In order to study the influences of magnetoelastic abrasive particles on the tool position during tool passivation and the tool rotation direction on the passivation effect under the same position,a magnetic field model was established based on the double-disk magnetic passivation mechanism and experimentally verified. Firstly,the simulation models for different positions of the tool in the magnetic field were established. Using COMSOL software,the changes in magnetic flux density in the magnetic field when the tool was positioned at distances of 2,4,6,8,and 10 mm from the disk,as well as when the tool was tilted at 30° at the 10 mm position were analysed. Secondly,based on the passivation mechanism of magnetoelastic abrasive particles in the double-disk magnetic passivation equipment,the passivation experimental scheme of the cutting edge of the tool at different positions was formulated,and the forward and reverse rotations of the tool were compared. Finally,the passivation effect of the tool under different parameters was discussed through the cutting edge morphology and passivation amount. Experiments show that the passivation effect of the tool is the best at a distance of 4 mm from the disk,and the passivation accuracy is high although the passivation speed is slow when the tool is reversed.

Aiming at the hybrid flow shop scheduling problem, an improved multi-objective salp swarm optimization algorithm based on Q-learning is proposed, simultaneously considering the minimization of makespan and equipment processing energy consumption. To enhance the convergence speed of the algorithm, a strategy combining chaotic mapping and heuristic rules is employed to generate diversified initial populations. To balance the global search capability and local exploitation capability of the algorithm, a Q-learning adaptive selection strategy is introduced in the selection of leaders proportion. To improve the optimization accuracy of the algorithm, an effective variable neighborhood search strategy is proposed to strengthen the local exploitation capability. Experimental validations conducted on public datasets demonstrate that the proposed algorithm can effectively solve the multi-objective optimization problem in hybrid flow shop scheduling.

Because of the reverse operation of the double-deep four-way shuttle warehouse, the efficiency of the outbound operation is reduced. The mathematical model of the process of outbound and inbound was established. A strategy for selecting the location of reverse and a strategy for selecting the location of back were proposed in which the influence of the current operation on the subsequent outbound was considered. A task sequence optimization algorithm was proposed to minimize the task time. An improved genetic algorithm was used. By using the Kendall coefficient to initialize the population, the spatial search ability of the algorithm was enhanced. The characteristics of the double-deep warehouse was taken into account, and then a gene order change process was introduced into the genetic algorithm to improve its convergence speed. The results of simulation experiment show that the proposed strategy can significantly improve the efficiency of outbound operation. At the same time, the improved genetic algorithm has faster convergence speed, better optimization effect and higher stability, which can effectively shorten the outbound operation time and improve the outbound operation efficiency.

Aiming at the problem of decreased trajectory tracking accuracy caused by actuator failure and offset faults in industrial robot arms,a terminal sliding mode adaptive fault-tolerant control method based on high-order sliding mode observer was proposed. Firstly,the dynamic equation of the industrial robot arm with actuator faults was established by analyzing the motion state of the industrial robot arm. Then,a high-order sliding mode observer was designed to accurately estimate the fault terms within the system,and an adaptive fault-tolerant control law was designed based on the terminal sliding mode surface to compensate for the impact of actuator faults. Finally,the stability analysis was conducted using Lyapunov function,demonstrating that the proposed control method can accurately track the command signal of the motion trajectory of industrial robot arms.The simulation results of a 6-degree-of-freedom industrial robot arm show that the designed high-order sliding mode observer can accurately estimate the faults within the system,with a maximum estimation error of only 0.011 (°)/s². The proposed terminal sliding mode adaptive fault-tolerant control law can effectively overcome the impact of actuator faults on the motion trajectory of industrial robot arms,and the maximum and average errors on the entire motion trajectory are 0.64 and 0.24 mm,respectively,demonstrating strong robustness. The positioning test results of fixed coordinate points in three-dimensional space show that the proposed terminal sliding mode adaptive fault-tolerant control law has high control accuracy,with maximum and average positioning errors of 0.89 and 0.31 mm,respectively,verifying the strong engineering practicality of the proposed control method.

Aiming at the problem of active obstacle avoidance and tracking control of mobile charging vehicles on narrow roads in old neighborhoods,a road model based obstacle avoidance path planning and low-speed tracking control strategy was proposed. Firstly,a community road model was constructed,and a quintic term path planning algorithm was used to achieve optimal obstacle avoidance path planning,taking into account path quality and road risk potential fields.Secondly,a Linear Quadratic Regulator (LQR) lateral and speed compensation PID longitudinal controller optimized by Genetic Nonlinear Decreasing Weight Particle Swarm Optimization algorithm (GA-NLDWPSO) was designed to achieve tracking of the planned path.Finally,a joint simulation platform of PreScan,CarSim and MATLAB/Simulink was built to verify the effectiveness of the proposed method. The simulation results showed that the proposed method could ensure the safe obstacle avoidance for mobile charging vehicles and achieved rapid speed control based on its low-speed characteristics. In response,the maximum longitudinal velocity error after stabilization was 0.059 km/h,and the maximum lateral error was effectively reduced,significantly improving tracking accuracy and stability.

In order to solve the limitations of traditional model prediction algorithms in intelligent vehicle trajectory tracking and to improve the accuracy of trajectory tracking and the lateral stability of the vehicle during the automatic driving process,a model prediction control trajectory tracking strategy based on adaptive fuzzy adjustment of weight coefficients and combining front-wheel cornering compensation and rear-wheel active steering control was proposed. First,the overall control structure was divided into two layers:the upper layer was the trajectory tracking control layer,which used the model predictive control algorithm to derive the ideal front wheel turning angle of the vehicle;the lower layer used the adaptive sliding mode control theory to design the front wheel turning angle compensator and the active rear wheel steering controller to derive the compensated front wheel turning angle and the rear wheel turning angle of the vehicle,and thus realized the four-wheel steering trajectory tracking control. Secondly,for the problem that the model predictive control with fixed weight coefficients was poorly adapted to the change of external vehicle speed and road surface adhesion coefficient,a weight coefficient adaptive adjustment strategy based on fuzzy control was proposed. Finally,through Simulink and CarSim software,a comparison was made with the traditional algorithm under the experimental conditions of different vehicle speeds and different road surface adhesion coefficients,and the simulation results show that different longitudinal vehicle speeds have a great degree of influence on the trajectory tracking of the traditional algorithm,while the improved algorithm has less influence. Especially when the vehicle speed is high and the road surface adhesion coefficient is low,the improved algorithm makes the trajectory error smaller,and at the same time ensures that the vehicle can drive safely and stably.

To solve the problem of driving anti-slip of Distributed Drive Electric Vehicle (DDEV) under complex working conditions,a multi-agent distributed cooperative control strategy based on modular was proposed. Firstly,a modular approach was used to build the whole vehicle structure,and each hub motor wheel and controller as a whole was regarded as an agent,and the slip rate model of the agent was established according to the wheel motion and the whole vehicle motion;then,a Distributed Model Predictive Control (DMPC) strategy based on multi-agents was designed to achieve drive anti-slip with co-optimisation under multiple constraints as the objective function,which solves the problem of insufficient drive power and achieves low energy consumption and comfort at the same time; finally,experiments were simulated using Simulink software and CarSim software. The experimental results demonstrate the effectiveness of the proposed control strategy and provide a new control method for further applications of distributed drives.

To address the issues such as low efficiency,heavy repetitive workload and strong reliance on experience in Numerical Control (NC) programming for manufacturing enterprises,an intelligent NC programming method based on knowledge graph is proposed. Firstly,the relevant knowledge in NC programming domain is analyzed and CAM knowledge elements are constructed to reduce the granularity of knowledge. An ontology model and semantic rules are then established. Secondly,manufacturing data from a marine diesel engine enterprise is utilized for knowledge extraction,the knowledge graph data layer is constructed by ontology mapping. Furthermore,knowledge reasoning is performed on the knowledge graph through entity alignment and semantic rule-based reasoning. Finally,an intelligent NC programming system based on knowledge graph is developed and the effectiveness of proposed method is validated by experimenting with the key components of marine diesel engine.

Aiming at the problems that the production design of aluminum profile extrusion die field relies heavily on the knowledge and experience of designers,the design process knowledge is difficult to reuse,and the experience knowledge resources are lost,the knowledge representation model of extrusion die ontology was proposed,and the construction method of Extrusion Die Knowledge Graph (EDKG) was explored. The rule experience related to die design and aluminum profile extrusion production was summarized as general knowledge,and the knowledge graph model layer of extrusion die was completed by seven-step method of ontology construction. An unsupervised knowledge extraction algorithm based on IA-DBSCAN was proposed. By fusing the entity feature weights of word embedding,information entropy and word frequency-inverse document frequency,the knowledge concepts extracted from the text data in the field of large-scale extrusion die were added to the knowledge graph data layer to realize the logical interaction between the model layer and the data layer. Finally,through the mapping rule R2RML of the resource description framework file,the data storage and visualization of the Neo4j graph database were realized by taking the aluminum profile extrusion die as a case,which can visually display the correlation between multi-dimensional information such as die design,material properties and processing technology,and promote the integration and standardization of knowledge in the field of die processing.

The standardization of process text data is crucial for data integration and reuse in manufacturing. To address the issue of inconsistent and non-uniform descriptions of process text data within manufacturing enterprises,a combined method of unsupervised data matching and supervised learning data matching was proposed,which integrating the BM25 algorithm and the DSSM algorithm to achieve low-cost standardization of process text data. First,the process text data was obtained and preprocessed from the enterprise′s process data management system,and an enterprise data dictionary was constructed based on the actual situation of the enterprise. Next,the unsupervised BM25 algorithm was used to coarsely match small batches of process text data with the enterprise data dictionary at the text similarity level.Experts then verified the coarse matching results to generate a training dataset. Finally,the training dataset was used to support the training of the DSSM algorithm based on supervised learning to achieve fine matching of process text data at the semantic similarity level. Validation was conducted on the standardization task of process names in a home appliance manufacturing company,demonstrating the effectiveness of the proposed method. This method can significantly reduce the labor costs involved in the standardization of process text data in manufacturing enterprises while ensuring the accuracy of the standardization process to the greatest extent possible.

Aiming at the issues of flow non-uniformity and high computational demands due to the compact structure and fine,dense fins of an air-to-air intercooler in a specific type of aviation piston engine,it proposed a numerical simulation method based on overset grids and a composite porous media model. By developing a composite porous media model and a heat exchanger model based on overset grids,the airflow uniformity and heat transfer characteristics within the intercooler under different flow conditions were investigated. The simulation results show that the flow uniformity of the intercooler is influenced by both hot-and cold-side flow rates.When the hot-side flow rate is 0.091 7 kg/s,the overall uniformity coefficient decreases by 1.88 % with an increase in cold-side flow rate,and by 0.82 % when the hot-side flow rate is 0.108 3 kg/s. Comparison between wind tunnel test results and simulations indicates that this method accurately predicts the flow and heat transfer characteristics of the intercooler. The relative error for intercooler outlet temperature does not exceed 2 %,and for heat transfer power,it is within 7.5 %. These simulation results are within a reasonable range of the experimental values,validating the model′s accuracy and reliability.

Blades serve as important components in aircraft engines,and their machining quality has a significant impact on engine performance. To address the issue of exceeding tolerances at certain positions after blade machining,it investigates error compensation in blade machining. First,based on the curvature changes of the blade profile in the U and V directions,adaptive sampling and equal parameter sampling are used to complete the planning of blade measurement points. Then,contact on-machine measurement is used to obtain the error between the theoretical profile and the actual profile. Based on the limited error data obtained from measurements,bilinear interpolation is used to calculate the error at each tool contact point. Finally,the theoretical toolpath is adjusted based on the error at each tool contact point to derive the compensated toolpath. Through an experimental study on a specific model of engine blade,the maximum contour error is reduced from 0.076 mm before compensation to 0.040 mm after compensation. The compensated blade contour now meets the precision requirements,validating the effectiveness of the compensation method.

Wave soldering online quality inspection serves as a crucial method for identifying defective products. High-precision inspection can reduce production costs and support early warning mechanisms during the wave soldering process.Conventional inspection techniques are effective in detecting common defects such as solder skips,cold solder joints,and solder bridging.However,various other anomalies may also occur on the production line,characterized by their suddenness,rarity,unpredictability,and heterogeneity. These anomalies are difficult to detect using standard inspection approaches. To address this challenge,an anomaly detection method based on reconstruction error metrics was proposed.Latent feature vectors generated by existing quality inspection models are reconstructed,and reconstruction errors were used to determine whether a sample was anomalous. This enabled online detection of production anomalies while maintaining high-accuracy detection of common defects. Furthermore,a feature extraction model was developed by integrating the Convolutional Block Attention Module (CBAM) with the EfficientNet architecture,and a Gated ensemble Generative Adversarial Network (GGAN) was introduced to further enhance anomaly detection performance. Empirical verification using production data from an electronics enterprise in Nanjing demonstrated that an anomaly detection rate of up to 98.5 % was achieved.Compared to existing methods,the proposed method significantly improved the detection of anomalous samples in wave soldering production lines while ensuring high-precision identification of typical defects.

To address the need for evaluating production cycles in complex assembly lines with frequent resource allocation disruptions,an XGBoost model-based fast prediction method for complex assembly line production cycle has been developed. This method trained an XGBoost model using a dataset derived from simulation data,applying XGBoost′s built-in feature importance for feature selection and dimensionality reduction. Bayesian Optimization (BO) algorithm was used to refine the XGBoost model′s hyperparameters,the optimized hyperparameters were then assigned to the XGBoost model for predicting production cycles,enhancing prediction performance.Validation on an aircraft assembly line demonstrates that the BO-XGBoost model outperforms LSBoost and Random Forest (RF) models optimized with Bayesian methods.Furthermore,compared to an XGBoost model optimized with traditional genetic algorithms,the BO-XGBoost model achieves a coefficient of determination (R²) of 0.944 and a Root Mean Square Error (RMSE) of 1.71,providing accurate predictions and improving real-time analysis,dynamic optimization,and decision-making capabilities.

In order to study the conveying efficiency of the horizontal double screw conveyer when conveying the viscous material and the integrity of the viscous particles after the conveying process,and to seek the optimum structural parameters of the conveyer,on the premise of ensuring the filling rate of 30 %,by changing the three parameters of screw diameter,screw pitch diameter ratio and overlap area ratio of the conveyor,the simulation was carried out in the discrete element software EDEM,the relationship between the structural parameters and the average axial velocity of particles and the particle breakage rate was studied and the better parameter combination was selected. The effects of the parameters on the average axial velocity of particles and particle breakage rate were evaluated. The results show that increasing the screw diameter is beneficial for enhancing the conveying efficiency of the double screw conveyor when transporting viscous materials,and the change range of particle breakage rote is less than 5 %,and the ratio of screw pitch diameter and overlap area should not be too large. The optimum structure parameters are:screw diameter 200 mm, screw pitch diameter ratio 1,overlap area ratio 0. The three parameters have significant effects on the average axial velocity of particles,the overlap area ratio has a significant effect on particle breakage rote,while the other two have no significant effect.

In response to the issues of high signal complexity and insufficient spatial feature extraction capability in hydraulic pump fault diagnosis,a fault diagnosis method combining CloFormer and Bi-LSTM was proposed. This method embedded the Bi-LSTM neural network into the CloFormer network to enhance the model′s learning capability of fault features and improve the representation performance of global long-distance information and local temporal information. Additionally,a multi-layer feature fusion structure was designed to learn and integrate multi-scale features,promoting the flow and transfer of feature information. Finally,this proposed method was applied to the task of hydraulic pump fault diagnosis to verify its effectiveness. The results demonstrate that the proposed method achieves accurate diagnosis of 5 categories of hydraulic pump states,with accuracy and F₁ scores reaching 98.74 % and 98.85 % respectively,outperforming other 5 comparative methods.

The thermal error suppression of the electric spindle of the traditional machine tool adopts the heat exchange control mode of circulating coolant,which has the problems of low real-time,high cost and poor connection with the numerical control system. Firstly, a Proportional-Integral-Derivative (PID) adaptive control of machine tool electric spindle water cooler based on sparrow search algorithm was proposed. Then,the PID control module of Huazhong CNC system was introduced,and the research on the sparrow search algorithm and its adaptive adjustment model was carried out,and the simulation analysis was carried out. Based on the numerical control PID module,the parameter identification of the control system of the water cooler was completed,the PID adaptive control module of the water cooler was developed,and finally the process monitoring module of the CNC system was designed,and the system was tested and verified. The tests results showed that the proposed method has achieved good results in the thermal suppression of the spindle of the machine tool and has good engineering application value.