Abstract:The structure of the manipulator has the characteristics of high nonlinearity and strong coupling,and high-precision motion control is a hot topic of concern for scholars.The AR4 manipulator was used as the research object to systematically analyze the forward and inverse kinematics that greatly impact the control of the manipulator,determining the manipulator's corresponding structural parameters.And the D-H method was used to solve the numerical calculation model of the manipulator forward and inverse kinematics.The cubic spline interpolation algorithm was used to optimize the manipulator's jitter phenomenon in the joint space.In Cartesian space planning,the linear interpolation method was used to reduce the end effector's motion distance.The specific planning points were obtained by Matlab simulation,meeting the design requirements.Finally,the SolidWorks was used to establish a three-dimensional model of the manipulator and generate an unified robot description formatc(URDF)model.The actual trajectory of the manipulator in joint and Cartesian space was planned according to MoveIt,and through RViz,the movement process was displayed.The results show that after adding the cubic spline interpolation algorithm,the joint motors of the manipulator can maintain stable operation,and the joint trajectory curvature is respectively reduced by 15.4%,35.6%,21.3%,26.8%,18.98%and 45.7%,which effectively solves the jitter vibration problem during joint movement and achieves smooth motion of the manipulator.

Abstract:The research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components.In recent years,machine learning,especially neural networks,has been widely applied in fatigue life prediction.However,the scarcity of fatigue data has limited the further application of neural networks in fatigue prediction.To address this issue,physics-informed neural networks that consider prior physical knowledge of fatigue have gradually gained attention.Firstly,provided an overview of the classification of machine learning algorithms and the application of neural-network models in multiaxial fatigue life prediction.Then,it focused on a deep exploration of the research on material fatigue life prediction based on physics-informed neural networks.Finally,the development of physics-informed neural networks was introduced from three aspects:physics-informed input features,the construction of physics-informed loss functions,and physics-informed network frameworks.Relevant studies show that physics-informed neural networks can exhibit better physical consistency and prediction performance in the process of multiaxial fatigue life prediction of materials.

Abstract:In order to study the influence of residual stress and hardness on bending fatigue performance of gear,the 20MnCrS5 steel gear with carburizing heat treatment was taken as the research object,and composite small diameter shot peening strengthening treatment was carried out to realize the gear with different hardness and residual stress states of the same material.Based on the maximum principal strain criterion,incorporating separate factors for residual stress influence and residual stress-hardness coupling influence were introduced respectively to establish the fatigue life prediction model.Through shot-peened gear bending fatigue tests,optimal values for both the residual stress influence coefficient and correction coefficient were determined.The two models'accuracy for life prediction was contrasted,and the accuracy of these models was further verified through unshot peened gear bending fatigue test.The results show that considering only residual stress influence yielded an optimal value of 0.09 for the residual stress influence coefficient,the model achieving high life predictive accuracy.Whereas considering the effects of residual stresses and hardness,it requires a correction coefficient with an optimal value of 0.04,the model achieve even higher predictive accuracy.

Abstract:In response to the problem of the gearbox fault diagnosis and analysis based on multi-sensor data under dataset imbalanced conditions,a gearbox fault diagnosis method based on a kurtosis index data fusion and a generative adversarial neural networks(GAN)was proposed.This method weighted the fusion of multiple sensor data based on signal kurtosis,highlighting the fault sensitive components of the gearbox in the fused signal.Then,a wavelet packet transform was used to extract the energy coefficients of each frequency band of the signal as signal features.Finally,the classification and recognition of signal features were implemented based on a back propagation(BP)neural network.Due to the fact that in actual working conditions,fault signals were more difficult to obtain than normal signals,GAN was used to expand the fault data section of the dataset,and the expanded dataset was used to train BP neural network.Through test analysis,it is shown that the fault accuracy of the described method is as high as 98%,which verifies the correctness of the proposed method and provides new ideas and methods for multi-sensor data fusion and fault diagnosis problems.

Abstract:To address the issue of gearboxes'vibration and noise,multi-objective topology optimization was adopted to optimize the box structure.First,a combination weighting method known as game theory comprehensive analytic hierarchy process and grey correlation was proposed to allocate optimal weight values to sub-objectives.At the same time,the iterative curves of the optimization results of the three weighting methods were compared to verify the advantages of the combination weighting method.Then,the compromise programming method was used to normalize the sub objectives to obtain the comprehensive objective function.Finally,based on the topology optimization results,stress displacement nephogram,and modal shapes,the box structure was improved.Compared with the original box,the improved box reduces the mass by 11.2%,the maximum stress of the box decreases by 39.6%,the displacement decreases by 5.1%,the node displacement amplitude response decreases by 82.8%,and the all first four-order frequencies increase.The proposed weight allocation method improves the disadvantages of low reliability of sub objective weight value allocation and ignoring subjective judgment,realizes the lightweight of the box.After the improvement of the box structure,the static and dynamic performances are significantly improved.

Abstract:Sprocket chain ring drive system is the core component of scraper conveyors.The wear of sprocket chain socket is one of the main fault of scraper conveyors.Started with the analysis of the meshing transmission characteristics of the sprocket chain socket,constructed the Archard linear wear model,calculated the wear depth of the chain socket's linear under working conditions,measured the wear depth of the actual wear sprocket,and verified the accuracy of the Archard linear wear model.The deformation model of ring chain was constructed by finite element method,the shape change of chain socket busbar was predicted,and the shape of sprocket tooth surface after wear was reconstructed according to the change of direction and busbar.The influencing factors of chain socket wear were analyzed.The results show that increasing the hardness of sprocket material,reducing the chain speed,the load and the laying angle can reduce the chain wear.This study provides a basis for the study of the wear pattern of sprocket chain of scraper conveyors.

Abstract:Reinforced shell structure is widely used in aerospace load-bearing structures because its high specific stiffness and specific strength.By considering the uncertainty and risk factors in the structural parameters,the reliability-based design optimization(RBDO)can avoid the overly conservative design of the structure and ensure its reliability and safety.An efficient RBDO method based on adaptive surrogate model was proposed to solve the problem of lightweight design of reinforced shell structure under buckling reliability constraints.The adaptive addition of sample points was implemented through the expected feasibility function criterion,and the discrete variables was continued by constructing piecewise functions.This increases optimization efficiency while ensuring the reliability of design results.Finally,the effectiveness of the proposed method is verified by comparing the RBDO results with the deterministic optimization results.

Abstract:In order to solve the problem of multi-source heterogeneous data fusion and improve the accuracy of reliability evaluation,a multi-source heterogeneous data fusion method based on approximate failure point was proposed by using D-S theory and the least squares method.Firstly,the probability envelope curves were obtained through constructing probability assignment for a single source of data and performing weighted fusion.A distribution fitting model based on approximate failure points was also established.Secondly,the parameter estimation value was obtained by the least squares method,and the area metric was constructed to determine the true failure distribution function,and then the reliability assessment was completed.Finally,the feasibility and effectiveness of the proposed method were verified by examples,and the accuracy was higher than that of Bayes method.

Abstract:Single tube towers are widely used as the foundation for carrying 5G communication equipments.Due to construction needs,the mounted equipments often changes with the changes in 5G construction.Due to the small damping of the single tube tower,the increase of mounted equipments may cause an excessive vibration,reducing its load capacity.Therefore,the control of tower top vibration is particularly crucial.A particle damping tuned mass damper(PDTMD)method was proposed to control the problem of excessive vibration at the top of 5G communication towers.Based on a collision theory,a mathematical model using PDTMD to control the vibration of the communication tower was established.The vibration response of the tower under effects of PDTM was verified by the detailed calculation,and the damping mechanism of PDTMD was analyzed.The damping effectiveness of PDTMD was compared with the traditional tuned mass damper(TMD).The results show that the particle damping has good energy dissipation ability.Compared with traditional tuned mass dampers,PDTMD has better damping effect and higher robustness.Finally,based on the actual signal tower,the usage parameters of PDTMD in complex environments were optimized.Effects of gaps between damping particles and honeycomb structures,particle materials,and particle mass ratios on the damping effect of dampers were analyzed.

Abstract:Due to the similarity between the internal structure of wind turbine blades and plant leaves,a new type of bionic leaf vein structural distribution was proposed,along with an entire composite blade layup program based on the bionic method of applying the mid-axis morphology of plant blades to 5 MW wind turbine blades.The modal analysis and static analysis of the new bionic vein blade were performed using the fluid-solid coupling method.The results show that the first six-order of the nature frequency of the bionic blade are improved in comparison to the traditional layup blade and are difficult to resonate,as well as its torsion resistance.Under the extreme wind load of 50 m/s,the displacement of the bionic blade's tip is significantly smaller than that of the traditional blade,and the distribution of the strain and the distribution of the shear stress are more uniform than those of the traditional layup blade,but the maximum value of shear stress rises.

Abstract:In response to challenges such as large sampling data,extended diagnosis time,and subjective fault feature selection in traditional bearing fault diagnosis,based on compressed sensing(CS)and deep multi-kernel extreme learning machine(D-MKELM)theory,a CS-DMKELM intelligent diagnosis model for rolling bearings was proposed.Firstly,sparse signals were obtained through threshold processing of transformed domain signals.A Gaussian random matrix was employed as the measurement matrix to compress the processed data.Secongly,the compressed data was used as the input signal for the D-MKELM.Particle swarm optimization(PSO)algorithm was applied to optimize critical parameters,enabling intelligent fault diagnosis.Results demonstrate that the proposed method,using only a small amount of bearing diagnostic data,automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM.The proposed method enables rapid fault diagnosis of bearings.With a diagnostic time of 0.55 s,a final recognition accuracy of 99.29%was achieved.The proposed method reduces the diagnostic time and exhibits the high diagnostic accuracy,providing a new approach for handling massive bearing data in the fault diagnosis.

Abstract:Shot peening process is widely used in the manufacturing process of gears and other basic components,and its own limitations limit the enhancement of the surface integrity of the workpiece.In order to further improve the surface integrity of the workplece,A combination of numerical simulation and experimental methods was utilized to study the effect of two surface composite strengthening processes,such as double shot peening and shot peening-ultrasonic rolling,on the surface integrity of 18CrNiMo7-6 carburization gear steel samples,and mainly analyzed the effect of the two composite strengthening processes on the improvement of surface integrity of the shot peened samples.The results show that the maximum value of the residual compressive stress of the double shot peening sample was 1 359.56 MPa,locates at the depth of 0.08 mm,and the maximum value of the residual compressive stress of the shot peening-ultrasonic rolling peening sample was 1 329.05 MPa,locates at the depth of 0.25 mm.Compare with the single shot peening sample,the surface roughness of the double shot peen-ing sample and the shot peening-ultrasonic rolling sample was 29.42%and 29.42%lower than that of the single shot peening sample.Compare with the single shot peening samples,the surface roughness of the double shot peening samples and shot peening-ultrasonic tumbling peening samples decreased by 29.42%and 62.76%,respectively,the surface microhardness increased by 8.70%and 17.60%,and the standard deviation of the surface node compressive residual stress value decreased by 23.36%and 89.50%.The shot peening-ultrasonic rolling process is more effective in enhancing the surface hardness,thickness of the residual stress layer and uniformity of the residual compressive stress,as well as reducing the surface rough-ness of the specimens,and can effectively improve the surface integrity of the shot peened samples.

Abstract:In practical engineering,gearboxes are subject to complex and variable operating environments,which hinder the ability of a single vibration signal to accurately and effectively represent fault information under different working conditions.To address this issue,a gearbox fault diagnosis method for variable working conditions based on weighted subdomain adaptive adversarial networks was proposed.Initially,a multi-source heterogeneous signal fusion strategy was employed to transform vibration signal spectrograms,current signal Gramian matrices,and infrared thermograms into a multi-channel dataset,offering diverse perspectives on gearbox operational states.Subsequently,a self-calibrated convolutions network(SCNet)incorporating an efficient channel attention(ECA)mechanism acted as a feature extractor,dynamically adjusting the interactions and dependencies between multi-source heterogeneous signals to balance the scale differences between the source and target domain heterogeneous data.Concurrently,during adversarial training of the feature extractor and domain discriminator,maximum mean discrepancy(MMD)and linear discriminant analysis(LDA)were introduced to measure the domain alignment degree of the current cross-domain task feature representation and the diagnostic task decision boundary.A dynamic balancing factor was constructed to real-time adjust domain alignment loss and class discriminability loss,effectively aligning each class space between the source and target domains.Finally,validated by a collected gearbox fault dataset under variable operating conditions.The results show that the proposed method achieves diagnostic accuracy exceeding 95% across different conditions,demonstrating its feasibility and effectiveness.

Abstract:To address the creep issue that arises during the long-term vertical storage of solid rocket motor(SRM),a method was proposed that involved embedding a specially shaped functional combustible core model(reinforcement structure)into the propellant grain matrix without altering the basic structure of the grain.Initially,the distribution patterns of creep in the propellant grain under the coupled effect of solidification cooling and vertical self-weight were analyzed by using three-dimensional numerical simulation methods.Subsequently,the reinforcement structure was designed by using the solid isotropic material with penalization(SIMP)method for topology optimization,determining the geometric configuration of the embedded reinforcement structure.Finally,the final optimized design results were determined through comparative analysis of the anti-creep effect of the topology-optimized reinforcement structure.The research results demonstrate that the deformation stress and strain of the solid rocket motor propellant grain with the reinforcement structure are significantly reduced compared to those without the reinforcement structure,effectively suppressing the creep of the grain.

Abstract:Focusing on the performance of single-orifice aerostatic bearings,the effect of design parameters on load capacity and stiffness was investigated,aiming to optimize the structural design and performance of aerostatic bearings.A parametric model of the single-orifice aerostatic bearing was developed using the finite element method.The influence of fac-tors such as air cavity design,orifice diameter,supply air pressure,air film thickness,orifice depth,air cavity thickness,and air cavity diameter on bearing performance was analyzed.Simulation and test were combined to validate the influence of design parameters on bearing performance.The results indicate that orifice diameter,air film thickness,supply air pressure,and air cavity diameter significantly affect the bearing·s load capacity,while orifice depth and air cavity thickness have a smaller impact.Parameters such as orifice diameter,supply air pressure,air cavity diameter,and air cavity thickness positively cor-relate with load capacity,whereas air film thickness and orifice depth negatively correlate.Additionally,aerostatic bearing with air cavity structures exhibits superior load capacity and stiffness compared to that without air cavities.The consistency between simulation results and test data confirms the reliability and accuracy of the proposed simulation model.

Abstract:Carbon fiber is increasingly used to replace conventional glass fiber in layup designs of large wind turbine blades to improve their structural strength,but the high cost of carbon fibers makes it difficult to cover the entire area of the blade.Therefore,the research of the influence of mixing ratio and relative position of carbon fibers and glass fibers on the structural performance of the blade can help to obtain higher performance and lower cost wind turbine blades.The proportion of carbon fibers and glass fibers in the corresponding position of the main beam of the blade and the relative position of layup were adjusted by Ansys software.And the structural statics,modal and buckling analyses were conducted by using a combination of computational fluid dynamic method and finite element method.The results show that the performance of the blade main beam using carbon fibers and glass fibers mixed layer can be similar to that of carbon fiber blades.When the carbon fibers near the tip of the blade can improve the blade first-order modal and buckling factors.When it is close to the root of the blade has less impact on the blade maximum stress and strain.Under the premise of ensuring the blade stability and anti-resonance performance,when the carbon fibers and glass fibers layup ratio is 3∶1 and the carbon fibers are close to the root of the blade,the overall performance of the blade is better.

Abstract:To solve the problems of high-power downstream belt conveyors in large inclination angles,high belt speed conditions which are very prone to flying cars and belt breakage,a kind of disk-type adjustable permanent magnet damping roller device was put forward by adjusting the size of the area of engagement between the permanent magnet and the coil to realize the braking adjustment.The Maxwell software was used to study the transient magnetic density distribution law of the disk-type permanent magnet damping device under stable conditions and the changing law of damping torque by different air gap thicknesses,and the test bench was built for test verification.The results show that with the increase of the air gap thickness,the magnetic density gradually increases,up to 2.1 T.The damping moment increase when air gap thickness is from 1 mm to 2 mm,the damping moment decrease when from 2 mm to 3.5 mm.The research can provide data support for improving and optimizing high-power downstream belt conveyors.

Abstract:To achieve quantitative detection of bolt loosening angles through single frame images,a method based on color segmentation and connected domain feature processing was designed.Firstly,a method for performing nonlinear stretching,normalization and optimal threshold segmentation on a component successively in the Lab color space was designed to segment and represent the red anti-loosening line image of the bolt loosening angle.Secondly,the morphological operations were performed on the image by using the open operation.Then,the orientation vector of the connected domain in the anti-loose line image was determined by computing the geometric moments.Finally,the bolt loosening angle was determined through the four-quadrant arctangent function.The results demonstrate that the precise measurement of the bolt loosening angle through a single frame image can be achieved by this detection algorithm,with a maximal relative error of 1.80%,its accuracy meets the needs of engineering practice and has strong engineering application value.

Abstract:In order to clarify the dynamic response,damage situation,and failure mode of titanium/steel corrugated composite plates and interfaces under impact loads,small energy(53 J)impact experiments were conducted on titanium/steel corrugated composite plates using a light air cannon.On the basis of verifying the effectiveness of the numerical calculation model,numerical simulations were conducted on composite plates under various velocities to study the impact mechanical response of composite plates and their interfaces under various energies.The results show that,under low energy impact conditions,the front of the corrugated composite plate shows plastic expansion damage,and the back plate has protrusions with cracks at the raised areas.The corrugated interface layer is tightly bonded and overall concave;cut the target plate along the impact center and observe that there are no cracks,delamination,or other damages on the cross-section.This is different from fiber reinforced composite laminates.There will generally be obvious delamination inside when there is barely visible damage on the impact surface.In numerical simulation,the cohesive force interface damage area of the corrugated composite plate under impact is less than that of the planar interface composite plate.When subjected to low energy impact,the absorption of bullet kinetic energy by the corrugated plate is mainly dominated by overall deformation energy absorption,and the damage to the titanium/steel composite plate at the corrugated interface is relatively small.Under various energy impacts,corrugated interface composite plates have tighter interface bonding,better structural integrity,and are less prone to damage compared to planar interface composite plates.

Abstract:Low-cycle fatigue is a typical failure mode of engine pistons.In order to study the influence of multi-source uncertainty factors on the reliability of low-circumference fatigue of pistons and improve the efficiency of the reliability analysis,a new reliability calculation method is constructed based on the polynomial-chaos-based Kriging(PC-Kriging)model and the Monte Carlo simulation(MCS),and the accuracy and efficiency of this method are proved by numerical examples.Taking the piston group structure of a certain diesel engine as the research object,a finite element model of the piston is established based on the thermal-mechanical coupling analysis,and the reliability analysis of the piston for low-cycle fatigue is carried out by using this method,taking into account the critical dimensions,the material properties,and the uncertainty of the load.The results of the reliability analysis show that,compared with the same type of method,this method is more efficient in calculation,requiring only 20+93 finite element calculations,and the probability of fatigue failure is 1.053%when the expected design life of the piston is 1.4×104.The sensitivity analysis shows that,the height of the piston,the piston diameter,the elasticity modulus of the material,and the parameters of the fatigue calculation model have a greater influence on the reliability.The analysis results can provide a guidance for the reliability design of the piston.