Passivation can improve the service life of the cutter, the stability of the cutting process and the quality of the machined surface. The edge of passivated cutter is not a regular arc. This paper used the method of discretizing the cutting edge axially into micro element cutting edge and was based on the basic theory of slip line field. Comprehensively considering the variation of ploughing force with cutting thickness and the characteristics of asymmetric edge shear, it pointed out the different cutting stages experienced in the cutting process of asymmetric edge cutter, and established the instantaneous milling force model of asymmetric edge cutter. Finally, a comparative analysis was carried out combining with milling experiments. The results show that the proportion of ploughing force decreases with the increase of cutting thickness in the whole cutting process; the milling force obtained from the model well accords with the experimental results. The correlation coefficient of the two is greater than 0.7, the ave-rage error is less than 5%, and the peak error is less than 15%. 

In order to measure the force loaded on bogie test bench and improve the measurement accuracy, a three-dimensional (3-D) force measuring platform with multiple force sensors connected in parallel was designed. Its measurement principle was analyzed theoretically and a Wheatstone bridge measurement circuit was built. 3-D force measuring platform is a kind of shear stress sensor, and the analysis shows that the output voltage of its mea-surement circuit has a definite linear relationship with the force loaded. The static calibration test of 3-D force measuring platform under uniaxial load was carried out and the output characteristics under uniaxial load and the interdimensional coupling effect of load measurement were analyzed. It finds that under uniaxial load, the voltage output of the measurement circuit corresponding to the loaded axis has good linearity, but the voltage output of the non-loaded axis measuring circuit is small. In this paper, the coupling degree evaluating coupling effect between dimensions and the numerical decoupling method based on least square method and BP neural network were also proposed, and their effectiveness was verified by experiments. At the same time, the orthogonal method was used to carry out the multi-axis loading test of the three-dimensional force measuring platform. The results show that the single-axis measurement error and the overall measurement error are not more than 1.5%, thus the measurement accuracy meets the test requirements.

A widely applicable post-adaptive input shaper algorithm was proposed to solve the residual vibration of multi-axis servo system caused by the flexibility of the system in the emergency stop section of high-speed motion. The algorithm does not need to identify the system modal parameters and it is based on the recursive least square method (RLS). The residual vibration signal was used as the input of the algorithm to optimize the shaper coefficient vector with the best vibration suppression effect under the current trajectory. The adaptive forgetting factor updating algorithm was introduced to improve the tracking performance of the shaper in non-stationary environment. A full connection multilayer neural network model was established and trained by using multiple sets of excitation tra-jectory samples. It solves the problem of the original algorithm that the cost of time is significantly increased due to multiple changes of trajectory and reoptimization. The experimental results show that the residual vibration amplitude of the post adaptive input shaper with adaptive forgetting factor is reduced by 28.3% averagely and 36.9% maximally, and the convergence time of residual vibration is reduced by 28.4%, compared with the traditional post adaptive input shaper. The residual vibration amplitude of the input shaper predicted by the multilayer perceptron model is reduced by 21.6% averagely and 29.8% maximally, and the convergence time of residual vibration is reduced by 23.7%, compared with the ordinary post adaptive input shaper. The algorithm proposed in this paper has certain application significance for improving the positioning accuracy of multi-axis servo system and shortening the positioning waiting time. The introduction of multilayer neural network model improves the overall work efficiency under the condition that the expected trajectory changes frequently.

A new 12 DOFs ( Degrees of Freedom) calculation model for the powertrain-subframe mounting system was established by reducing the mount into three-dimension springs with the nonlinear force and displacement relation． Then the rigid mode and energy distribution，the displacements of the powertrain's CG，the displacements of the subframe's CG，and the displacements and loads of the mounts in the powertrain-subframe mounting system of an electrical vehicle under the ultimate load were calculated． The theoretical calculation model was verified by ADAMS model． The calculation model provides methods for rigid mode optimization and the design of mounting force-displacement control in powertrain-subframe mounting system．

4D printing，also called variable-property rapid prototyping，is a technology that combines material， mathematics，and additive manufacturing． The materials used in 4D printing are smart materials，and the printed structure，shape，attributes，or function can change over time under environmental stimuli． With its characteristics of self-assembly，self-adaptation and self-repairing，flexible，personalized and intelligent structures can be designed． This article firstly introduced 4D printing technology，including the characteristics of 4D printing，molding methods，materials used and deformation forms，and then explained the application progress of 4D printing． Finally，it discussed the key issues and the future development direction of 4D printing．

A clonal selection algorithm (CSA) for multi-robot collaborative scheduling was proposed to solve  the difficulties in controlling and optimizing the task assignment and scheduling of multi-robot in the same period in intelligent manufacturing system. Firstly, the initial conditions of multi-robot task were analyzed. Then taking the task completion as the constraint condition and taking task flow time, single robot maximum cost and multi-robot total cost as the objective function, a mathematical model of multi-robot task allocation and scheduling optimization was constructed. The affinity function was introduced to dynamically change the parameters of clone, mutation and selection, so as to improve the computational efficiency. Finally, the scheduling method was verified by Gantt chart analysis. The results show that the proposed method is stable and feasible. Meanwhile, the experiments show that the clonal selection algorithm has the superiority of fast convergence speed and high calculation accuracy, and can optimize various indicators under complex multi-task conditions.

Based on the theory of thermal elastohydrodynamic lubrication ( TEHL) ，the lubrication and friction characteristics of modified herringbone gears were analyzed． Firstly，the variation of instantaneous contact lines on tooth surface of the herringbone gears with the specified structure was analyzed under the specified working condition． The specified number of meshing points along the meshing line were selected as analysis objects and the essential values of contact parameters were obtained． Secondly，the accuracy of the TEHL calculation program was verified and the TEHL calculation was performed． The oil film characteristic values at the meshing points were derived from calculation results to obtain their variations along the meshing line． The influences of input torque， driving gear rotation speed and amount of crowned modification on the film profile curves at the meshing point S1 were analyzed． Finally，the variation law of the friction coefficients at the meshing points was obtained by using the Ｒee-Eyring model． The calculation results show that the maximum film pressure on tooth surface increases first and then decreases，while the variation trends of the minimum film thickness and the maximum film temperature are opposite to that of the maximum film pressure． In addition，the temperature rise and the thickness of the film are influenced significantly by the rotation speed of driving gear; the increase of input torque or amount of crowned modification will lead to the film temperature rise and the peak value rise of the film pressure; the value of friction coefficient is almost zero at the pitch point while greater at the engaging-in / out side．

Engineering structures are often subjected to multi-dimensional random loads, and the accurate simulation of multi-dimensional random load time history is of great significance to the reliability design of structures. Based on the rigorous theoretical formula derivation of spectrum representation method about the multi-dimensional random load, the cause for the large deviation between the spectrum density matrix of the synthetic load and that of target load was analyzed in detail, providing a theory support for the multi-dimensional random load time domain simulation correction. A multi-dimensional random load time domain simulation algorithm based on the spectrum correction method was proposed. By calculating the deviation between the spectrum density matrix of the synthetic time domain load and that of the target load, the iterative technique was developed to modify the spectrum density matrix of the synthetic time domain load through feedback of errors. Furthermore, the spectrum density matrix of the synthetic time domain load was made to approach to that of the target load. Finally, a numerical analysis about three dimensional random load model was carried out. The results show that the spectrum density matrix of the synthetic multi-dimensional random load time history based on the spectrum correction method can accurately match the spectrum density matrix of the target load.

A soft wearable upper limb rehabilitation robot was designed based on the principles of light weight, flexibility, easy-wear and ergonomics to make up the shortcomings of traditional rehabilitation robot, such as heavy weight, difficult to wear and poor motion flexibility. The robot body was make of elastic cloth and the pneumatic artificial muscle（PAM）was used as the antagonistic driving joint of the actuator to realize the elbow flexion/extension and pronation/supination motion. Moreover, the dynamic model of the rehabilitation robot is established according to the mathematical model of the new PAM and Lagrange dynamic equation. Aiming at the control difficulties of the soft system driven by PAMs, such as easy chattering and delayed response, a PID controller based on RBF neural network was designed. Aiming at the problem that the initial parameters of PID controller depend on experience, the fuzzy PID controller was designed to determine the optimal initial parameters of PID controller. Finally, the performance of the RBF-PID controller was verified through simulation and experimental research, and was compared with the traditional PID controller. The results show that the RBF-PID controller has fast response speed and high control stability, and it can realize the stable control of the soft rehabilitation robot.

A horizontal stability optimization control strategy of port overload AGV under low attachment coefficient road surface was proposed based on three-layer control structure. The MPC algorithm was used in the upper controller, which can continuously sample and predict the AGV dynamic parameters and alleviate the time lag problem of the control system caused by the series connection with the middle and lower-level controllers. The genetic algorithm was used in middle-level controller to optimize the parameters of the fuzzy controller for application to the stable control of the port AGV body. The SQP algorithm was applied in the lower-level controller to convert the driving torque optimal distribution problem into a quadratic programming sub-problem  and find an online solution. The Matlab / Simulink and Trucksim joint simulation platform based on port AGV was constructed to verifies the validity and robustness of the proposed strategy.

According to the requirement of the fault-tolerant control for the soft speed-sensing technology，a novel self-driving vehicle speed estimation method based on the interacting multiple-model unscented Kalman filter was proposed to adapt to the unknown statistical characteristics of the system noise． Firstly，a nominal model，which includes vehicle kinematic and dynamic characteristics，was established based on the positioning information of the self-driving vehicle，and then it was transformed into a state space nominal model including the unknown statistical characteristics of the system noise by using the forward Euler discretization method． Secondly，a series of typical values were used to describe the unknown statistical characteristics of the system noise，and a series of state space nominal models including different statistical characteristics of the system noise were obtained． For each state space nominal model，unscented Kalman filter was used to estimate the self-driving vehicle speed and all of the outputs were smoothly fused by interactive multiple-model algorithm． Thus，the interacting multiple-model unscented Kalman filter with adaptive ability to the statistical characteristics of the system noise was obtained． Simulation results show that the estimation accuracy of the proposed method for the vehicle longitudinal and lateral speeds is 4 times and 1. 5 times as many as that of the unscented Kalman filter，respectively，which satisfies the requirement of the fault-tolerant control for the self-driving vehicle．

An Ｒ + { 2-UPＲ + ＲPＲ} leg mechanism was designed to improve the mobility and adaptability of quadruped robots in complex environment． The spiral theory analysis showed that the mechanism has 3 freedom degrees of two rotations and one movement． Then，the inverse kinematics solution and the positive solution equation were derived based on the position relationship between the member and the motion pair in space，and the velocity and acceleration of the driving member were solved． Then，with the help of Matlab calculation software and ADAMS simulation software，the accuracy of the positive and negative solution model was verified through the comparison of kinematics positive and negative solution theoretical calculation results and simulation results． Finally，the motion trajectory and velocity changes of the leg mechanism were obtained through the motion simulation of the robot's single leg． And the working space of the leg mechanism was analyzed to obtain the working range and motion characteristics of the foot end． The results show that the proposed leg mechanism with less independent joints has better working adaptability and movement flexibility．

When the plug-in hybrid electric vehicle (PHEV) starts the engine in motion process, the driving torque fluctuates greatly due to the different torque response characteristics of the motor and the engine. A torque coordinated control strategy was proposed in this paper to solve this problem. First of all, the hybrid electric vehicle power system model was established on the LMS.AMESim platform. Then, a torque coordination control strategy model of “clutch oil pressure fuzzy control + motor torque compensation + engine speed control + engine torque change rate limit” based on engine idling speed ignition was built on the Matlab/Simulink platform. Finally, based on the built co-simulation platform, the mode transition co-simulation was carried out and compared with different control strategies. The results show that the proposed mode switching control strategy can greatly improve the vehicle impact and clutch friction work, and improve the smoothness of mode transitions.

When the traditional sliding mode control ( SMC) method is applied to steer-by-wire ( SBW) system，it is necessary to obtain the upper bound value of system disturbance in advance，and the change of system disturbance will lead to poor stability of angle control． In order to improve the wheel angle tracking performance of SBW system，an adaptive neural network sliding mode control ( ＲBFSMC) method considering system disturbance was proposed． Firstly，the ＲBF neural network was used to estimate the system uncertainty and motor torque disturbance in real time． Secondly，the wheel angle controller was designed by combining ＲBF with SMC in order to improve the adaptability and stability of the angle control． The joint simulation results of MATLAB /Simulink and CarSim on SMC and ＲBFSMC show that，ＲBFSMC can better maintain 0° wheel angle and realize fast and stable tracking of dynamic wheel angle under the conditions of vehicle maintaining steering，continuous steering and single /double lane changing，and it has better corner response and tracking performance than SMC． The study indicates that ＲBFSMC has better robustness and stability than SMC．

Label image sharpness is the key point that influences the effectiveness of visual inspection during the label qualify inspection of household appliances with machine vision． To solve the motion blurring problem of tag image captured by camera fixed on the moving robot，a regularization model based on L0 norm was proposed． The general methods of deblurring algorithms have considered and implemented much of image priors for blurred images，which can obtain the well-done visual overall effect． However，these general algorithms does not take the characteristic of identification tag into consideration，and this is an important prior to deblurring． To this point，a specific model consisting of image gradient prior regularization and sparse regularization was proposed to analyze pixel distribution and gradient distribution features of label image． The experimental results show that，as compared with other deblurring algorithms，the proposed method can effectively suppress the ringing effect at the edge of the label image while restoring label image sharpness on synthetic images and real images，and the real computation speed is increased by 80. 52% ．

The pre-stage asymmetry is the main factor causing the zero deviation of the jet-pipe servo valve. In view of the uneven size of two receiving holes, the misalignment of jet-pipe and the receiver and the asymmetry of the receiving hole center, a mathematical model of the jet-pipe pre-stage was established. A modified model based on definite integral was designed to deal with the asymmetry of the receiving hole center. Considering the asymmetry of the receiving area of the pre-stage, the quantitative analysis method and model of the pressure characteristics and the zero deviation value were established. The results show that the pre-stage processing, assembly and environmental effects will cause the asymmetry of the geometric structure and directly cause the zero deviation of the servo valve. The initial assembly error between the jet-pipe and the receiver, the initial error of the radius of the recei-ving hole will seriously cause the servo valve to produce a zero deviation. The servo valve produces a positive zero deviation, when the right receiving hole is smaller than the left receiving hole, the jet-pipe has an offset error to the right, and the angle between the axis of the right receiving hole and the vertical is smaller than the angle between the left receiving hole and the vertical. The experimental results are in accordance with the theoretical results. In the processing and assembly process of the servo valve, the symmetry of the pre-stage structure should be guaranteed as much as possible.

The research on fault early warning method of large-scale nuclear power equipment has far-reaching significance for timely troubleshooting, reducing safety risks, cutting unnecessary costs and improving power generation efficiency. However, most of the traditional fault early warning systems of nuclear power equipment are real-time monitoring and alarm systems and havent made full use of the data value, so their early warning effects need to be improved. This paper proposed a fault early warning method based on Bayesian LSTM. The theoretical health value of time node was predicted by neural network and was compared with the actual value to find out the abnormal value. Firstly, the healthy historical operation data was extracted from the real-time database, and then a series of preprocessing were carried out on these data. Finally, the processed data was used as training set to establish the LSTM prediction model. The cross validation method was used to reserve the test set, and the goodness of fit, mean square error and Bayesian hypothesis test method proposed in this paper were used to comprehensively verify the accuracy of the model. After the generalization ability of the model was guaranteed, the data in the real-time database was predicted in real time, and the abnormal signals during fault creep were identified by threshold method. The experimental results show that the Bayesian-BiLSTM prediction model has better prediction accuracy than the LSTM model in the case of high time delay. In case 1, for the three time series outliers, the model proposed in this paper finds the abnormal signal and gives an alarm at least 15 hours in advance compared with the existing real-time monitoring systems, and its reliability is further verified by case 2.

During the metal cutting process，the intense thermo-mechanical load can cause changes in the microstructure of the chips． In order to reflect the influence of the material microstructure on the mechanical behavior of shear zone，an analytical model of microstructure evolution in shear zone based on dislocation density was proposed． And it was used to model the microstructure evolution process caused by plastic deformation during chip formation． Firstly，the distribution of strain and strain rate in shear zone was calculated through the analytical model of unequal shear zone． Secondly，the Johnson-Cook ( J-C) flow stress model was replaced by the material model based on dislocation density to calculate the temperature field in the shear zone iteratively． Finally，the evolution process of dislocation density and grain size in shear zone of orthogonal cutting oxygen-free copper and aluminum alloy was simulated． The results show that the analytic model for the micro-evolution of the shear zone combined with the dislocation density can better reflect the basic characteristics of the deformation field and microstructure evolution during the cutting process． The predicted values of cutting force and grain size in chips under different cutting parameters are in good agreement with the experimental data．

The surface wettability and mechanical properties of Nd: YAG crystals were investigated to study the influence of plasma jet on Nd: YAG crystals by using self-developed plasma equipment at room temperature． The results show that the plasma has significant effects on surface properties of Nd: YAG crystals，and the wettability of Nd: YAG crystals with three different roughness is improved after plasma modification． Especially，the smooth surface changes from hydrophobic to super-hydrophilic，and the good state of hydrophilicity lasts 30 hours． The improvement of wettability of Nd: YAG surface mainly lies in the increase of oxygen-containing functional groups on the surface of the modified Nd: YAG crystals． The microhardness remains unchanged within 0 to 11h，decreases first and then increases within 11 to 22h，and remains stable within 22 to 35h after the modification． Especially，a minimal microhardness occurs between 11 to 22 h after the modification． Under the peak load of 15. 0，30. 0 and 70. 0mN，the average microhardness decreases by 56. 2%， 45. 0% and 31. 6%，respectively． And the morphology of some indentations shows features of“ladder”and“shadow” due to the rapid increase of plasticity on Nd: YAG surface 11 hours after the modification．

Aiming at the problem that the real working load is difficult to obtain in the fatigue analysis of hydraulic excavator working device, a method based on the measuring data and rigid-flexible coupling model was proposed to evaluate the fatigue life of working device. Taking a 21-ton excavator working device as the research object, the actual working pressures and displacements were measured by the pressure and displacement sensors during the digging process for different working mediums. Next, the excavating resistance on the bucket tip was calculated by the mechanical equilibrium equation. The forces of each hinge point was obtained by the rigid-flexible coupling virtual prototype simulation in ADAMS, then the real load-time history of hinge points for multi-working conditions was composed acoording to the proportion of the working mediums. Finally, the fatigue analysis for the boom and bucket rod were conducted in nCode, and the results were compared with that of the fatigue bench test. The results show that there is a good agreement between them. The method proposed in this paper can provide basis for anti-fatigue design of hydraulic excavator working device.

In fused deposition modeling, the quality of parts can be effectively improved by optimizing the control of the filling speed. However, the filling speed is intercoupled with layer thickness, nozzle temperature and contour corner angle, so it is difficult to optimize the speed control. Based on the bivariate printing experiments, this paper used multivariate nonlinear regression method to construct an optimized control function of the filling speed by considering multiparameter synergistic coupling effect. Combining error analysis, uniaxial tensile and bending test, the effect of the optimization control of the filling speed was tested. Results show that, under the same conditions of processing parameters, by employing the speed optimization method proposed in this paper, samples’ accuracy error values are less than or closed to the average minimum accuracy error value, and the tensile performance can be enhanced slightly. Finally, the influence of this method on the tensile properties is also verified through fracture microscopic morphology analysis.