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This paper aims to improve the anti-corrosive properties of aluminum alloy AA2024-T3 by coating of hybrid sol-gel coating incorporated with TiO₂ nanosheets and to investigate the effect of nanosheets’ size on the improvement of corrosion-resistant performance.

A series of hybrid sol-gel films incorporated with varying amounts of TiO2 nanosheets were developed to enhance the corrosion protection performance of the bare metal. Scanning electron microscopy, transmission electron microscopy and atomic force microscopy were used to investigate the structure and morphology of the coatings obtained. In addition, the corrosion-resistant properties of the coatings were evaluated using salt spray test and electrochemical impedance spectroscopy.

The corrosion current was as low as 9.55 × 10-4 µA/cm² and optimal positive corrosion potential reached −0.6 V when the size and loading amount of TiO₂ nanosheet were optimized, resulting in a remarkable improvement in anti-corrosive properties.

This work first investigates the effect of incorporation of TiO₂ nanoparticles on hybrid sol-gel coating on the improvement of anti-corrosive performance of aluminum alloy AA2024-T3.

This paper aims to investigate the effect of cerium modification and electrodeposition on the properties of the silane films obtained. Besides, the influence of dissolved oxygen was also researched through inhalation of oxygen or nitrogen. Moreover, the corresponding corrosion behavior of the silane films was also studied.

In this paper, surface morphology and corrosion-resistant properties of the films were characterized by scanning electron microscopy, electrochemical impedance spectroscopy, immersion test and the salt spray test.

The paper reveals that all the practical parameters including the concentration of dissolved oxygen had a marked effect on the anti-corrosive performance of the films, which may be attributed to the dense and compact structure of the films obtained. Furthermore, the self-healing ability of the films had also been enhanced through the rise of dissolved oxygen concentration in proper proportion.

This paper reveals the effect of practical parameters on the properties of the silane films obtained and the corrosion behavior of these films.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

Although high-order smooth reproducing kernel mesh-free approximation enables the analysis of structural vibrations in an efficient collocation formulation, there is still a lack of systematic theoretical accuracy assessment for such approach. The purpose of this paper is to present a detailed accuracy analysis for the reproducing kernel mesh-free collocation method regarding structural vibrations.

Both second-order problems such as one-dimensional (1D) rod and two-dimensional (2D) membrane and fourth-order problems such as Euler–Bernoulli beam and Kirchhoff plate are considered. Staring from a generic equation of motion deduced from the reproducing kernel mesh-free collocation method, a frequency error measure is rationally attained through properly introducing the consistency conditions of reproducing kernel mesh-free shape functions.

This paper reveals that for the second-order structural vibration problems, the frequency accuracy orders are p and (p − 1) for even and odd degree basis functions; for the fourth-order structural vibration problems, the frequency accuracy orders are (p − 2) and (p − 3) for even and odd degree basis functions, respectively, where p denotes the degree of the basis function used in mesh-free approximation.

A frequency accuracy estimation is achieved for the reproducing kernel mesh-free collocation analysis of structural vibrations, which can effectively underpin the practical applications of this method.

The impact of rolling on the performance of micro arc oxidation (MAO) coatings on ZM5 alloy has been underreported. The purpose of this study is to explore the correlation between rolling and the failure mechanism of MAO coatings in greater depth.

The influence of rolling on the corrosion and wear properties of MAO coating was investigated by phase structure, bond strength test (initial bond strength and wet adhesion), electrochemical impedance spectroscopy and wear test. The change of the surface electrochemical properties was studied by first principles analysis.

The results showed that the MAO coating on rolled alloy had better corrosion and wear resistance compared to cast alloy, although the structure and component content of two kinds of MAO coating are nearly identical. The difference in interface bonding between MAO coating and Mg substrate is the primary factor contributing to the disparity in performance between the two types of samples. Finally, the impact of the rolling process on MAO coating properties is explained through first-principle calculation.

A comprehensive explanation of the impact of the rolling process on MAO coating properties will provide substantial support for enhancing the application of Mg alloy anticorrosion.

The purpose of this paper is to propose a novel serial structure repetitive control scheme for shunt active power filter (SAPF) to improve the steady-state accuracy and dynamic performance of SAPF. The novelty of this scheme lies in the reconfiguration of the pole of repetitive control internal model, so that the dynamic response of the repetitive control is improved greatly.

By analyzing the mathematical model of repetitive control, the repetitive control delay can reduce by giving up the needless poles of the internal model, and the general mk + i repetitive control can be designed through the pole configuration method. The controller can track a set of specific order harmonics.

The experimental results are coincident with the theoretical analyses, which prove the effectiveness of the proposed method on harmonic suppression and great performance in dynamic response.

An APF prototype has been designed with the serial structure repetitive control proposed in this paper, and it can successfully eliminate the harmonics current of nonlinear load with faster dynamic response. Moreover, the proposed controller can be applied to any three-phase system for fast dynamic response and high tracking accuracy.

In this paper, the mathematical model of the repetitive control for specific set of harmonica is developed. A novel serial structure repetitive control is designed, so that the SAPF can eliminate the fundamental reactive current and specific order harmonics and speed up the dynamic response of the repetitive control.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud.

First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one.

In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully.

In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.