roller to the middle of the roller. The profile design of roller with logarithmic type can take a value between 1.0 and 2.5 for the bias factor based on working conditions, roller size and load conditions. The value of bias factor is adaptive to tilt error and the impact of tilt error on contact stress can be reduced by setting a reasonable value.

In order to explore the safety threat of slope deformation to transmission lines, taking the transmission tower-line system as the research object, the overall finite element model of the tower-line system considering soil-structure interaction (SSI) effect is established, and the rationality of the numerical analysis model is verified based on the field measured data. On this basis, considering the relationship between the slope deformation area and the spatial position of the tower and the influence of the slope deformation angle, the response law of the stress characteristics of the tower-line system to the slope deformation is explored. The results show that when the towers are located above, below and inside the slope deformation body respectively, the failure mode and deformation resistance of the upper tower line system are significantly different. And with the increase of slope deformation angle, compared with horizontal slope deformation, the anti-deformation ability of tower-line system will decrease by 25%-50%. When the tower is located outside the slope deformation body, the anti-deformation ability of tower-line system will decrease most seriously, with the decrease range of 33%-50%.

The study examines wires in high-vibration zones of aircraft, where artificial damage was introduced to accelerate wear. Step-up-stress vibration testing was conducted to simulate accelerated aging and measure wire wear over a fixed period. Three surrogate models were developed using the wire diameter after artificial damage as the input and the experimentally obtained wire wear as the output. This established a nonlinear relationship between the initial artificial damage and the wear rate. The finite difference method was applied for time superposition to approximate the entire life cycle. Results indicate that the surrogate model using a back propagation neural network (BPNN) achieved the highest accuracy. Predicting lifespan through wear rate across the product's lifecycle can significantly reduce experimental costs. These findings provide theoretical and experimental guidance for future research on anti-wear technology and health management of aircraft wiring harnesses.

This paper proposes a robotic grasping technique based on object recognition and fully convolutional grasp quality convolutional neural network (FC-GQCNN). To address the limitations of traditional GQCNN, such as low computational efficiency and redundant feature calculations, an improved FC-GQCNN is developed. By replacing the fully connected layers in GQCNN with 1×1 convolutional layers, the proposed network can handle input images of arbitrary sizes. Furthermore, the integration of FC-GQCNN with the YOLOv8 object detection algorithm forms a YOLOv8-FCGQCNN cascade structure, effectively solving the challenges of object recognition and localization in complex environments. Experimental results demonstrate that this method achieves an 86% grasp success rate across 10 different objects, with an average detection time of 0.09 s per frame, which is 22 times faster than traditional GQCNN, significantly improving system efficiency. This method can accurately detect the grasping position of the object of interest and has higher reliability than the baseline method.

In order to meet the operation requirements of the Tianjin trunk line water conveyance box culvert of the South to North Water Transfer Project, a special multifunctional operation vehicle is developed and its high precision control is realized. Firstly, the structure and working mechanism of the multifunctional operation vehicle are described, and its kinematics models are established respectively. Secondly, a cross coupling synchronization control strategy is proposed to solve the problem of vehicle body attitude deviation caused by speed non-synchronization in dual motor independent control. Finally, the system model of multifunctional vehicle is built and the simulation analysis of cross coupling synchronization control is completed. The results of software simulation and system engineering application show that the proposed cross coupling synchronous control strategy can effectively improve the non-synchronization errors of double motors and improve the attitude control accuracy of the multifunctional vehicle for box culverts, and has strong engineering practicability and application popularization.

By predicting the wear trend of aeroengine, the wear state of aeroengine can be monitored effectively. Among the effective observation data reflecting the engine wear state, the oil analysis data can indirectly reflect the overall wear trend of aeroengine. Therefore, by establishing a trend prediction model based on oil sample analysis data, so as to realize the wear trend prediction of engine. However, the current models used in aeroengine trend prediction are mainly single prediction models, and the combined prediction models are only general linear combinations, with poor prediction effect. Therefore, a nonlinear variable weight combination prediction model based on support vector machine is proposed, and realizes the parameter optimization through particle swarm optimization algorithm. The oil sample analysis data is obtained through the bearing fatigue test of the whole life oil system, and the oil samples are collected at fixed intervals for performance analysis. Through the combination prediction analysis of the spectral analysis data, by comparing the prediction results of the combination prediction and the prediction results of the single prediction model, the prediction accuracy exceeds the prediction accuracy of the single prediction model, which fully verifies the superiority and effectiveness of the combination prediction model proposed in this paper.