A neural network adaptive impedance control method based on stiffness damping characteristics was proposed to address the problem that the force tracking performance of impedance control for sanding robots was affected by the unknown environmental stiffness and the change of environmental position.Since the reference trajectory is not easy to be determined due to the unknown environmental parameters,an adaptive PI control law was constructed to compensate the reference trajectory and reduce the steady-state error of force tracking; in order to improve the dynamic performance of the force tracking control,according to the uniform regulation law of the force error on the stiffness coefficient and damping coefficient—stiffness damping characteristics,and combined with that the force error has the characteristics of time-varying and non-linear,an activation function describing the relationship between force error and stiffness damping characteristics was designed,and an adaptive impedance parameter neural network model was constructed,whose outputs were stiffness coefficient and damping coefficient,to ensure the suppleness of force tracking control through the impedance control based on the fusion of the reference trajectory compensation and an adaptive impedance parameter neural network model. The simulation results show that the proposed adaptive impedance control method has better force tracking effect than the traditional impedance control and the impedance control with reference trajectory PI compensation.

Main journal is crucial for the crankshaft of the diesel engine. The machining accuracy of parallelism is one of the determining factors to ensure the strength and reliability of the crankshaft. At present,there are some limitations in manual or non-contact detection methods,such as low automation,low measurement accuracy or susceptible to surface factors.Therefore,a contact detection method is proposed based on wireless transmission. By designing a contact sensor ring array device to collect data of the main journal in turn,coordinates of contour points are extracted and analyzed by using the principle of spatial analytic geometry.The parallelism of the main journal is obtained in real time during working time. The experimental results showed that the maximum error in detecting the parallelism of the main journal was 9.65 %. This detection method can effectively measure the parallelism between the crankshaft main journal and the centerline,providing a theoretical basis for crankshaft in machine inspection.

Rule-driven scheduling is essential for online workshop scheduling. However, to maintain scheduling performance and avoid disturbances and economic losses, rules at various levels must quickly change in response to workshop demands and environmental changes. To tackle this challenge, a dynamic analytical model is proposed to monitor the performance of scheduling rules while multiple production indexes and complex state information are dynamically changing in the workshop. Firstly, the workshop in the production scheduling process is divided into a hierarchical structure. A network linking combined rules, state information, and performance indexes is established after analyzing the characteristics of state information changes at the process unit, buffer,and equipment levels. Secondly, complex coupling relationships between various object entities in the associated network are defined based on the Multiple Resolution Entity (MRE) concept to guarantee the dynamic consistency of performance analysis for scheduling rules at various levels. The Parallel Discrete Event System Specification (PDEVS) technique is used to model MRE, creating a coupled model that captures the complex relationships between various entities and creating a mechanism for dynamic performance variation in key entity scheduling rules in the atomic models of the different hierarchical units. Finally, by using the production scheduling procedure of a particular workshop as an example, the effectiveness of the model and methods was confirmed.

To improve the flexibility of flexible job shop dynamic scheduling and real-time response ability to disturbance,Multi-Agent and Dynamic game Real-time Scheduling method (MDRS) based on multi-Agent dynamic game is proposed. First,a real-time scheduling system based on multi-Agent was constructed,and the coordination mechanism among Agents is designed. Through the division of labor and cooperation of Agents,the real-time production scheduling and control of the workshop are realized. On this basis,a real-time allocation method based on non-cooperative dynamic game is proposed considering machine failure and new task insertion. When the abnormal event occurs,the dynamic game model is re-established according to the real-time information of the workpiece and the machine,and a three-party non-cooperative dynamic game is formed with completion time,total production energy consumption and key machine burden as players. Nash equilibrium search algorithm based on backward induction is designed to solve sub-game refined Nash equilibrium and optimize real-time distribution scheme. Finally,the system is realized with JADE platform and simulated in different test scenarios. The simulation results show that the three optimization objectives can reach Nash equilibrium,and are improved to varying degrees compared with other algorithms,which verified the feasibility of the multi-Agent real-time scheduling system and the effectiveness of the MDRS.

Aiming at the poor control effect of the traditional shaker,an adaptive iterative learning control algorithm was proposed,which constructs an external displacement closed-loop on the basis of the original displacement three-parameter control system to form a double closed-loop control system. At the same time,in order to more accurately simulate the dynamic behavior of the shaker,the Gray Wolf Optimization (GWO) algorithm was introduced to optimize the nonlinear organic originated regression neural network (Nonlinear Auto-Regressive with exogeneous inputs neural network,NARX) for shaker model identification. The simulation results show that the shaker model identification using GWO-NARX neural network achieves a high identification effect with an accuracy of 99.8 %. On the basis of identification model,the control accuracy of the shaker was greatly improved by using the adaptive iterative learning control algorithm,and the maximum error is reduced by 49.6 % compared with the original system. Compared with the traditional NARX neural network for shaker model identification,the GWO-NARX neural network has a better identification effect and the model is closer to the real system;compared with the traditional three-parameter control system,the adaptive iterative learning control algorithm improves the accuracy of shaker waveform reproduction and can better adapt to the complexity of the system. It provides reliable technical support and solutions for practical engineering applications.

A learning mechanism ant colony algorithm was proposed for the path planning problem of mobile robot in U-shaped obstacle environment. Firstly,to solve the problem of long algorithm running time,neighborhood removal was introduced to discard poor and symmetric paths. Secondly,to solve the problem of slow convergence speed,taboo strategies were applied to enable ants to quickly escape U-shaped obstacles.Then,to solve the problem of path deadlock,a learning mechanism was proposed to continuously discard deadlocked paths. Finally,a simulation comparison was conducted between the proposed algorithm and other improved algorithms. The results showed that the learning mechanism ant colony algorithm not only shortened the running time compared to the control group algorithm,but also improved the convergence speed, which verifies superiority of the algorithm.

In complex obstacle environments,mobile robots using the Gaussian Path Motion Planning (GPMP2) algorithm suffer from the problems of falling into local optimums and poor obstacle avoidance performance,a method based on stochastic restart and obstacle avoidance improvement was proposed Gaussian Process Motion Planning with Stochastic Restart and Obstacle Avoidance Improvement (GPMP2-SROAI). Firstly,the random restart mechanism in the Covariant Hamiltonian Optimisation for Motion Planning (CHOMP) was introduced to apply perturbations to the trajectory to jump out of the local optimum and improve the efficiency and robustness of the trajectory optimization. Then,a Model Predictive Control with Control Barrier Function (MPC-CBF) approach based on the barrier function was introduced to avoid collisions by predicting the range of motion of the robot during the optimisation process. Simulation results show that, the improved algorithm achieves a success rate of 92.6 % in path planning,which is 24.9 % higher than that of GPMP2,12.5 % higher than the path shortest probability,and 4.8 % higher than the average smoothing degree,and also achieves a better quality of trajectory planning compared with the mainstream algorithms,with smoother trajectories and better obstacle avoidance effects.

Regarding the impact of uncertainty caused by internal and external disturbances on trajectory tracking accuracy of wheeled mobile industrial robots,a non-singular terminal sliding mode trajectory tracking control method was proposed. Firstly,the dynamic model and trajectory tracking error model of wheeled mobile industrial robot were established. Then,an extended state observer was designed to estimate the uncertainty in the system,and a non-singular terminal sliding mode control law was designed to suppress the influence of disturbances on the control system through real-time compensation of uncertainty,achieving high-precision control for the motion trajectory of wheeled mobile industrial robots. Finally,the stability analysis was conducted using Lyapunov theory. The simulation results show that the designed extended state observer can accurately estimate the uncertainty in the system,and the maximum errors of estimation are only 0.02 m/s and 0.03 rad/s separately. The designed non-singular terminal sliding mode control law has higher tracking accuracy and stronger robustness compared to the extended Kalman control method and the integral sliding surface control method,the maximum error of trajectory tracking is only 0.04 m. The test results verifiy that the proposed control method meets the design expectations,the maximum error of trajectory tracking is only 0.07 m,which can ensure that the wheeled mobile industrial robot can more accurately track the desired trajectory.

Aiming at the dynamic changes in wheel profiles, the optimal rail top/side Friction Coefficient Combinations (FCCs) for wheels with worn flange and worn tread profiles in different service stages were explored separately.Wheel profile wear data was tracked along two subway lines as the distance traveled increased.A dynamic model of subway vehicle was constructed to analyze the effects of rail top/side FCCs on wheel-rail wear properties,attack angle of wheelset,surface fatigue index and derailment coefficient,with the dynamic changes in wheel profiles taken into account. Then a multi-objective optimization model was developed based on the entropy-weighted TOPSIS method,which includes the four performance indicators mentioned above as the criteria. Finally,the optimal FCCs were obtained.The results suggest that as wheel profiles dynamically change,the wheel-rail contact points show sudden changes in position and tend to concentrate to varying degrees,resulting in poorer match between wheels and rails.The comprehensive performance of worn flange profiles is affected primarily by rail top friction coefficient during initial operation. As the distance traveled increases,the rail side friction coefficient predominates.The comprehensive performance of worn tread profiles is influenced mainly by rail top friction coefficient in all stages of wheel service.During the stages of initial operation,early wear,middle wear,and late wear,the optimal rail top/side FCCs for wheels with a worn flange profile are 0.30/0.10,0.25/0.10,0.20/0.10,and 0.20/0.10,respectively. For wheels with a worn tread profile,these are 0.20/0.15,0.20/0.30,0.20/0.35,and 0.30/0.15,respectively.

To solve the problem of trajectory tracking accuracy and control stability of driverless vehicles, a method that considers feedforward control and dynamically adjusts speed PID controller parameters is proposed. Seven polynomial is used for lane change trajectory planning, the double PID controller of longitudinal position and speed is improved, the longitudinal displacement error is adjusted dynamically, and the PID parameters are adjusted in real time. Meanwhile, the lateral displacement error and the pendulum velocity error are solved, making the tracking error converge, and finally the control amount is converted into the desired front wheel angle through the motor model, thus solving the problem of large lateral tracking error caused by the model mismatch. By simulation verification, when the vehicle is running in the urban road scene at 60 km/h, the transverse displacement error is controlled within 0.015 m, the longitudinal displacement error is controlled at mm level, and the error range is controlled at [0.002,0.006] m. The speed of the body is stable and the speed error is not more than 0.83 rad/s. On this basis, the real vehicle experiment is further completed. The results of both simulation and real vehicle experiment show that the designed controller can meet the requirements of high precision in trajectory tracking and ensure the smooth driving of driverless vehicles in the lane change condition.

Taking the vertical CNC machine tool column as the research object,a new bionic configuration of Basalt Fiber reinforced Polymer Concrete (BFPC) column structure was proposed. The finite element model of the machine tool column was established,and through the analysis of static and dynamic performance,based on the response surface method and genetic algorithm,the Latin hypercube experimental design was carried out on the design parameters that greatly affected the performance of the BFPC column,and the response surface model was established by sensitivity analysis,and the column mass,maximum static deformation and first-order natural frequency were selected as the optimization targets,and the Pareto optimal solution set was obtained by multi-objective genetic algorithm. The simulation results show that compared with the prototype column,the optimized column mass is reduced by 42.64 %,the maximum static deformation is reduced by 20.98 %,and the first-order natural frequency is increased by 2.99 %. This optimization method significantly improves the static and dynamic performance of the column,achieves the design effect of light weight and optimal size,and can provide a reference for the optimal design of the column.

A part model feature recognition method combining attribute adjacency graph and point cloud was proposed by combining two feature recognition methods to overcome the limitations of current part model feature recognition technology based on attribute adjacency graph and point cloud.The model attribute adjacency graph was used to match feature subgraphs to find and separate feature surfaces,and then the feature surfaces in point clouds were sampled.The point cloud classification network structure on the basis of PointNet network was improved by adding a local feature extraction module and a Transformer based non-local feature extraction module and combining feature attribute adjacency graph information with original point cloud data.Experimental results indicate that the recognition accuracy for 24 common features is 99.92 %.

The waviness of bearing raceway is the main influencing factor on the vibration characteristics of bearings.In order to reduce the vibration level of silicon nitride ceramic ball bearings,a research on the superfinishing process control of silicon nitride ring raceway waviness was carried out. Superfinishing process experiments was conducted on silicon nitride ring raceway using a raceway superfinishing machine,the influence trend of superfinishing process parameters on raceway waviness was studied. A bearing calculation model for vibration characteristics was proposed considering raceway waviness,and the effectiveness of the culation model was verified through vibration testing,the influence of raceway waviness was analyzed on bearing vibration characteristics. The results show that in superfinishing process,the raceway waviness first decreases and then increases with the increase of the tangential velocity of the workpiece,and then decreases and then increases with the increase of the oscillation frequency; the vibration level of the bearing gradually intensifies with the increase of raceway waviness. When the pressure of the oilstone is high,increasing the tangential velocity of the workpiece and oscillation frequency for rough machining results in a rapid decrease in raceway waviness. In superfinishing process,when the tangential velocity of the workpiece is 700~800 m/min,the oscillation frequency is 800~1 000 times/min,and the oilstone pressure is 0.2~0.4 N/m²,the surface waviness is≤0.05 μm. The root mean square value of bearing vibration acceleration is ≤2 m/s²,which meets the requirement of Z3 level bearing vibration acceleration.

The drill bit for carbon fiber reinforced plastics machining facing the issues like short running life and bad machining quality.Carbide drill bits with DLC coating would be a good solution to increase the life of drill bits which with lower frictional coefficient and higher surface hardness. 4 groups of samples had been studied like uncoated carbide drill bits,monolayer DLC coating,Cr/W-DLC/DLC and Cr/CrN/DLC composite coating. By comparing the frictional coefficient,surface hardness and roughness of different coatings,the data showed that all the drill bits with DLC coating had advantage of low frictional coefficient and could improve the surface roughness.The data of cutting torque force,cutting temperature,cutting speed and frank wear from 4 groups of samples' drilling testing showed that the composite coating of Cr/CrN/DLC provided best service life and much more stable performance,its service life was 2.65 times of the drills without coating.

In order to solve the problem of curve recognition and extraction for laser 3D contour projection,a method based on OpenCASCADE (OCC) platform is proposed. This method uses OCC,an open source 3D geometry engine,to read the digital model of the projected part,combine the relevant geometric and topological data structures,and identify the type of sectional curve based on local geometric properties. Finally,the contour coordinate data is written into the PLY file by sampling methods such as equal arc length and segmentation. Based on the above curve recognition and extraction methods,a 3D contour curve recognition and extraction system is developed. The system can be applied to the projection indication in the fields of machinery manufacturing and composite layering,which provides effective support for the realization of digital manufacturing.

To address the issues of slow surface defect detection speed in industrial aluminum profiles,as well as low accuracy in detecting small target defects such as scratches and paint bubbles,a small target aluminum profile surface defect detection model based on improved YOLOv8n was proposed. Firstly,cross-scale feature fusion was employed to enhance the network's ability to perceive multiscale information,by integrating features from different scales. Secondly,the C2f structure of the YOLOv8 backbone network was improved by incorporating RFAConv attention convolution. This was done to design a new structure to enhance the network's feature extraction ability and improve the accuracy of small object detection. In the neck region,lightweight RepGhost modules were used to reduce parameter count and computational complexity. Finally,the original YOLOv8's CIoU loss function was replaced with WIoUV3 to improve detection accuracy and convergence speed.Extensive experiments on aluminum profile defect datasets showed the model's effectiveness. It achieved a 2.5 % increase in mAP@0.5,The frame rate has reached 110 fps,and reduced weight file size by 16.2 %,surpassing the original model's performance. Experiments on the VOC2012 dataset and the Northeastern University hot rolled strip surface defect dataset demonstrated the algorithm's robustness.The refined model effectively meets industrial demands for aluminum profile detection.

To address the issue of local optima arising from the insufficient flexibility of traditional ratio-based studies on vertical axis wind turbine blades, it is essential to enhance the conversion efficiency of wind turbines when faced with complex and variable real-world challenges. This study focuses on optimizing multiple aerodynamic performance indicators, such as lift coefficient and drag coefficient, for existing airfoils to improve their aerodynamic characteristics. It employed a Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) with an elite strategy for optimization, combined with airfoil parameterization to derive optimized airfoils, followed by simulation validation of their aerodynamic performance metrics. The results indicate a significant enhancement in the aerodynamic performance of the optimized airfoil, with a 20.85 % increase in lift-to-drag ratio, a 17.35 % increase in lift coefficient, and a 2.91 % reduction in drag coefficient. Validation results demonstrate that the optimized airfoil exhibits improved wind energy conversion efficiency compared to the original design, showing better self-starting capability at low wind speeds, a broader range of adaptable wind speeds, and higher wind energy conversion efficiency. This optimization design integrates genetic algorithms with fluid dynamics simulations, providing new insights for enhancing the wind energy conversion efficiency of vertical axis wind turbines.

Sand casting is one of the main processes for the current production of complex parts.The technical difficulty,excess auxiliary machinery and process integration of complex parts production are all closely related to the design of the molding line.In view of a series of problems existing in the current sand casting production line,such as unstable sand mold transportation and positioning,inefficient recycling of auxiliary equipment,low production efficiency, and redundant human resources,a horizontal splitting fully automatic molding line is designed,which mainly includes the entire production line key mechanisms such as layout,bottom plate clamping mechanism,box pressing iron conversion device,bottom plate conversion device,mold ejection device and sand trough belt conveyor,and it is based on Programmable Logic Controller (PLC) to accurately control the coordination of each key component operation,realizing automatic control of the production process.Practical applications have shown that the designed fully automatic horizontal parting molding line can realize automatic molding pouring and sand shakeout of sand casting products,effectively improving the smoothness and production efficiency of the production line space,reducing human labor intensity,and optimizing production environment.

The urgent demand for tiny parts and products in many industries has promoted the rapid development of microfabrication technology. As one of the most promising microfabrication technologies,micro cutting technology can efficiently and accurately generate complex features in metals,ceramics,polymers and composite materials,and is widely used in medical,communications and aerospace fields. Micro cutting tools have been widely concerned by the industry and researchers as a key factor which can affecting the machining quality of micro cutting technology. The latest achievements and development of micro cutting tools were reviewed and discussed. The development status and performance of micro milling,micro turning,micro drilling and micro grinding tools were emphatically discussed and analyzed,and the failure analysis,monitoring and coating of micro cutting tools were introduced in detail. Finally,the possible development trend and research direction of micro cutting tools in the future were emphasized,hoping that can provides important reference for the researchers of micro cutting tools.