Laser-assisted water jet machining of high quality micro-trap structures on stainless steel surfaces

Secondary electron emission(SEE)has emerged as a critical issue in next-generation accelerators.Mitigating SEE on metal surfaces is crucial for enhancing the stability and emittance of particle accelerators while extending their lifespan.This paper explores the application of laser-assisted water jet technology in constructing high-quality micro-trap structures on 316L stainless steel,a key material in accelerator manufacturing.The study systematically analyzes the impact of various parameters such as laser repetition frequency,pulse duration,average power,water jet pressure,repeat times,nozzle offset,focal position,offset distance between grooves,and processing speed on the surface morphology of stainless steel.The findings reveal that micro-groove depth increases with higher laser power but decreases with increasing water jet pressure and processing speed.Interestingly,repeat times have minimal effect on depth.On the other hand,micro-groove width increases with higher laser power and repeat times but decreases with processing speed.By optimizing these parameters,the researchers achieved high-quality pound sign-shaped trap structure with consistent dimensions.We tested the secondary electron emission coefficient of the"well"structure.The coefficient is reduced by 0.5 at most compared to before processing,effectively suppressing secondary electron emission.These results offer indispensable insights for the fabrication of micro-trap structures on material surfaces.Laser-assisted water jet technology demonstrates considerable potential in mitigating SEE on metal surfaces.

MR damping design of the drive joints of the lunar-based extravehicular spacesuit booster mechanism

Considering the special walking behavior of astronauts on the lunar surface,to reduce the impact on their bones and improve safety during extravehicular operations and walking,a magnetorheological(MR)damping mechanism of power assisted transmission joint used in a new type spacesuit is proposed.In order to improve the damping performance of the MR damper,the influence of the damper s structural parameters on both the output and dynamic adjustable range of the damping torque is examined.According to the theoretical mechanical model,the output damping torque is calculated,the finite element method is used to conduct numerical tests.At the same time,the structural parameters of the damper are optimized by the response surface methods.The results indicate that the simulated torque aligns with the theoretically designed torque,and the damping characteristics of the optimized structure are effectively improved by the response surface method.Compared with the initial structure,the damping torque is increased by 10.8%,and the dynamic adjustable range is expanded by 52.9%.

Potentials and Challenges of Carbon Knowledge Graph in Sustainable Textile Production for Carbon Traceability:A Review

Textile production has received considerable attention owing to its significance in production value,the complexity of its manufacturing processes and the extensive reach of its supply chains.However,textile industry consumes substantial energy and materials and emits greenhouse gases that severely harm the environment.In addressing this challenge,the concept of sustainable production offers crucial guidance for the sustainable development of the textile industry.Low-carbon manufacturing technologies provide robust technical support for the textile industry to transition to a low-carbon model by optimizing production processes,enhancing energy efficiency and minimizing material waste.Consequently,low-carbon manufacturing technologies have gradually been implemented in sustainable textile production scenarios.However,while research on low-carbon manufacturing technologies for textile production has advanced,these studies predominantly concentrate on theoretical methods,with relatively limited exploration of practical applications.To address this gap,a thorough overview of carbon emission management methods and tools in textile production,as well as the characteristics and influencing factors of carbon emissions in key textile manufacturing processes is presented to identify common issues.Additionally,two new concepts,carbon knowledge graph and carbon traceability,are introduced,offering strategic recommendations and application directions for the low-carbon development of sustainable textile production.Beginning with seven key aspects of sustainable textile production,the characteristics of carbon emissions and their influencing factors in key textile manufacturing process are systematically summarized.The aim is to provide guidance and optimization strategies for future emission reduction efforts by exploring the carbon emission situations and influencing factors at each stage.Furthermore,the potential and challenges of carbon knowledge graph technology are summarized in achieving carbon traceability,and several research ideas and suggestions are proposed.

Numerical Simulation of Flow Field and Flow State Division in Thin-Film Evaporators

The flow field and flow state of thin-film evaporators are complex,and it is significant to effectively divide and quantify the flow field and flow state,as well as to study the internal flow field distribution and material mixing characteristics to improve the efficiency of thin-film evaporators.By using computational fluid dynamics(CFD)numerical simulation,the distribution pattern of the high-viscosity fluid flow field in the thin-film evaporators was obtained.It was found that the staggered interrupted blades could greatly promote material mixing and transportation,and impact the film formation of high-viscosity materials on the evaporator wall.Furthermore,a flow field state recognition method based on radial volume fraction statistics was proposed,and could quantitatively describe the internal flow field of thin-film evaporators.The method divides the high-viscosity materials in the thin-film evaporators into three flow states,the liquid film state,the exchange state and the liquid mass state.The three states of materials could be quantitatively described.The results show that the materials in the exchange state can connect the liquid film and the liquid mass,complete the material mixing and exchange,renew the liquid film,and maintain continuous and efficient liquid film evaporation.

Rolling bearing fault diagnosis based on data-level and feature-level information fusion

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.

Electrical power generator from electrospun hybrid PVDF-BaTiO_(3)nanofiber membranes

To enhance the piezoelectric performance of piezoelectric polymer thin films in general,hybrid polyvinylidene difluoride(PVDF)and nanosized barium titanate(BaTiO_(3))piezoelectric films were prepared and their piezoelectric performance examined.The hybrid nanofibers were fabricated via electrospinning at an external voltage of 15 kV.The nonwoven fabrics were collected using a roller collection device,and their morphological structures were analyzed via scanning electron microscopy.The crystal structures of these piezoelectric films were characterized via micro-Raman spectroscopy.β-phase of the composite nanofiber membrane almost increased to twice owing to the addition of BaTiO_(3)nanoparticles.Compared with pure,electrospun PVDF piezoelectric film,the piezoelectric characteristics of the hybrid piezoelectric films were considerably enhanced because of the additional BaTiO_(3)nanoparticles.The maximum instantaneous open-circuit voltage of the hybrid PVDF-BaTiO_(3)nanofibers film can be high up to 80 V.The high-performance hybrid piezoelectric films exhibited notable prospects for applications in wearable electronic textiles.

42CrMo硬齿面齿轮虚拟全寿命的试验与分析

在完成42CrMo材料硬齿面齿轮大样本全寿命成组试验的基础上,通过试验数据处理获得了定寿命下极限应力概率分布和R S N曲线。提出有限元疲劳寿命预测法的研究策略和实施方法,采用虚拟技术得到了42CrMo齿轮应力寿命曲线,与实际试验齿轮S N曲线较为接近。

A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings

Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.

GA-1DLCNN method and its application in bearing fault diagnosis

Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model(LetNet-5), one-dimensional large-kernel convolution neural network(1 DLCNN) is designed. Since the hyper-parameters of 1 DLCNN have a greater impact on network performance, the genetic algorithm(GA) is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1 DLCNN by the genetic algorithm is named GA-1 DLCNN. The experimental results show that the optimal network model based on the GA-1 DLCNN method can achieve 99.9% fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1 DLCNN is compared with one-dimencional small-kernel convolution neural network(1 DSCNN) and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1 DLCNN is optimal.

A method for workpiece surface small-defect detection based on CutMix and YOLOv3

Surface small defects are often missed and incorrectly detected due to their small quantity and unapparent visual features.A method named CSYOLOv3,which is based on CutMix and YOLOv3,is proposed to solve such a problem.First,a four-image CutMix method is used to increase the small-defect quantity,and the process is dynamically adjusted based on the beta distribution.Then,the classic YOLOv3 is improved to detect small defects accurately.The shallow and large feature maps are split,and several of them are merged with the feature maps of the predicted branch to preserve the shallow features.The loss function of YOLOv3 is optimized and weighted to improve the attention to small defects.Finally,this method is used to detect 512×512 pixel images under RTX 2060Ti GPU,which can reach the speed of 14.09 frame/s,and the mAP is 71.80%,which is 5%-10%higher than that of other methods.For small defects below 64×64 pixels,the mAP of the method reaches 64.15%,which is 14%higher than that of YOLOv3-GIoU.The surface defects of the workpiece can be effectively detected by the proposed method,and the performance in detecting small defects is significantly improved.

Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

A modified time domain interpolation method for LS channel estimation in OFDM systems

A modified time domain interpolation method is proposed for orthogonal frequency division multiplexing(OFDM)systems to address the problem that time domain interpolation in the least square(LS)channel estimation method based on comb-type pilots cannot choose the pilot spacing flexibly.Firstly,the estimated channel frequency response(CFR)at pilot positions in the frequency domain is obtained by LS channel estimation based on comb-type pilots,and the estimated channel impulse response(CIR)in the time domain is obtained by linear interpolation and inverse fast Fourier transform(IFFT).Secondly,the error of the estimated CIR obtained by linear interpolation is analyzed by theoretical deduction,and a method for correcting it is proposed.Finally,an estimated CFR at all subcarrier positions in the frequency domain is obtained by performing zero padding in the time domain and fast Fourier transform(FFT)on the modified CIR.The simulation results suggest that the proposed method gives similar performance to time domain interpolation,yet it does not need to meet the condition of time domain interpolation that the number of subcarriers must be an integral multiple of pilot spacing to use it.The proposed method allows for flexible pilot spacing,reducing the number of pilots and the consumption of subcarriers used for channel estimation.

Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles

To offset the defect of the traditional state of charge(SOC)estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter(MVEKF)algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test(DST)demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5%at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.

Human-machine compatibility and dynamic analysis of a novel unpowered and self-adaptive shoulder rehabilitation exoskeleton

To reduce the complexity of the configuration and control strategy for shoulder rehabilitation exoskeleton,a 2R1R1P2R serial of shoulder exoskeleton based on gravity balance is proposed.Based on three basic rotatory shoulder joints,an exact kinematic constraint system can be formed between the exoskeleton and the upper arm by introducing a passive sliding pair and a center of glenohumeral(CGH)unpowered compensation mechanism,which realizes the human-machine kinematic compatibility.Gravity balance is used in the CGH compensation mechanism to provide shoulder joint support.Meanwhile,the motion of the compensation mechanism is pulled by doing reverse leading through the arm to realize the kinematic self-adaptive,which decreases control complexity.Besides,a simple and intuitive spring adjustment strategy is proposed to ensure the gravity balance of any prescribed quality.Furthermore,according to the influencing factors analysis of the scapulohumeral rhythm,the kinematic analysis of CGH mechanism is performed,which shows that the mechanism can fit the trajectory of CGH under various conditions.Finally,the dynamic simulation of the mechanism is carried out.Results indicate that the compensation torques are reduced to below 0.22 N·m,and the feasibility of the mechanism is also verified.

Comparative study on tribological properties of isostatic graphite and carbon graphite under dry sliding and water-lubricated conditions

The tribological properties of isostatic graphite and carbon graphite under dry sliding and water lubricated conditions were studied.The friction test was conducted by using a pin-on-disc tribometer.The friction coefficient and the wear rate were employed to evaluate the tribological performances of the two materials,and wear morphology was used to analyze the wear mechanism.The results show that the friction coefficient of the isostatic graphite is larger than that of the carbon graphite under the dry sliding condition,and the wear rate is lower than that of the carbon graphite.Under the water lubricated condition,the friction coefficients and the wear rates of the isostatic graphite decrease obviously.The main wear form of the isostatic graphite is abrasive wear,while the main wear form of the carbon graphite is spalling wear.Finally,the tribological mechanism of the isostatic graphite under dry sliding and water lubricated conditions were systematically analyzed.

Analysis and Control of Surface Delamination Defects During Milling of Orthogonal Aramid Fiber‑Reinforced Composites Laminates

The aramid fiber-reinforced composites(AFRC)can increase the durability of corresponding applications such as aerospace,automobile and other large structural parts,due to the improvement in hardness,heat build-up,wear properties and green environmental protection.However,because of its complex multiphase structure and unique heterogeneity and anisotropy,the poor compression fatigue resistance and the incident surface fibrillation are inevitable.To improve the assembly precision of AFRC,mechanical processing is necessary to meet the dimensional accuracy.This paper focuses on the influence of contour milling parameters on delamination defects during milling of AFRC laminates.A series of milling experiments are conducted and two different kinds of delamination defects including tearing delamination and uncut-off delamination are investigated.A computing method and model based on brittle fracture for the two different types of delamination are established.The results can be used for explaining the mechanism and regularity of delamination defects.The control strategy of delamination defects and evaluation method of finished surface integrity are further discussed.The results are meaningful to optimize cutting parameters,and provide a clear understanding of surface defects control.

Evaluation of remaining useful life for corroded pipeline with finite element simulation and reliability theory

An integrated approach was proposed to evaluate the remaining useful life(RUL)of corroded petroleum pipelines.Two types of failure modes(i.e.,leakage and burst failure)were considered,and the corresponding limit state functions(LSFs)were established with the structural reliability theory.A power-law function was applied to model the growth of corrosion defects,and the effect of external environmental factors on the growth of the pipeline s defect was considered.Moreover,the result was compared with the commonly used linear growth model.After that,a finite element simulation model was established to calculate the burst pressure of the pipeline with corrosion defects,and its accuracy was verified through hydraulic burst test and by comparison with international criteria.On that basis,the probability that the pipeline may fail was calculated with Monte Carlo simulation(MCS)and by considering the LSFs,and the pipeline s RUL was obtained accordingly.Furthermore,sensitivity analysis was conducted to determine the sensitivity parameters for the corrosion and RUL of the pipeline.The results indicate that the radial corrosion rate,wall thickness and working pressure have a great influence on the failure probability of the pipeline.Thus,corresponding measures should be adopted during the operation process of the pipeline to reduce the corrosion rate and increase the wall thickness,so as to prolong the pipeline s RUL.

Nonlinear identification and characterization of structural joints based on vibration transmissibility

In order to investigate the nonlinear characteristics of structural joint,the experimental setup with a jointed mass system is established for dynamic characterization analysis and vibration prediction,and a corresponding nonlinearity identification method is studied.First,the sine-sweep vibration test with different baseexcitation levels areapplied to the structural joint system to study the dominant modal of mass rigid motion.Then,based on t e harmonic balance method principle,t e measured vibration transmissibilities a e utilized for nonlinearity identification using different excitation levels.Experimental results show that nonlinear spring and damping force can be represented by a polynomial order approximation.The identified nonlinear stiffness and damping force can predict the system’s response,and they can reveal t e shifts of resonant frequency or damping due to discontinuity of contact mechanisms within a certain range.

Influence of image data set noise on classification with a convolutional network

To evaluate the influence of data set noise, the network in network(NIN) model is introduced and the negative effects of different types and proportions of noise on deep convolutional models are studied. Different types and proportions of data noise are added to two reference data sets, Cifar-10 and Cifar-100. Then, this data containing noise is used to train deep convolutional models and classify the validation data set. The experimental results show that the noise in the data set has obvious adverse effects on deep convolutional network classification models. The adverse effects of random noise are small, but the cross-category noise among categories can significantly reduce the recognition ability of the model. Therefore, a solution is proposed to improve the quality of the data sets that are mixed into a single noise category. The model trained with a data set containing noise is used to evaluate the current training data and reclassify the categories of the anomalies to form a new data set. Repeating the above steps can greatly reduce the noise ratio, so the influence of cross-category noise can be effectively avoided.

Ride quality evaluation of the soil compactor cab supplemented by auxiliary hydraulic mounts via simulation and experiment

In order to evaluate the ride quality of the soil compactor cab supplemented by the auxiliary hydraulic mounts (AHM), a nonlinear dynamic model of the soil compactor interacting with the off-road deformable terrain is established based on Matlab/Simulink sofware. The power spectral density (PSD) and the weighted root mean square (RMS) of acceleration responses of the vertical driver s seat, the cab s pitch and roll angle are chosen as objective functions in low-frequency range. Experimental investigation is also used to verify the accuracy of the model. The influence of the damping coefficients of the AHM on the cab s ride quality is analyzed, and damping coefficients are then optimized via a genetic algorithm program. The research results show that the cab s rubber mounts added by the AHM clearly improve the ride quality under various operating conditions. Particularly, with the optimal damping coefficients of the front-end mounts c a 1,2 = 1 500 N · s/m and of the rear-end mounts c a 3,4 =2 335 N · s/m, the weighted RMS values of the driver s seat, the cab s pitch and roll angle are reduced by 22.2%, 18.8%, 58.7%, respectively. Under the condition of the vehicle travelling, with the optimal damping coefficients of c a 1,2 = 1 500 N · s/m and c a 3,4 =1 882 N · s/m, the maximum PSD values of the driver s seat, the cab s pitch and roll angle are clearly decreased by 36.7%, 54.7% and 50.6% under the condition of the vehicle working.