In the absence of sufficient escalator motor bearing failure data, to address the issue of unstable bearing fault characteristics during frequent load and speed variations in escalator operation, this paper proposed a transfer diagnosis method for escalator motor bearings using Stockwell (S) transformation combined with subdomain adaptation. Firstly, for the fault characteristics of escalator motor bearings, a time-frequency image of vibration signals was generated using the S transform combined with bilinear interpolation. This time-frequency image effectively reflects bearing fault features and is subsequently aligned with the requirements of the feature extraction network. Secondly, local maximum mean discrepancy (LMMD) was introduced at the output end of the feature extraction network layer based on the deep residual neural network ResNet-50. It incorporates the confidence of bearing fault sample categories as weights in the mapped maximum mean discrepancy (MMD), aligning the dis-tributions of subdomains belonging to the same category, thereby expanding the scope of transfer learning. Next, the network was constructed to minimize both LMMD and cross-entropy loss functions, and network training was performed using mini-batch gradient descent. Consequently, by refining the feature differences between different fault categories, fault subdomain self-adaptation was achieved, overcoming the problem of low transfer diagnosis accuracy. Finally, based on two publicly available bearing fault datasets and a limited amount of escalator motor bearing fault data, the S-transformed time-frequency dataset was constructed, and transfer diagnosis experiments were conducted. The results demonstrate that the proposed method achieves an average accuracy of 99.1% and 95.49% for transfer diagnosis in two different source-to-target domain scenarios of escalator bearings, outper-forming five commonly used diagnostic methods in terms of recognition accuracy and robustness.

Traffic signs on the road contain a large amount of semantic information about traffic rules, and rapid and accurate access to this information helps to achieve higher levels of assisted driving functions, thus improving vehicle’s safety performance. In view of the traffic signs are susceptible to external factors and the problems of high similarity between categories and small size, this paper made targeted improvements in data augmentation, feature extraction and feature enhancement based on YOLOv5s model. In the data augmentation part, color space transformation and geometric transformation matrix were used to simulate the possible color changes and shape changes of traffic signs in actual scenes, and the Mosaic algorithm and Copy-paste algorithm were used to improve the number of tiny traffic signs in the training set and the richness of the background. In the feature extraction part, a feature extraction module based on channel attention calibration was constructed to improve the model’s ability to discriminate similar features. In the feature enhancement part, the number of prediction branches and downsampling multiplier were optimized by fusing shallow features and deep features with a dual-path enhancement structure, so as to increase the detection accuracy of tiny traffic signs. In addition, the K-means++ algorithm was used to cluster the prior bounding box templates and construct the loss function based on the CIoU metric, thus reducing the difficulty of the prior bounding box regression. Experiments on the TT100K and CCTSDB dataset test show that the mAP@0.5 of the proposed model is 88.8% and 83.5% respectively, and the speed of the model is 120.5 f/s and 114.7 f/s respectively. Compared with the existing traffic sign detection models, the proposed model reaches the advanced level in both accuracy and speed. Comparison experiments for data augmentation algorithms, prediction branches, and channel attention module positions further demonstrate the effectiveness of the proposed specific optimization methods.

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.

To study the distribution of soot particles and the efficiency of fume extraction inside the SLM (selective laser melting) forming bin, this research analyzed the flow law of shielding gas and soot particles in the forming bin with Fluent discrete phase model (DPM) based on the established model of the large-format porous wind wall forming bin. Then multi-objective genetic algorithm (MOGA) was used to optimize the structure of the porous wind wall forming bin. The length of air inlet P₁, the radius of wind wall hole P₂, the length of conical protection plate P₃ and the shaft length of wind wall hole P₄ were taken as optimization variables, and the average flow velocity of shielding gas in the middle section of the forming bin, the concentration difference of smoke particles at the inlet and outlet of the forming bin and the maximum concentration of smoke particles in the entire forming bin were taken as optimization objectives. The response surfaces and sensitivity analysis results of each optimization variable and optimization target were obtained, and the optimization target before and after optimization was compared. The results show that for the four optimized variables, the order of influence on the flow velocity of the shielding gas in the middle section of the forming bin is P₂>P₄>P₃>P₁; the order of influence on the concentration difference of inlet and outlet particles is P₂>P₄>P₁>P₃; the order of influence on the maximum particle concentration in the porous air wall forming bin is P₂>P₁>P₃>P₄; the pore size of the porous wind wall plays a key role in the flow of dust particles in the forming bin. Through multi-objective genetic algorithm, the optimized length of gas inlet is 358 mm, the radius of wind wall hole is 20 mm, the length of conical protection plate is 589 mm, and the shaft length of wind wall hole is 6 mm. Compared with that before the optimization of the structure of the forming bin, the flow velocity of the shielding gas in the middle section of the forming bin after the optimization increases by 11.3%; the concentration difference between the inlet and outlet particles decreases by 16.8%; the maximum particle concentration in space decreases by 23.9%; the trend of outward diffusion of soot particles decreases, and the flow velocity of the shielding gas passing 30 mm above the forming table increases by 21%, indicating that the shielding gas can carry the soot particles out of the forming bin more efficiently.

For the individual difference of faces which are the operation object of massage robot, the dynamic motion primitives (DMPs) model was used to generalize the posture trajectory and force trajectory. Firstly, in order to improve the learning accuracy of DMPs, the study proposed an optimized teaching strategy. Based on the Mediapipe feature points in the massage area, the similarity between the operating objects was calculated to optimize the learning objects. Secondly, Gaussian mixture regression (GMR) was introduced, and the algorithm integrated multiple massage information to enhance learning ability. Finally, a back-propagation neural network (BPNN) model was constructed to fit the forced term of DMPs algorithm, which fundamentally changes the limitations of the original model. The experiment shows that the average errors of position and attitude of BPNN-DMPs model are reduced by 44.1% and 54.5%, 44.1% and 54.5%, 29.7% and 46.4% respectively, compared with DMPs, MDMPs and SADMPs algorithms without increasing the running time. Gaussian mixture regression can integrate multiple trajectory patterns and the implementation effect of the optimized teaching strategy is significant. Compared with the non-optimized object, the average errors of the position and posture of the face experiment are reduced by 52.3% and 70.2%, and the standard deviation is reduced by 46.3% and 71.1%. The average errors of position, posture and force in the back experiment decrease by 27.7%, 66.7% and 24.1%, and the standard deviation decreases by 25.7%, 54.4% and 44.1%.

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.

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.

In order to improve the thermal-hydraulic performance of louvered fin and flat tube heat exchangers (LFHE), by arranging common flow down vortex generator (CFDVG) on LFHE’s flat tubes, this paper proposes louvered fin-common flow down vortex generator (LF-CFDVG). Then, considering the use of active grille air shutter (AGS) may change the inflow direction of air at the core of LFHE, the influence of inflow direction on the thermal-hydraulic performance of LF-CFDVG is further studied at the air velocity of 3 m/s. The results show that the pressure drop Δp of LF-CFDVG is always greater than that of Baseline (namely the LFHE without CFDVG) due to the impact of minimum free flow area reduction after the appearance of CFDVG, the increase in frictional resistance caused by the air velocity increment and the differential pressure resistance caused by CFDVG. In the process of γ (inflow direction angle) increasing from 0° to 30°, under the influence of air velocity reduction, Δp of both Baseline and LF-CFDVG decreases, so that increasing γ helps to reduce the resistance to air flow. At the same time, under the impact of high-speed and low-temperature main stream transported by the longitudinal vortices on the tube wall between CFDVGs, the convective heat transfer ability of LF-CFDVG is significantly enhanced, as compared with the Baseline. Moreover, in the process of γ increasing from 0° to 30°, the convective heat transfer coefficients of Baseline and LF-CFDVG both reduce due to the decrease of longitudinal vortex strength and scale, so that increasing γ impairs heat exchange ability. It is also found that the comprehensive performance of LF-CFDVG continuously decreases in the process of γ increasing from 0° to 30°. Thus, increasing γ is not conducive to the improvement of comprehensive performance.

In the working process of large-scale construction machinery such as excavators and shield machines, due to the complex working conditions and changeable environment, the hydraulic system power component axial piston pump of large displacement has greater hydraulic impact than the conventional piston pump, and it causes greater flow pulsation and pressure pulsation. In order to reduce the flow and pressure pulsation of axial piston pump of large displacement under excavation, shield and other working conditions, reduce the impact damage of oil medium on downstream components, and provide theoretical guidance for selecting oil medium methods, this paper built a mathematical model of oil dynamic viscosity, density and bulk elastic modulus based on the influence of temperature field. On this basis, a mathematical model of piston pump with solid-liquid-temperature coupling was established. ADAMS and AMESim software were used to complete the joint simulation of 750 mL/r piston pump under the coupling of solid-liquid-temperature, and the variation law of flow pulsation and pressure pulsation of piston pump under different temperature was obtained. The influence of oil temperature on the pressure pulsation of the piston pump was investigated by the whole pump test, and the correctness of the solid-liquid-temperature coupling simulation model of the 750 mL/r piston pump was verified. According to the three characteristics of oil, the orthogonal test method and the single factor analysis method were used to consider their influence degree and influence law on the flow pulsation rate of the piston pump, respectively. The results show that the outlet flow pulsation and pressure pulsation of the piston pump increase with the increase of temperature. Under the set condition, the influence of oil bulk modulus on flow pulsation rate is 97.19%, the density is 2.03%, and the dynamic viscosity is 0.78%. In order to reduce the influence of oil characteristics on the pulsation rate of piston pump outlet, hydraulic oil with larger bulk modulus and smaller density should be selected.

To broaden the effective frequency band of piezoelectric energy harvesters, this paper designed a nonlinear magnetic piezoelectric energy harvester with adjustable frequency and fast switching between monostable and bi-stable mode. This harvester is composed by adding a pair of movable magnets on a piezoelectric cantilever beam with an end magnet. Firstly, the distributed parameter dynamics of the harvester was analyzed. The dynamics equation of the system was derived based on Euler-Bernoulli theory, and the expressions of kinetic energy, potential energy and electric energy of the system were analyzed according to Lagrange function. The magnetic force expression of the system was obtained according to the magnetic dipole model. The first-order reduced model of the system was obtained by Galerkin discrete method and Taylor expansion, and the analytical expression of the equations was derived via the harmonic balance method. Then, the monostable and bi-stable characteristics of the harvester were studied by simulation software, and the effects of magnet spacing, damping, load resistance and other parameters on the output voltage and output power of the system were analyzed and verified by the subsequent experiment. According to the experimental result, the nonlinear magnetic force brought by the movable magnet can significantly increase the output voltage and output power of the harvester; the resonant frequency of the energy harvester can be changed by adjusting the magnet spacing, which better widens the working frequency band; the energy harvesting system can rapidly switch between monostable mode and bi-stable mode by adjusting the magnet spacing, and use the monostable mode and and the bi-stable mode to harvest vibration energy in high-frequency environment and the low-frequency environment, respectively.

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.

Hot bar soldering is a method used to connect electronic components. The stability of soldering horn temperature is the decisive factor of soldering quality. Due to the short time of hot bar soldering, the thermal inertia and random noise of thermocouple measurement have a great influence on the temperature control of the process. This paper developed a hot bar soldering power supply with STM32F407 microprocessor as the core, and designed the main circuit and control system of the power supply. By analyzing the delay response and time constant error of the thermocouple, this paper designed a new control method of hot bar soldering based on an Extended Kalman Filter (EKF) state observer, which realizes pulse width modulation and stable control of soldering temperature. It also analyzed the heating and thermal radiation effects of the heater tip, established a temperature model of the heater tip, and developed a simulation model of the hot bar soldering system based on the above main circuit and control scheme to verify the effectiveness of the control methods. A testing platform for the hot bar soldering system was built, and experiments were conducted according to the process parameters set by the simulation model. The simulated temperature waveform was compared and analyzed with the measured waveform. The results show that the trend of the simulated and tested temperature waveforms follows the same pattern. Compared to only using PID control, the control method based on EKF achieves a shorter adjustment time, reduces the impact of effective noise on the hot bar soldering system, and improves temperature control stability. The simulation model of the hot bar soldering system provides a reference model for the design of the hot bar soldering power supply. Finally, the hot bar soldering tests of FPC and PCB board, coaxial cable and LED circuit board were carried out to achieve the reliable connection of the components.

The position control of robot manipulators has been recognized as the most fundamental and simplest objective in the robotic control field. For the high-precision position control problem of the multi-axis robot system, this study proposed a simple output feedback nonlinear PD plus gravity compensation (PD+) synchronization position controller combining with the cross-coupling techniques. The global finite-time stability of closed-loop systems was strictly demonstrated by applying Lyapunov stability theory and geometric homogeneity techniques. Compared with the asymptotic stable full-states feedback control schemes, the presented controller ensures the finite-time stability of the robot manipulators with position measurements only; compared with the output feedback asymptotic stable controllers, the proposed controller ensures the finite-time convergence of robot’s states; compared with the output feedback controllers without synchronization term, the proper introduction of nonlinear synchronization control items enables the proposed controller to take into account the synchronous and coordinated motion between the axes on the premise of ensuring the high-precision position control of the multi-axis robot system. The proposed controller has the advantages of simple structure, easy implementation, faster response speed and better overall system performance, which meets the high precision requirements of actual production for the robot system. Numerical simulation results demonstrate the effectiveness of the proposed control algorithm and the expected performance of the system. The proposed control method not only ensures the global output feedback finite-time stable synchronization control of multi-axes robot systems, but also provides an effective alternative approach for the output feedback synchronization position stabilization of a large class of nonlinear second-order systems.

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.

As China’s nuclear industry enters the third 30 years of development, the maintenance need for nuclear equipment is becoming increasingly urgent. The internal structure of the nuclear steam generator is complex, and the key structure, namely the heat transfer tube, has the restrictions like small diameter, long pipeline and difficult disassembly and installation, which make traditional repair methods extremely difficult in implementation. In order to solve the problem of corrosion damage of small-diameter nuclear heat transfer tube due to long-term high temperature and high pressure condition, an all-position automatic TIG welding gun for liner repair was designed in this paper, and the gun reliability verification and welding repair test were carried out. Firstly, the overall structure design of the welding gun was presented, and the advantages of the designed welding gun comparing with the traditional tungsten electrode TIG welding gun were described. Next, the design and verification of the welding gun transmission system with small size and high space utilization were completed. Then, the stiffness of the key part of the welding gun, namely the conductive shaft, was modelled, analyzed and calculated, and the reliability of the theoretical model was verified by finite element solution. On this basis, an optimization method of the welding gun structure was proposed. Moreover, the field test of deflection and the welding test were carried out, finding that the stiffness of the conductive shaft satisfies the field welding conditions. Finally, linear regression equations and deflection correction formulas were used to quantitatively predict the deflection of the welding gun, and the results verify the rationality of the designed structure. Welding tests results show that the rotation speed of the transmission system is stable and controllable in working condition of welding gun, and the welding seam is well formed. The developed liner welding gun can satisfy the requirements of liner welding repair of the stainless-steel heat transfer tube inside the nuclear steam generator.

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.

In order to explore the application of metal nanoparticles to the repair of microcircuits with multi-conducting elements and to analyze the motion trends during the dielectric serial assembly of nanoparticles, the dielectrophoretic serial assembly behavior of nanoparticles in a non-uniform electric field is investigated based on a multi-gap nanoelectrode system. In the investigation, first, the particle dielectrophoretic assembly experiments of the conductive island microelectrode system were conducted, finding that the molten nanoparticle wires obtained from the assembly could enhance the circuit conductivity. Then, a comparative experiment of dielectrophoretic serial assembly was conducted for the double-gap and the multi-gap serial nanoelectrode systems, finding that, with the increase of the number of conducting islands in the system, a body assembly phenomenon occurs in all nano-gaps, which helps realize the connection of conducting elements within the multi-gap serial nanoelectrode system. Finally, a simulation analysis was carried out not only for the electric field distribution but also for the dielectrophoretic force, alternating current electrothermal flow and their combined force during the dielectrophoretic assembly of nanoparticles. The results show that the average values of the dielectrophoretic force and the alternating current electrothermal flow inside the gap are both higher than those outside the gap at a frequency of 150 kHz; and that nanofluid pumping occurs in any gap of the multi-gap serial nanoelectrode system and the nanofluid pumping is not affected by the number of gaps. Moreover, the emergence of nanofluidic pumps indicates that metal nanoparticles in non-uniform electric field have an tendency of bulk and surface assembly during the dielectric serial assembly, and this tendency may directly affect the quality of generated nanoparticle wires.

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.

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.

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.