30CrMnSiA thin-wall cup-shaped part is a key basic component widely used in the flexible gear of harmonic reducer. Aiming at the problem of the low bearing capacity and short service life of harmonic reducer caused by insufficient strength and toughness of 30CrMnSiA thin-wall cup-shaped parts manufactured by traditional turning method, this paper proposed a plastic deformation-heat treatment process which consists of spinning, quenching, tempering, spinning and aging to manufacture 30CrMnSiA thin-wall cup-shaped parts with less or no cutting and excellent mechanical properties. Through the tensile and impact experiment, by comparing the mechanical properties of each process part, the microorganization of each process part was analyzed. The results show that tempered sorbite microstructure with high strength can be obtained by spinning-quenching and tempering process, but the plasticity was reduced significantly. The fibrous microstructure of spun parts can be further refined by aging heat treatment, and the fine carbides precipitated and uniformly distributed on the ferrite matrix. High strength and good plasticity can be obtained by subsequent aging at 300 ℃ for 6 h. As compared with parts obtained by turning forming after quenching and tempering heat treatment, the yield strength and tensile strength of 30CrMnSiA thin-wall cup-shaped parts manufactured by plastic deformation and heat treatment are improved by 93.65% and 47.88%, respectively. The hardness is increased by 26.87%, and the impact strength is increased by 12.01%. Meanwhile, the elongation and percentage reduction of area are 11.60% and 24.64%, respectively. The thin-wall cup-shaped parts with high strength and toughness can be manufactured by the plastic deformation and heat treatment process of spinning-quenching-tempering-spinning-aging, which provides a new method for manufacturing the thin-wall cup-shaped parts with high-strength and toughness.

Bearing is one of the most widely used rotating parts in industrial equipment. If the bearing runs in fault condition for a long time, it will cause huge economic loss and threaten personal safety, so that the investigation of bearing fault diagnosis is of great significance. Fault diagnosis technology based on deep learning is becoming more and more mature, but there are problems such as over-fitting, unstable effect and low accuracy in the case of small samples. In order to solve these problems, this paper proposes a Transformer variant model MDT (Multi-Head Convolution and Differential Self-Attention Transformer) to realize end-to-end few-shot fault diagnosis. This model combines the new data embedding algorithm of MC (Multi-Head Convolution) and the DSA (Differential Self-Attention) mechanism. The MC algorithm performs multi-path one-dimension convolution on the sample, extends the sample from one dimension to two dimensions by multi-channel output, and extracts rich fault information in each frequency domain in the original sample through multiple convolution kernel sizes. As compared with the original dot product self-attention in Transformer, the DSA mechanism obtains the corresponding attention weight vector for each feature through the difference, so as to extract deeper fault features from the sample. MDT inherits the powerful ability of Transformer to process sequence data, which can extract richer fault information from time-domain signals and avoid the overfitting problem common in small-sample models. Experimental results show that the proposed method can stably obtain more than 99% test accuracy in the bearing fault diagnosis task with only 100 training samples per fault type, and has strong anti-overfitting ability and strong robustness.

The manipulator with five degrees of freedom (5-DOF) cannot reach any pose, so it is easy to get no solution when the traditional pose description method is used to solve the inverse kinematics. In this paper, a 5-DOF manipulator consisting of four rotary joints and one prismatic joint, which is suitable for cleaning, spraying, welding and other operations, is taken as an example to establish a kinematic model and its inverse kinematic analysis is carried out. Then, an inverse kinematics solution method based on joint angle parameterization combined with the feasible direction of approach vector is proposed based on the end pose description of degree of freedom constraints. In this method, firstly, the motion space of the end executor is reduced from three dimensions to two dimensions through joint angle parameterization. Then, the feasible direction of the approach vector of the end executor at different target positions (distal, middle and proximal) is analyzed by geometric method, so as to avoid blind parameter setting and ensure the existence of inverse kinematics solutions. Thereafter, the optimal solution is selected according to the motion continuity and motion range of each joint. The simulation analysis results of path planning show that the actual path is very consistent with the planned path and the joint motion is stable, which proves the feasibility and accuracy of the proposed solution method. The 5-DOF manipulator studied in this paper is representative to some extent and the proposed solution method possesses the advantage of low computational complexity and simple solving process. Therefore, the solution idea can provide reference for the inverse kinematics solution of the manipulator with less degrees of freedom.

With the development of science and technology and the needs of life, flexible gripper technology has gradually become a research hotspot because of its safety and compliance. As a plant that can realize envelope grasping, the movement characteristics of flytrap grass have strong reference for the grasping movement of flexible grippers. According to the soft mesh structure and the deformation mechanism of the Venus flytrap, this paper proposed a hydraulically driven bionic Venus flytrap flexible gripper structure composed of double bionic blades. Firstly, a mathematical model of the relationship between the bending angle and the pressure of the fully embedded single-column grid was analyzed based on the simulation model, and then the relationship between the bending angle and the pressure of the multi-column grid was analyzed based on the simulation model, and the working pressure of the flexible gripper was determined to be 0.040 MPa. The analysis of simulation results shows that the bending angle change and force of the incomplete edge mesh are greater than that of the complete mesh, which proves that the incomplete mesh is the weak point of the strength of the flexible gripper. The bending experiments and closing force experiments of the hydraulically actuated and pneumatically actuated bionic blades were carried out, and it was proved that the performance of hydraulic actuation was better than that of pneumatic actuation under working pressure, and the ready pressure of the flexible gripper was determined to be 0.010 MPa. Finally, adaptive experiments show that the designed flexible gripper can grasp objects of different shapes, and the maximum load capacity is confirmed to reach 304.3 g. The hydraulically driven bionic Venus flytrap flexible gripper proposed in this paper can provide an effective solution for live insect capture and non-destructive harvesting, as well as a theoretical and technical basis for the research and development of bionic plant robots.

Taking the valve control cylinder system controlled by proportional relief valve and proportional speed-regulating valve as the research object, this paper established a dynamics model of hydraulic system. The friction force of propulsion hydraulic cylinder was compensated based on LuGre model. The bristly observer was established to estimate the motion characteristics of the spool and the stability of the observer was proved by Lyapunov first method. The uncertainty of hydraulic system was integrated with external load interference, and the adaptive rate was estimated. Based on the inverse integral adaptive control algorithm, a pressure-velocity compound control strategy was proposed and the stability of the control strategy was verified. Based on the hydraulic cylinder speed control, the pressure error was introduced into the speed expectation to realize the pressure-flow compound control of valve control cylinder system. The co-simulation platform of the valve-controlled cylinder system was established in AMESim and Matlab, and the speed regulation, sudden load and load disturbance of the valve-controlled cylinder system were simulated and analyzed under different error proportions. The simulation results show that the pressure-velocity composite controller has good control performance under the conditions of speed regulation, sudden load and load disturbance of the valve control cylinder system. The overflow control of proportional relief valve effectively reduces the fluctuation and overshoot of system pressure. Overchanging the proportion of pressure error can effectively change the distribution of pressure and velocity error when the formation changes abruptly. In practical engineering, the distribution of compound control error can be carried out by changing the proportion of pressure error according to the need.

The three-parameter Weibull distribution is widely used to describe product longevity because of the convenience and adaptability of its mathematical processing. The three-parameter Weibull distribution with location parameter is one of the most suitable models for studying the reliability of mechanical components, especially for long-life and high-reliability products. Parameter estimation of three-parameter Weibull distribution has always been the focus of attention. This paper proposed an iterative method based on least squares to estimate the parameters of the three-parameter Weibull distribution. The initial location parameter was set to 0, the initial shape parameter and scale parameter were obtained by using least squares, and the new location parameter was obtained by substituting them into the unbiased estimation of the location parameter, and multiple iterations were performed. In this process, the shape parameters and scale parameters gradually become smaller and the location parameters gradually become larger, and finally the stable shape parameters, scale parameters and location parameters were obtained, which are the final parameter estimates, and the lifetime of 99% reliability was calculated. The method was proved to be convergent by Monte Carlo simulation. Compared with the correlation coefficient method by two metrics including Bias and Root Mean Square Error (RMSE) for different Weibull models with different small and medium sample sizes (10, 15, 20, 25 and 30), the three estimated parameters and the 99% reliability of the lifetime of the proposed method are more accurate. The analysis of two examples shows that the method is feasible and valid. Compared with the correlation coefficient method, the estimation results are more conservative and more suitable for engineering application.

The traditional analysis method is not practical in the power prediction and parameter optimization analysis of low temperature differential Stirling engine. In order to predict the output power of low temperature differential Stirling engine quickly, this paper studied the application of the second order Simple model in the thermodynamic cycle analysis of low temperature differential Stirling engine. It described the simplified structural model of low temperature differential Stirling engine and the temperature characteristics of internal working medium. Based on Simple model, this study derived the actual heat transfer equation of non-ideal heat exchanger in low-temperature differential Stirling engine, and analyzed the heat return loss, pumping loss and actual heat transfer of heat exchanger. The variation of the temperature, pressure and energy of the working medium in the low-temperature differential Stirling engine system with the crank Angle was illustrated by examples, and the theoretical output power of the low-temperature differential Stirling engine was analyzed. The actual output power of low temperature differential Stirling engine at different heating temperatures was compared with the calculated power of Simple model. The comparison results show that the error between the output power calculated by the Simple model and the actual output power is small, indicating that the Simple model is in good agreement with the actual cycle of the low-temperature differential Stirling engine. In order to study the influence of regenerator on engine performance of low temperature difference Stirling engine, the paper optimized the structure of regenerator of low temperature difference Stirling engine. The output power of the regenerator after optimization was compared with that before optimization. The comparison results show that after optimizing the regenerator, the actual output power of the low-temperature differential Stirling engine and the calculated power of the Simple model are both increased by 20%. It is shown that optimizing the structure of regenerator is an effective method to improve the performance of low temperature differential Stirling engine.

In order to analyze the interaction between the vibration of vibrating screen and the movement of particles and reveal the influence of vibration parameters on the dynamic characteristics of the system, this paper establishes a model for the coupling of vibration sieve system dynamics and particle dynamics. By using this model, real-time bi-directional coupling simulation of screening process can be realized, and the accuracy of the simulation model is effectively improved. The steady-state particle distribution at each layer of screen is obtained based on the actual screening yield which is calculated in terms of screen test. As compared with the uncoupled model, the model considering the coupling of vibrating screen and particle dynamics can obtain more accurate force analysis, and enhance the material throwing of the sieve body, avoiding the particle accumulation of each layer in the traditional simulation, which is more consistent with the actual screening situation. Meanwhile, the coupling analysis also indicates that the maximum impact force of materials does not decrease as the number of screen layers decreases. Finally, the variation law of material impact force with the main parameters of vibrating screen is studied. The results indicate that the impact force of the particles not only varies as the proportion of screen surface area, but also relies on the residence time of the particles on the screen; the impact force of the particles decreases with the increase of the inclination angle of screen surface. When the vibration direction angle is within 57°to 90°, the magnitude of the impact force shows an inverted V-shape. The results provide a theoretical guidance for the dynamic optimization design and noise reduction of vibration screen.

Due to the long-term operation of planetary gearboxes in strong noise environments and changing working conditions, the collected vibration signals exhibit weak fault characteristics and variable signal patterns, making them difficult to identify. Intelligent fault diagnosis of planetary gearboxes under these conditions remains a challenging task. In order to achieve high diagnostic accuracy and strong model generalization performance, a fault diagnosis method using a graph neural network with a multi-scale time-spatial information fusion mechanism is proposed. The method first uses convolution kernels of different scales to extract features from the original vibration signal, reducing the masking effect of strong noise signals on valuable information and enhancing its feature expression ability. A channel attention mechanism is then constructed to adaptively assign different weights among different channels to features of different scales, enhancing features in segments of information containing crucial fault characteristics. Finally, the multi-scale features of the convolution module output are used to construct graph data with spatial structure information for graph convolution learning. This approach allows for the full utilization and deep fusion of multi-dimensional time domain information and spatial correlation information, effectively improving the accuracy of diagnosis and the generalization performance of the model. The proposed method was verified using a fault dataset of wind power equipment with planetary gearbox structure. The average diagnosis accuracy of the proposed method was found to reach 98.85% and 91.29% under cross-load and cross-speed conditions, respectively. These results are superior to other intelligent diagnosis methods, including deep convolutional neural networks with wide first-layer kernels (WDCNN), long short-term memory network (LSTM), residual network (ResNet), and multi-scale convolution neural network (MSCNN). Therefore, the strong generalization performance and superiority of the proposed method were confirmed.

Multi-modal data fusion of LiDAR (Laser Imaging, Detection, and Ranging) and binocular camera is important in the research on 3D reconstruction. The two sensors have their own advantages and disadvantages, and they can complement each other through data fusion to obtain better reconstruction results. In order to achieve data fusion, firstly it is necessary to unify the two data into the same coordinate system. The calibration results of the external parameters between the LiDAR and the camera are very important to 3D reconstruction. Due to sparse LiDAR point cloud and its positioning error, it is a challenge to extract feature points accurately for constructing accurate point correspondences when calibrating extrinsic parameters between LiDAR and stereo camera. In addition, most calibration methods ignore that LiDAR works on spherical coordinate system and directly use the Cartesian coordinate measurement results for calibration, which introduces anisotropic coordinates error and reduces the calibration accuracy. This paper proposed a calibration method by minimizing isotropic spherical coordinate error. Firstly, a novel calibration object using centroid feature points was proposed to improve the extraction accuracy of feature points. Secondly, the anisotropic LiDAR Cartesian coordinate error were convert into the isotropic spherical coordinate error, and the extrinsic parameters were solved through directly minimizing the spherical coordinate error. The experiments show that the proposed method has advantages over the anisotropic weighting method. The method ensures that the solution is globally optimal and the number of calibration samples required is greatly reduced on the premise of sacrificing some accuracy. With the optimal calibration error of 2.75 mm, the amount of calibration data can be reduced by about 54.5% by sacrificing 3.6% accuracy using the proposed method.

In order to overcome the difficulty in accurate prediction of the dynamic wear of tooth profile of precision RV reducer for industrial robot, this paper took BX-40E reducer as an example, obtained the wear coefficients under different position conditions through equivalent experiment based on the generalized Archard wear formula, with the consideration of the influence of different position conditions after the wear evolution in the wear prediction process. According to the deformation coordination theory and Langkali-Nikraves contact force model, the load distribution and contact pressure between teeth were determined. Considering the time-varying tooth profile wear and meshing force excitation, the numerical calculation model of dynamic tooth profile wear of transmission system was established by analytical modeling method. As compared with the tooth profile wear curve with the constant wear coefficient, both the wear value and the tooth surface distribution are significantly different, and the overall difference increases with the increase of wear times. The accuracy and necessity of quantifying the tooth surface wear with the wear coefficient taking into account the difference of contact position conditions were obtained. The wear depth curve of cycloid gear and needle teeth presents an asymmetric and irregular inverted “W” shape along the tooth profile. Due to the wear, the teeth near the tooth root and tooth top first fall off and then mesh, resulting in impact and micro protrusion peak. There is almost no wear at the concave convex transition position of cycloid tooth profile. With the increase of wear times, the wear peak area becomes narrower, and the non-uniform increase of wear rate slows down. There is a functional mapping relationship between the meshing force and the pressure angle. The results of the study provide a theoretical basis for improving the wear reduction and vibration reduction of cycloidal needle gear.

The microstructure variations of machined surface determine the performance of machined components. Accurately predicting the microstructure evolution of machined surface and thus enhancing surface hardness of machined components is an effective way to improve the service performance and realize the controllable machining of components. Machining is one the fundamental manufacturing techniques of TC4 components and the severe plastic deformation during machining process induces the complex evolutions of microstructure for TC4 machined surface. For the grain refinement phenomenon during TC4 cutting, this paper studied the multi-scale distribution characteristics of microstructure, evolution mechanisms of grain refinement and its effect on the material hardness under different cutting speeds (100 ~ 500 m/min). The results show that grain refinement degree at meso-scale (10^-6 ~ 10^-5 m) increases first and then decreases with the increasing of cutting speed. At cutting speed of 300 m/min, grain refinement degree of machined surface is 69.7% and the grains in the shear bands of chips are refined to 2 ~ 6 μm. Complex dislocation patterns and nano twining are the features of microstructure at micro-scale (10^-8 ~ 10^-7 m). The deformation twinning type is mainly characterized as {10\bar{\mathrm{1}}1} compression twinning and it is generated at higher cutting speed (> 200 m/min). Grain refinement during machining of TC4 was predicted based on the modified Z-H grain refinement model and nano twining volume fraction prediction model. The hardening effect of grain refinement was also considered in the prediction model. The evolution of grain sizes and the work hardening was predicted. The relation between grain refinement and material hardness was established, and the hardness of TC4 machined surface was predicted with directional controlling the grain refinement degree and formation of nano twinning. And the hardening mechanism in micro-scale of TC4 machined surface was revealed.

In-mold hot-pressing is a common drying method in the production of pulp moulded tableware. The wet paper mold embryo obtained after moulding is heated under the condition of extrusion and vacuum. Heating plate is the heat source of hot-pressing machine, and the temperature uniformity of its working surface affects the drying quality of products. Aiming at the temperature non-uniformity of the heating plate in the process of pulp molding hot pressing, this paper proposed an optimization method which combined simulation and orthogonal experiment. Firstly, the working process of heating plate was analyzed and the heat transfer model of heating plate was established. Then, based on Fluent, the temperature field was simulated numerically. According to the results of temperature field distribution, the high-temperature area and low-temperature area in the oil circuit structure were staggered as far as possible, and four new labyrinth oil circuit structures were designed. Finally, an orthogonal test with 4 factors and 4 levels was designed based on the structure of oil circuit, the plane height of oil circuit, the thickness of heating plate and the section diameter of oil circuit, and the range analysis and variance analysis were carried out. The results show that during the actual drying process, the maximum temperature and minimum temperature of the working surface are 224.47 ℃ and 209.92 ℃, respectively, and the temperature range is as high as 14.55 ℃ and the temperature standard deviation is 3.01 ℃. The thickness of heating plate and the diameter of oil passage have a significant influence on the temperature difference, and the structure of oil passage has a significant influence on the temperature standard deviation. Based on the above analysis, the structure of heating plate was improved. Compared with the original design, the temperature range of the working surface of heating plate has been reduced to 7.27 ℃ and the temperature standard deviation has been reduced to 1.09 ℃, which ensures the uniformity of temperature of heating plate and improves the quality of pulp molded products.

With the development of modern technology, the automotive and aerospace fields are pursuing the lightweight of materials, and the high strength and high toughness of materials is the basis of lightweight. 7000 series aluminum alloys (Al-Zn-Mg-Cu series aluminum alloys) have the advantages of high strength, high hardness, good corrosion resistance, et al. Among all aluminum alloys, 7055 aluminum alloy has the highest strength. The common preparation method of 7055 aluminum alloy is spray forming process. Stable growth of the aluminum ingot during deposition is the basis for the preparation of large-size ingots with uniform deposition quality by the spray forming process. Due to the variation of numerous process parameters during the jet forming process, the existing theoretical model is difficult to meet the requirements of quality control in the actual production process. This paper built a GA-BP neural network prediction model for the diameter and a model for regulating the growth rate of the ingot billet based on the correlation analysis between the historical data of the injection molding process and the diameter of the deposited surface of the ingot billet, by combining BP neural network and genetic algorithm. Based on the real-time fluctuation of process parameters, the diameter variation was calculated and used as an input layer into a trained velocity regulation neural network model to optimally regulate the lifting speed of the deposition substrate, resulting in a uniform and stable deposition growth profile of the ingotst. Finally, this method was used to regulate the growth rate of ingots. The results show that the deviation of large-size ingot diameter is within 5%, which verifies the feasibility of growth rate regulation.

Sliding bearings in mixed lubrication state are prone to wear due to deformation or misalignment under low-speed conditions. In order to analyze the influence of journal misalignment and wear on the mixed lubrication characteristics of sliding bearings, this study established a coupled model of average flow equation, G-T contact equation and Archard wear equation considering journal misalignment and elastic deformation. The bearing characteristic parameters and time-varying wear parameters under mixed lubrication were calculated by finite difference method and over-relaxation iteration method. The lubrication performance of bearings before and after journal misalignment or wear was compared, and the effect of roughness and boundary friction coefficient on various performance parameters was analyzed. A friction and wear test rig was built to test the lubrication characteristics of bearings in misaligned state, which verified the correctness of the theoretical model. The theoretical analysis and experimental results show that, when heavy load and large eccentricity occur, the bearing will change to mixed lubrication state. The greater the journal tilt, the more likely the bearing to have mixed lubrication. When the bearing is misaligned, the peak pressure and the shape of contact area will change, resulting in a difference in wear and the distribution of wear depth is tilted either axially or circumferential. The wear reduces the fluid hydrodynamic effect and decreases the film thickness ratio, leading to a decrease of about 20% in the hydrodynamic pressure peak, a decrease of about 90% in the contact pressure peak, and a maximum decrease of about 19.71% in bearing capacity. By comparing the bearing morphologies before and after wear, it finds that the journal tilts leads to that the wear concentrates on the end where the clearance reduces. This research provides theoretical basis for sliding bearing design in practical engineering.

Template matching is a common key technology in the field of machine vision. Currently, edge feature-based template matching methods are facing challenges such as time-consuming searching and low matching accuracy in a complex environment. In order to ensure the robustness while improving the real-time performance, this paper proposed a real-time edge feature-based template matching method. Firstly, in the stage of template creation, a new edge sparse method was proposed, and it can screen out the strong invariant edge points from the template image. It reduces the redundancy of template information while retaining the key template features to ensure the stability and improve the computing efficiency. Secondly, in the stage of pyramid search-based image-matching, a top-level pre-screening method was proposed. Normalized Manhattan distance was used as a constraint to exclude incorrect target poses from the top search results to speed up the search in subsequent layers. Five datasets with different working conditions were constructed, and the proposed template matching method was compared and applied to the fast visual dispensing process for free plane pose. The experimental results show that the proposed matching method can significantly improve the matching speed while ensuring high accuracy. And it can overcome interference factors such as illumination change, rotation, defects, multiple targets, and occlusion, enabling practical applications that require both high robustness and real-time performance.

Micro-Electro-Mechanical System (MEMS), also known as microsystem or micro-electro-mechanical system, is a high-tech micro device or system developed on the basis of microelectronic technology. MEMS integrates photolithography, corrosion, LIGA, silicon and non-silicon surface micromachining, precision machining and other technologies, and its size is on the micron scale. The microsensors produced by micromachining technology are widely used in engineering practice because of their simple structure, high sensitivity and stable operation. The microsensor usually uses electrostatic excitation and capacitance detection to detect the signal, that is, the displacement change of the resonator during vibration leads to the change of the distance between two electrodes, so as to change the capacitance between electrodes, thus the detected capacitance change frequency is the frequency of resonantor vibration. A cross microresonator was proposed to solve the problem of weak capacitance signal and low detection accuracy of microsensor. To study the multi-field coupling effect of microresonator, the multi-field coupling nonlinear dynamics equation of harmonic oscillator was established considering Van der Waals force and electric field force. The dynamic displacement of nonlinear vibration was obtained by using Linz Ted-Poincare method, and the influence of multiple physical field parameters on mean vibration displacement and capacitance variation of the harmonic oscillator was analyzed. The cross microresonator was fabricated by micro-nano machining, and the capacitance variation resulted from resonant frequency and vibration displacement was measured by electrostatic excitation-capacitance detection method. The results show that the cross microsensor increases the plate area, thereby aggrandizing the capacitance variation, and thus the signal intensity becomes stronger. When the plate area increases by 75%, the capacitance change is 4.2 times of the original, and the signal intensity increases by 5.0 times, so it is more convenient for capacitance detection.

Aiming at the problems of complex trajectory learning and lack of coordination constraint analysis when a dual-robot collaborative system performs humanoid tasks with strong coordination constraints, this paper proposed a dual-robot cooperative handling trajectory learning and generalization method based on dynamic movement primitives (DMPs). Firstly, starting from the dual-robot cooperative handling task, the coordination constraints of the dual-robot were analyzed, and the motion constraint model of the dual-robot was established. Then, the robot motion trajectory was decoupled into position trajectory and orientation trajectory, and the quaternion was used to realize the non-singular description of the orientation trajectory. And the dynamic movement primitives model of position trajectory and orientation trajectory were established respectively. They were combined with the dual robot motion constraint model and DMPs model, and the dual-robot movement trajectory was obtained, taking into account their respective task requirements and relative pose constraints. Finally, the simulation and experiments of the cooperative handling trajectory of the two robots were carried out. The results show that: using the learning and generalization method of the dual-robot cooperative handling trajectory, when the starting and ending states are changed, the position errors of start point and end point of the dual-robot cooperative handling with the fixed orientation are 0.029 2 mm and 0.112 7 mm respectively; the position errors of start point and end point of variable orientation coordinated handling are 0.032 3 mm and 0.113 1 mm respectively; and the quaternion orientation errors of the end point are 0.001 4, 0.002 7, 0.001 8, 0.003 0, indicating that the cooperative handling trajectory learning and generalization method has high motion control accuracy; even if the task parameters of the starting and ending are changed, the generalized trajectory can still ensure the accessibility of the target, which verified the scientificity and effectiveness of the proposed dual-robot coordination motion trajectory control strategy. The method proposed in this paper can effectively learn the human handling process and can accurately generalize new motion trajectories. It realizes the dual-robot coordinated motion and has important engineering application value.

To address the challenging task of inspecting hard-to-reach areas, such as high piers and the bottom of bridges, the paper developed a wall-climbing robot for bridge disease detection based on negative pressure adsorption. For the robot’s own adsorption stability, this paper established and derived a formula for calculating the adsorption force index under conditions of anti-slip and anti-overturning, based on which the minimum adsorption force required by the robot to achieve stable wall adsorption at all angles was determined. The results show that to ensure the reliable operation of the robot, the adsorption module needs to provide 53.0 N adsorption force. The preliminary design of the centrifugal impeller was formulated based on empirical principles, followed by fluid mechanics simulation and response surface optimization of the impeller basin using Fluent. An evaluation function, comprising adsorption force and torque, was established to optimize the impeller design parameters to maximize the comprehensive evaluation function value of the adsorption module. Compared to the initial design scheme, the optimized design achieved a 3.4% increase in the evaluation function value while maintaining stability. Taking into consideration the aerodynamic performance of the chamber along with the topology optimization results, topology optimization of the negative pressure chamber was performed. The structure and arrangement of reinforcing ribs inside the chamber were obtained, with the reinforcing ribs connected to the wheel support arm designed in “八”-shaped and linear hollow structures. This optimization reduced the maximum vertical displacement of the negative pressure chamber to 18.5% of the original model, with a minimal increase in mass of 16.9%. It shows that the precise layout effect of the strengthening rib is obvious, and the vertical deformation is successfully controlled within a reasonable range. Finally, a prototype was constructed using UTR6180 photosensitive resin and 3D printing technology, with approximate dimensions of 300 mm×280 mm×15 mm and a mass of approximately 1.15 kg. The performance test of the prototype was conducted under various working conditions, demonstrating that the wall-climbing robot can stably adsorb and move on various bridge walls without slipping or drifting.

Aluminum alloy materials are widely used in the fields such as aerospace, automotive manufacturing and shipbuilding. However, the load stress and residual stress during the manufacturing and equipment process directly affect the mechanical properties and fatigue life of aluminum alloy components. In this paper, for the purpose of evaluating the internal stress of aluminum alloy and on the basis of acoustic elasticity principle, the phased array longitudinal-wave detection technology was studied, and an internal stress detection method of aluminum alloy was set up based on the time difference during longitudinal wave propagation. Then, an experimental system for phased array longitudinal-wave ultrasonic stress detection was set up, and calibration experiments were carried out to reveal the linear relationship between the internal stress of aluminum alloy and the time difference during longitudinal wave propagation, with the correlation equations being also formulated. The results show that, within the tensile stress range of 0~286 MPa, the absolute calibration errors of 5 mm and 3 mm aluminum alloy plates are respectively less than 2.85 MPa and 10.82 MPa, the corresponding relative errors are respectively not more than 2.36% and 13.93%, and the maximum relative errors of both specifications occur within the stress range of less than 28.58 MPa, meaning that it is necessary to improve the resolution and accuracy of ultrasonic measurement in small stress detection. The phased array longitudinal-wave system was then used to detect the stress 5 mm aluminum alloy plate specimens, and an average stress error of (1.174±4.567) MPa, an absolute error of less than 9.42 MPa as well as an estimated initial residual stress of 3.329 MPa was obtained. The experimental results show that the proposed phased array longitudinal-wave ultrasonic method is effective in detecting the average stress of 5 mm aluminum alloy plate; that the method based on the time difference during longitudinal wave propagation can be used to detect the stress type, stress size and residual stress; and that the proposed method is effective in improving the detection accuracy and efficiency of the internal stress detection of aluminum alloy.

High water-based hydraulic motors can be used in fields such as coal mining, food, and underwater operation due to their medium-friendly nature. However, currently high water-based motors still use hydraulic oil motor structure, only replacing the medium with high-water-based emulsion. Traditional shaft and disk flow structures will suffer severe leakage and rusting phenomena under low-speed, high-pressure, and high water-based conditions. Additionally, the current valve flow structure problem is that one plunger needs to be equipped with two check valves, which causes the motor to have a larger volume, and the flow valves must be accurately matched. Otherwise, there will be channeling and fluid entrapment phenomena. In view of the above problems, a shuttle valve flow structure was proposed to control the motor flow distribution, which consists of a shuttle valve and a cam. The cam drives the plunger’s liquid intaking and discharging process. Firstly, the flow valve was structurally designed and theoretically analyzed, revealing its flow distribution principle. Secondly, the dynamic response characteristics of its parameters were analyzed in AMESim. The cam driven by the sine acceleration function curve was selected to control the valve core, and the flow-through hole with a diameter of 0.6 mm, with small pressure and flow fluctuations, was used. Additionally, the motor’s torque fluctuation was 7.39%, verifying the shuttle valve’s good flow distribution performance. Fluent simulation was used to optimize the valve’s internal flow field and select the notch structure with small pressure drop and uniform velocity distribution. Based on this, prototype preparation and experimental analysis were carried out. Under 16 MPa working condition, the plunger chamber can quickly build pressure, the pressure fluctuation at the inlet of the flow valve is 12.5%, and the leakage is 2 drops/min. It can be seen that after the shuttle valve is applied to the high water-based hydraulic motor, stable flow distribution can be achieved.