Motivated by the profound impacts of longitudinal and transverse waves of earthquake, a novel underwater bionic propeller that utilized longitudinal and transverse compound wave patterns was proposed and designed. A kinematic model incorporating the composite waves was theoretically established, alongside the development of a physical prototype and testing platform. The propulsion performances of the propeller were systematically compared and analyzed through CFD simulations as well as prototype tests under varying amplitudes of longitudinal wave superposition. Simulation results show that both the thrust and velocity generated by the undulating fin may be significantly enhanced, with mean thrust increasing by 27.6% and peak thrust exceeding 200%. Experimental results reveal that under a frequency of 2 Hz with a longitudinal-wave amplitude of 20°, the steady-state average velocity achieved by the propeller reaches 0.761 m/s, which is approximately 14.7% greater than that of without longitudinal wave. This paper demonstrates that composite wave bionic fins exhibit superior thrust and velocity performance compared to single sinusoidal wave configurations, thereby offering an innovative propulsion mechanism for advancing high-performance bionic fish robots.

Due to effects of the non-stationary operations, noisy working environment and strong electromagnetic interference for the wind turbines, the fault impulses of rolling bearings were submerged by strong noise, and the weak features were difficult to accurately identify. To solve the above problems, a frequency domain group sparse model with graph regularization constraints was proposed, which might effectively extract fault features of rolling bearings without periodic prior knowledge. Firstly, vibration signals were converted into graph signals to construct the graph regularization constraints, and the structured information was utilized to guide the penalty strength to improve the accuracy of sparse reconstruction. Secondly, the frequency domain group sparse model with graph regularization constraints was constructed, the method was given to determine the shrinkage threshold of the in-group components, and the objective function was simplified with the proximal mapping to optimize the solution. Finally, the parameters of the model were optimized by using the constructed comprehensive index and the moth flame optimization algorithm, and rolling bearing faults were identified by the envelope spectrum analysis of the reconstructed signals in the time domain. Numerical simulation and experimental results demonstrate that the proposed method has good anti-noise performance and may effectively extract weak fault features of rolling bearings under strong noise interference.

Based on the gear friction loaded tooth contact analysis(FLTCA) method, an optimal design method was proposed for the meshing efficiency of face-hobbed hypoid gears. Firstly, a method was employed to preset the peak-to-peak values of unloaded transmission errors and the positions of the contact zones, facilitating the modification design of the positive and negative tooth surfaces of face-hobbed hypoid gears. Then, building upon the modified tooth surface design, an optimization objective was set to maximize gear meshing efficiency under driving conditions. And the optimization analysis model was established by comprehensively considering factors such as the peak-to-peak values of gear pair loaded transmission errors, distribution of full-load contact pattern on both sides of the tooth surfaces and the maximum contact stresses. To enhance the solution speed of the optimization model, the Kriging surrogate model was employed in conjunction with a multi-island genetic algorithm to address and solve the optimization model. Finally, a case design and test validation were conducted on a commercial drive axle with a face-hobbed hypoid gear pair. The effectivenesses of the optimization method proposed were verified through unloaded contact pattern tests and whole-axle transmission efficiency tests.

In order to fully understand the effects of non-metallic inclusions in gear materials(herein after referred to as "inclusions") on contact damage evolution of wind turbine gears, the configuration force theory, which was used to describe the change of system energy, was introduced to describe the contact damage evolution of gears. Besides, the effects of inclusions depth, size and arrangement on the key contact bearing areas of gears were discussed. The results show that the inclusions affect the stress distribution and damage forms of gear contact damage, resulting in different areas of material spalling and the formation of failure areas. The damage model based on the configurational force theory may accurately describe the contact damage problems of wind turbine gears containing inclusions, which provides a new method for the subsequent failure analysis and fatigue life prediction of the gears.

The accessory gearbox gear transmissions were pivotal components for power transmission of the aero-engine accessories. The configuration and structure design of the accessory gearbox transmissions for aero-engines were complex, involving many parameters of components and system. Traditional design methods based on empirical formulas and scattered software programs could not meet the more efficient design requirements of advanced aviation equipment for high load capacity, long service life, lightweight, high performance transmission systems. Therefore, a “configuration design—component design—system analysis—system optimization” method for aero-engine gear transmission design was established, and the aero-engine accessory gearbox gear transmission design and analysis softwares were developed based on the C++/Python. According to the design requirements, four configuration schemes were generated applying the software, and a coaxial multiple-output non-symmetric power split configuration scheme was selected by comparison. A multi-objective optimization model for the transmission system was developed based on the NSGA-Ⅱ algorithm. With lightweight and high load capacity as the optimization goals, a reduction of 15.81% in gear transmission weight and an improvement of 2.98% in weak gear transmission safety are achieved. This provides theoretical methods and software tools to the research of gear transmissions for new generations of advanced aviation equipment.

In order to study the reliability of automobile structures, the uncertainty of mechanical structure analysis and design was summarized, and the uncertainty of design variables and parameters of automobile structures was analyzed from three aspects: structural parameter uncertainty, material performance parameter uncertainty and load uncertainty. The research progresses of probabilistic reliability analysis and non-probabilistic reliability analysis methods were reviewed and summarized. The applications of reliability analysis method in automobile structures were listed. The mathematical model and algorithm of reliability were sorted out, and the applications of reliability optimization design in lightweight and crashworthiness of automobiles were studied, and the development trends of reliability analysis and optimization design of automobile structures were presented. 

In order to determine the optimal machining parameters of thin-wall complex variable cross-section scroll precision milling process and reveal the effects of milling combined parameters on milling forces, a method was proposed to optimize milling combined parameters and predict milling forces accurately for variable cross-section scrolls based on multiple regression analysis and grey correlation method. Firstly, the geometric model and precision milling model of variable cross-section scrolls were established, and the orthogonal test method was used to analyze the influences of milling parameters on milling force components. Secondly, the prediction model of milling forces was obtained based on multiple regression method, and the significance test analysis was carried out on the regression equation. Then, the close correlation degree among milling parameters and milling forces were explored by grey correlation method. Finally, the milling force prediction model was verified by milling experiments and the optimal milling parameters were obtained. The results show that the maximum, minimum and average relative errors between the predicted milling forces and the experimental force are as 9.02%, 3.31% and 6.16% respectively, which verifies the reliability and accuracy of the model and provides a basis for machining high-precision variable section scroll parts.

Aiming at the problems including the poor output performance of piezoelectric pumps with ball valve and the easy fatigue failure of piezoelectric pumps with umbrella valve, a piezoelectric pump with umbrella floating valve was designed based on the floating working principle of ball valve and the structure feature of umbrella valve that had excellent performance and was not easy to fatigue failure. The prototypes of piezoelectric pump with umbrella floating valve and piezoelectric pump with ball valve were made, and the output performances of two pumps were tested and analyzed. The results show that when the driving voltage is as 300 V（peak-to-peak value）, the maximum rate of flow and maximum pressure difference of piezoelectric pumps with umbrella floating valve are better than those of piezoelectric pumps with ball valve. The double-inlet structure improvement of piezoelectric pumps with umbrella floating valve was carried out, and the output performances of double-inlet and single-inlet piezoelectric pumps with umbrella floating valve were compared by tests. The testing results show that the maximum rate of flow and maximum pressure difference of double-inlet piezoelectric pumps with umbrella floating valve are as 121.66 g/min and 7.71 kPa at a driving voltage of 300 V, which are 17.23% and 32.93% higher than those of single-inlet piezoelectric pumps with umbrella floating valve, respectively.

Aiming at the lunar soil sampling mission in deep cryogenic and low-gravity environment of the moon, a four-degree-of-freedom rope-driven robotic arm with low power consumption, light weight, simple structure, and high reliability was designed and developed. The driving rope was installed at the end of the arm to greatly reduce the power consumption of the robot arm. Torsion springs were installed at joints 2 and 3 to provide more than 20N downforce to the end of the robot arm by spring torque. The fitting gap of different materials were matched to improve the reliability of the robot arm in deep cryogenic environments. A kinematics model of the robot arm was established, and the correctness of the kinematics model was verified. The simulation results of the sampling path show that the robot arm may achieve the sampling task. The semi-closed-loop control strategy was used to conduct position step tests and sinusoidal trajectory tracking tests on each independent joint of the prototype. The test results show that each joint has high response speed, good stability, and trajectory tracking capability. 

 Due to the large size of aircraft parts and the complex distribution of tooling on the assembly site, the laser tracker had poor visibility. During the guided assembly or off-rack inspection, it was necessary to adjust the laser tracker station repeatedly, which seriously affected the measurement stability and efficiency. To solve this problem, a method was proposed for optimizing the station of a laser tracker in aircraft assembly based on digital twins. A measurement constrained model of laser trackers for aircraft assembly was established. The digital twin environment was established based on an accurate simulation of aircraft assembly site elements. Then, the optimization of laser tracker station placement was completed. Finally, a measurement simulation of the laser trackers in the digital twin environment was conducted to verify the feasibility of the genetic algorithm in solving the optimal station of the laser trackers. The results show that the coverage rate of the optimized laser tracker to the measuring points is increased by 110%, and the coincidence rate of the measuring points is increased from 11.7% to 55.5%, both the coverage rate and the coincidence rate of the measuring points of the laser trackers are significantly improved.

Aiming at the existing tool wear prediction methods which caused the problems of poor prediction accuracy due to lack optimization algorithms and inadequate network structure. A tool wear prediction model with the combination of improved SABO(ISABO) and improved BiLSTM(IBiLSTM) network(ISABO-IBiLSTM model) was proposed. Firstly, the acceleration vibration signal and force signal data were preprocessed by truncation method, Hampel filtering method, and improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)-improved wavelet thresholding noise reduction method. Then, the time-domain, frequency-domain, and time-frequency-domain features of the preprocessed signal data were extracted, and the features are screened by Spearman and maximum mutual information correlation coefficient to construct the inputs of the model. Finally, the ISABO algorithm was used to perform parameter optimization of the IBiLSTM network, and based on the obtained optimized parameters, the network was trained to achieve wear prediction. The experimental data analysis results show that the proposed ISABO-IBiLSTM model has a prediction accuracy of 98.49% to 98.83% for tool wear, which is significantly improved compared to BiLSTM, IBiLSTM, and improved convolutional neural networks(ICNN)-BiLSTM models.

Aiming at the flexible job shop scheduling problems under the mode of multi variety and small batch production, an intelligent scheduling method was proposed to minimize the total tardiness of orders based on combination rules and reinforcement learning. Transforming the flexible job shop production scheduling problem into a Markov decision process, according to the characteristics and optimization objectives of the problems, seven features were used to represent the workshop states, and six combination rules were designed as an action library. The problem was solved by using the improved DQN algorithm. Taking the aerospace structural parts machining workshop as a case study, the feasibility and effectiveness of the proposed method in shortening task delivery time are verified by comparing with other common rule-based methods in five different scale calculation examples.

Due to limitations inherent in offline tool wear measurement methods, the availability of wear samples was restricted, and measurement noise was often unavoidable, which complicated the reliability of tool wear monitoring. To address these challenges, a soft sensor modeling and uncertainty analysis approach of tool wear was proposed based on semi-supervised Bayesian Transformer by integrating a semi-supervised Transformer model, Dropout and Monte Carlo(MC) simulation methods. Firstly, a soft sensor model was constructed based on the semi-supervised Transformer network architecture, the network training methods of unsupervised feature extraction and supervised fine-tuning were used to guide the construction of the tool wear soft sensor model under small samples. Then, in order to quantify the impacts of noise on tool wear, a noise network channel was designed for uncertainty analysis. Finally, using approximate Bayesian computation based MC-Dropout, the random uncertainty caused by noise and the cognitive uncertainty resulting from model modeling errors were quantified, aiming at providing more comprehensive information for tool wear assessment. The results show that the proposed soft sensor model and the uncertainty analysis framework may provide a powerful tool for tool health management.

 A multi-source wavelet time-frequency transform convolutional neural network was proposed to address the issues of limited fault samples in rotating machinery bearing fault diagnosis, along with the vulnerability to overfitting and the poor generalization ability of traditional models when dealing with small datasets. Initially, for high-frequency data obtained from a single vibration sensor, a wavelet transform-based time-frequency convolutional layer was formulated to integrate both the real and imaginary components of wavelet coefficients. Here, the real component represented the amplitude information of vibration signals, while the imaginary component depicted phase information. Compared with a convolution layer that only considering real part, this convolutional layer may extract comprehensive time-frequency features. Subsequently, the time-frequency convolutional layer was employed to independently extract features from high-frequency data acquired by multi-sensors on a single device, and these features were then concatenated. Lastly, a dense module utilizing lightweight depth-separable convolution was developed to conduct further feature extraction from the concatenated features, facilitating fault classification. The effectiveness of the model was confirmed through experimentation using Case Western Reserve University rolling bearing dataset, achieving an accuracy of 98.5%.Additionally, the model was deployed for fault diagnosis in rotary kilns, belt conveyors, and grate coolers, demonstrating an average accuracy of 97.19%.

Magnetic fields of certain strength, in the same and opposite directions to the electric field(that is, cis and reverse magnetic fields), were applied for exploring the influences of magnetic field on growth pattern of three-dimensional nickel microstructure in the MLED-AM processes. A comparative study of the average volumetric deposition rate, surface morphology and grain size during MLED-AM without and with two directions of magnetic field actions was carried out by taking micro-nickel pillars with diameter of 50 μm and aspect ratio of 10∶1 as an example. The experimental results show that the action of magnetic fields may increase the average volumetric deposition rate and further refine the grain size of sedimentary bodies compared to no magnetic field action, and the effects of reverse magnetic field are more significant(the average volumetric deposition rate is increased by 25%~50% and the average grain size may reach 31.52 nm). Meanwhile, the action of magnetic fields may improve the surface morphology of micro-nickel pillars prepared by MLED-AM to a certain extent, and the effects of cis magnetic field are better. According to the experimental results and analysis, the reverse magnetic field has a greater impact on the efficiency and quality of MLED-AM.

To preserve the excellent deformation hardening capability of high manganese steel components, the laser wire feeding cladding technology of high manganese steel coatings was proposed. High manganese steel coatings were fabricated on Mn13 steel plate using laser wire feeding technology, with the coating subsequently undergoing deformation hardening treatment via ultrasonic rolling technology. Analyses were conducted on the microstructure, phase composition, and mechanics properties of the coatings before and after ultrasonic rolling, thus revealing the hardening mechanism attributed to ultrasonic rolling. The results demonstrate that the microstructures of the laser wire feeding cladding high manganese steel coatings are the dendritic structures, featuring component segregation of Mn and C elements between the dendrites. No phase changes occur during the ultrasonic rolling processes, and both the hardness and wear resistance of the coatings are markedly enhanced post-ultrasonic rolling. The high manganese steel coatings exhibit dendritic segregation of C and Mn,dislocations, and twins, which significantly impeded the movement of dislocations during ultrasonic rolling, resulting in a higher density of dislocations. Owing to the twinning induced plastic deformation effect in high manganese steels, a substantial number of deformation twins are formed internally post-ultrasonic rolling, interacting between each other to further enhance the deformation hardening capacity of the high manganese steel coatings. Laser wire feeding cladding provides a technical foundation for high-performance surface repair of large high manganese steel components.

To study the effects of hard particle parameters on sanding jetting behavior of train adhesion enhancing, the visual detection technology was used to conduct jetting behavior tests with different particle sizes, shapes and densities. The influence weights of various factors on jetting velocity and diffusion angle were analyzed, and the movement characteristics and influencing mechanism of particles inside sanding device were explored. The results show that with the increase in the particle sizes and densities, the jetting velocity and diffusion angle gradually decrease. The diffusion angle of irregular particles is generally greater than that of circular particles. The weight of the significant influencing factors on jetting velocity(from the largest to the smallest) is: size, density. The significant influence factor weight of diffusion angle(from the largest to the smallest) is: shape, size. The particle size and density are directly proportional to the gas-solid coupling forces they are subjected to, and inversely proportional to the initial particle acceleration. Irregular particles have a higher number of collisions than those of circular particles. According to the changing law of coupling forces and kinetic energy, the particle motion characteristics are divided into two stages: acceleration and stability. At the initial moment, the particle acceleration determines the amount of kinetic energy that the particles may achieve in the stable stage.

To study the tribological characteristics of electrical contacts in nuclear safety level DCS equipment, the risk frequency of the equipment was evaluated. A set of ball-on-flat tribological test apparatus was established to conduct a series of current-carrying friction tests at the risk frequency. The interfacial friction and wear characteristics under different input electric currents were investigated and the results show that the risk frequency of the nuclear safety level DCS equipment is as 12 Hz. At this frequency, as the input current increases from 1 A to 3 A, the friction coefficient shows a trend of decreasing first and then increasing. Under high-current conditions, the duration until electrical contact failure significantly increases. The electrical contact interfaces exhibit five features: scratches, particles, micro-cracks, debris accumulation and splashing. Although there are some oxidation products in the contact areas, as long as there are enough contact areas to achieve conductivity path, the interface may still maintain good electrical contact status.

 In response to the problems of low transmission efficiency, high power consumption, poor performance in heavy load starting, and asynchronous operations of multiple motors in the traditional asynchronous motor driving system of belt conveyors in coal mines, a multi-motor permanent magnet direct driving belt conveyor was proposed based on the fuzzy active disturbance rejection deviation coupling control strategy. Then, the simulation tests of fuzzy active disturbance rejection deviation coupling control strategy and the on-site industrial tests of underground transportation groove belt conveyor were carried out, and the results show that compared with traditional master-slave control, the multi motor permanent magnet direct driving system of the belt conveyors based on fuzzy PI active disturbance rejection deviation coupling control strategy has a maximum speed difference of only 0.04% among multi motors under rated loads, and a maximum synchronization performance improvement of 99.7%. In the on-site dynamic coal dropping tests, the maximum asynchronous speed is only 2%, which may meet the zero speed heavy load starting requirements of long distance and heavy duty belt conveyors, significantly improving the synchronization performance and anti-interference ability of the multi motor driving system of belt conveyor.

 For the issues of decreased effectiveness in active noise reduction under acceleration conditions with rich and varying orders in the working speeds of hybrid electric vehicle engines, a variable order notch filter-x least mean square(VOFxLMS) algorithm was proposed, and a corresponding multi-channel active engine noise control system was established. In MATLAB/Simulink, the multi-channel active engine noise control simulation model was constructed for a seven-seat hybrid multi-purpose vehicle(MPV), by utilizing the actual vehicle acoustic path, in-cabin noise, and engine speed signals, two algorithms were employed for noise reduction simulation and comparison. Simulation results indicate that the proposed VOFxLMS algorithm may effectively reduce noise for specific orders at various charging speed points. Compared to the traditional notch FxLMS algorithm, the proposed VOFxLMS algorithms overall noise reductions at the left and right ears of the drivers seat and the third-row left seat are increased by 28.5%, 60%, 50% and 50%, respectively. The noise reduction effectiveness of the active engine noise control system employing the VOFxLMS algorithm was verified through on-road tests during acceleration conditions at speeds ranging from 70 to 100 km/h, and the system demonstrates effective suppression across various engine orders, including 2nd, 5th, 5.5th, 6th, 6.5th, 7th, and 8th orders.