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Surface texture and titanium nitride (TiN) coating have been established as effective methods for enhancing the tribological property of mechanical friction pairs. This study aims to investigate the tribological performance of dimple-textured surfaces with TiN coating under oil-lubricated conditions using a pin-on-disc wear experiment. Four types of pin samples with various end surfaces were designed, including bare rod samples, TiN-coated samples, textured samples, and TiN-coated/textured samples. The surface texture consists of a series of cylindrical micro-dimples with a diameter of 150 μm and a depth of 40 μm fabricated on the end surface of the pin. TiN coating treatment on the textured surface of the end face was performed by vacuum sputtering coating equipment. The study focuses on measuring and comparatively analyzing the friction coefficient, wear morphology, and binding force of the pin-disc friction pairs among the experiments. Compared with bare rod samples, TiN-coated/textured samples will reduce the friction coefficient (COF) of the pin-on-disc friction pair by at least 20% under oil-lubricated conditions in a 50 N normal contact load. The results indicate that the synergistic effect of dimple surface texture and TiN coating optimizes friction performance and reduces wear, highlighting the novelty of this study. Furthermore, the study identifies the hydrodynamic lubrication effect of the surface morphology formed by the dimple surface texture as a key factor in improving lubrication performance and reducing friction. Additionally, the dimple surface texture enables the mitigation of third body wear due to the wear debris storage function of the micro-dimples. This research provides valuable insights for the design and fabrication of mechanical friction pairs with high wear resistance under oil-lubricated conditions. [ABSTRACT FROM AUTHOR]

Boiling heat transfer on nanostructured surfaces presents great potential in cooling highly integrated microelectronic devices. Analysis of the factors affecting boiling heat transfer included the analysis of nanostructure and wettability, indicating that consideration of the influence of working liquid quantity is essential in finite spaces. Rapid boiling water films with various thicknesses placed on the same nanochannel copper plate were studied via molecular dynamics (MD) simulations. The simulation results reveal that the potential energy difference in the vapor–liquid coexisting region on the nanochannels was lower for thicker films, and the evaporation rate was lower. The effect of water film thickness on boiling heat transfer is closely related to the potential energy difference in the vapor–liquid coexisting region on the nanochannels. The heat transfer effect was the worst in case 1, where the water thickness was lower than the height of the nanochannels. This is because there is no guaranteed liquid replenishment at the nucleation points, although the potential energy difference was greatest in the vapor–liquid coexistence zone of case 1. Evaporation was the greatest in case 2, where the water film just covered the nanochannels because of the larger potential energy difference and sufficient liquid water replenishment. This study is of great significance for the analysis of the vapor–liquid flow mechanisms of micro/nanostructured surfaces and the improved design of thermal management equipment of micro/nano devices. [ABSTRACT FROM AUTHOR]

The influences of interactions between copper plate and nano-water droplets on wettability with different solid–liquid potential parameters were studied, and the spread of droplets was compared and analyzed using the molecular dynamics method. The diameter of droplet spreading and the amount of water molecules adsorbed on the copper gradually increased with the potential parameters, whereas the absolute potential energy value between copper and water also increased. Negative potential energy represents the attraction force between solid atoms and liquid molecules, and the attraction grows stronger with the large absolute value of negative potential energy. The heat transfer performance of the wettability surface can be explained by analyzing the force of solid and liquid from the perspective of energy. These results are of great significance for establishing the wettability model of a smooth plate correctly and the design and manufacture of a special functional surface. [ABSTRACT FROM AUTHOR]

In this paper, the interaction mechanism between the solid–liquid–gas interface phenomenon caused by nanostructure and surface wettability and boiling heat transfer is described, and the heat transfer theory of single wettable nanostructure surface and mixed wettable nanostructure surface is proposed. Through molecular dynamics simulation, the thermodynamic model of the wettable surface of nanostructures is established. The nanostructures are set as four rectangular lattice structures with a height of 18 Å. The solid atoms are platinum atoms, and the liquid atoms are argon atoms. The simulation results show that with the increase of surface hydrophilicity of nanostructures, the fluid temperature increases significantly, and the heat transfer at the interface is enhanced. With the increase in surface hydrophobicity of nanostructures, the atoms staying on the surface of nanostructures are affected by the hydrophobicity, showing a phenomenon of exclusion, and the evaporation rate in the evaporation area of nanostructures is significantly increased. In addition, the mixed wettable surface is influenced by the atomic potential energy and kinetic energy of the solid surface, and when compared with the pure wettable surface under the nanostructure, it changes the diffusion behavior of argon atoms on the nanostructure surface, enhances the heat transfer phenomenon compared with the pure hydrophobic surface, and enhances the evaporation phenomenon compared with the pure hydrophilic surface. This study provides insights into the relationship between the vapor film and the heating surface with mixed wettability and nanostructures. [ABSTRACT FROM AUTHOR]

Texture and coating technology can significantly improve the tribological properties of mechanical components. In this study, the lubricating and wear properties of the reticular surface texture under the action of TiN were studied. Lubrication and wear experiments at different speeds were conducted using a UMT-3 wear and friction testing machine. Using Fluent fluid simulation, the bearing capacity of the oil film was obtained, and the lubrication performance of the texture was verified. The results showed that the simulation experiment and the lubrication experiment were consistent to a certain extent. For the groove width and angle parameters studied in this paper, optimal parameters existed to achieve the maximum bearing capacity, 1.27 N. Due to the high hardness and low elastic modulus of the TiN coating, the coated reticular texture was more wear-resistant, and it achieved the minimum wear volume 1.148 × 10−6 mm3 from the grinding stainless-steel matrix. The effect of the fluid dynamic pressure, wear debris collection, and lubricating oil storage were the main reasons for the increase in load-carrying capacity and the decrease in wear with the coated reticular texture. [ABSTRACT FROM AUTHOR]

N-ary gray-level (nGL) coding strategy is an effective method for absolute phase retrieval in the fringe projection technique. However, the conventional nGL method contains many unwrapping errors at the boundaries of codewords. In addition, the number of codewords is limited in only one pattern. Consequently, this paper proposes a new gray-level coding method based on half-period coding, which can improve both these two deficiencies. Specifically, we embed every period with a 2-bit codeword, instead of a 1-bit codeword. Then, special correction and decoding methods are proposed to correct the codewords and calculate the fringe orders, respectively. The proposed method can generate n2 codewords with n gray levels in one pattern. Moreover, this method is insensitive to moderate image blurring. Various experiments demonstrate the robustness and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the present work was to comparatively investigate the generation and characteristics of fretting and sliding wear debris produced by CuNiAl against 42CrMo4. Methods: Tribological tests were conducted employing a self-developed tribometer. Most experimental conditions were set the same except for the amplitudes and number of cycles. Morphological, chemical, microstructural and dimensional features of the worn area and debris were investigated using optical microscope (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) and a laser particle sizer. Outcomes: Not only wear scar profiles but also the wear debris color, distribution and generated amount under fretting and sliding wear modes were quite different, which can be attributed to the significant difference in wear mechanisms. Particle size analysis indicates that the fretting debris has a smaller size distribution range; the biggest detected fretting and sliding wear debris sizes were 141 μm and 355 μm, respectively. Both fretting and sliding debris are mainly composed of copper and its oxides, but the former shows a higher oxidation degree. [ABSTRACT FROM AUTHOR]

The traditional estimation methods of space targets pose are based on artificial features to match the transformation relationship between the image and the object model. With the explosion of deep learning technology, the approach based on deep neural networks (DNN) has significantly improved the performance of pose estimation. However, the current methods still have problems such as complex calculation, low accuracy, and poor real-time performance. Therefore, a new pose estimation algorithm is proposed in this paper. Firstly, the mask image of the target is obtained by the instance segmentation algorithm, and its point cloud image is obtained based on a depth map combined with camera parameters. Finally, the correlation among points is established to realize the prediction of pose based on multi-modal feature fusion. Experimental results in the YCB-Video dataset show that the proposed algorithm can recognize complex images at a speed of about 24 images per second with an accuracy of more than 80%. In conclusion, the proposed algorithm can realize fast pose estimation for complex stacked objects and has strong stability for different objects. [ABSTRACT FROM AUTHOR]

Object detection and segmentation can improve the accuracy of image recognition, but traditional methods can only extract the shallow information of the target, so the performance of algorithms is subject to many limitations. With the development of neural network technology, semantic segmentation algorithms based on deep learning can obtain the category information of each pixel. However, the algorithm cannot effectively distinguish each object of the same category, so YOLOMask, an instance segmentation algorithm based on complementary fusion network, is proposed in this paper. Experimental results on public data sets COCO2017 show that the proposed fusion network can accurately obtain the category and location information of each instance and has good real-time performance. [ABSTRACT FROM AUTHOR]

In this research, a novel vortex generator (VG) is presented. The experimental and numerical investigations were carried out to study the micro thermal-hydraulic performance in a heated tube. The numerical results showed that the fluid in the core flow region and the near-wall region was fully mixed because of the longitudinal vortices created by the vortex generators. In addition, the experimental results showed that the heat transfer coefficient (h) decreased with the increasing pitch ratio ( P R ) value, while the friction coefficient exhibited the opposite trend. With the increasing ration angle ( R A ) numbers, the h values decreased while the f numbers increased. In addition, the maximum and minimum values of the fraction ratio were 1.66 and 4.27, while these values of the Nusselt number ratio were 1.24 and 1.83. The maximum thermal enhancement factor (TEF) was 1.21 when P R   = 0.5, R A   = 0° and R e   = 9090. The heat transfer enhancement mechanism of the vortex generator is explained from the microscopic point of view. [ABSTRACT FROM AUTHOR]

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network is the most accurate. This research provides scientific and technical support for measuring and predicting small leaks. [ABSTRACT FROM AUTHOR]

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B = f A , which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system. [ABSTRACT FROM AUTHOR]

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed. [ABSTRACT FROM AUTHOR]

Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods. [ABSTRACT FROM AUTHOR]

Droplet generation process can directly affect process regulation and output performance of electrohydrodynamic jet (E-jet) printing in fabricating micro-to-nano scale functional structures. This paper proposes a numerical simulation model for whole process of droplet generation of E-jet printing based on the Taylor-Melcher leaky-dielectric model. The whole process of droplet generation is successfully simulated in one whole cycle, including Taylor cone generation, jet onset, jet break, and jet retraction. The feasibility and accuracy of the numerical simulation model is validated by a 30G stainless nozzle with inner diameter ~160 μm by E-jet printing experiments. Comparing numerical simulations and experimental results, period, velocity magnitude, four steps in an injection cycle, and shape of jet in each step are in good agreement. Further simulations are performed to reveal three design constraints against applied voltage, flow rate, and nozzle diameter, respectively. The established cone-jet numerical simulation model paves the way to investigate influences of process parameters and guide design of printheads for E-jet printing system with high performance in the future. [ABSTRACT FROM AUTHOR]