Surface texture was prepared on the ASTM1045 steel substrate before spraying. Three texturing patterns (groove pattern, square pattern, and hexagon pattern) were acquired by laser processing to investigate the influence of texturing patterns on the adhesion strength of sprayed coatings. The Ni60 coatings were prepared on the textured surface by atmosphere plasma spraying technology. Scanning electron microscopy and laser 3D microscope were used to characterize the morphology of texturing. The adhesion strength between coating and substrate was examined by the tensile test. Image pro plus software was used to calculate the contact area ratio of the textured substrate. The results show that the texturing processed substrates by laser radiation present plain area between two dimples and the protrusion around texturing, and the contact area between coating and substrate is increased. The adhesion strength of coatings with a groove pattern, a square pattern, and a hexagon pattern is 58, 33, and 47 MPa, respectively. The adhesion strength of sprayed coatings varies with the change of the texturing pattern, and it does not only depend on the contact area ratio but also on the texturing density and the texturing microstructure.

In this study, surface texturing and hydrogenated amorphous carbon (a-C:H) diamond-like carbon (DLC) coating was combined to evaluate the coating performance at various temperatures in oil lubricated reciprocating sliding tests. Micro dimples were created by laser surface texturing on M2 steel using a Pico second laser. DLC coating was deposited by hybrid magnetron sputtering on textured substrates. Textured a-C:H showed stable coefficient of friction at 30, 80, and 125 °C compared to un-textured a-C:H. At 30 °C, graphitization was not observed for both textured and un-textured DLC coating. Graphitization was more pronounced in the case of un-textured a-C:H at 80 and 125 °C. Results show that, at all temperatures tested (30–125 °C), DLC textured samples showed higher wear resistance compared to un-textured DLC coating. The improvement in wear resistance can be explained by the lower graphitization of textured DLC coating. Lower graphitization in the case of textured DLC might be due to the wear particle capturing and lubricant retention ability of textures.

Contours and surface textures provide powerful cues used in image segmentation and the analysis of object shape. To learn more about how the visual system extracts and represents these visual cues, we studied the responses of V2 neurons in awake, fixating monkeys to complex contour stimuli (angles, intersections, arcs, and circles) and texture patterns such as non-Cartesian gratings, along with conventional bars and sinusoidal gratings. Substantial proportions of V2 cells conveyed information about many contour and texture characteristics associated with our stimuli, including shape, size, orientation, and spatial frequency. However, the cells differed considerably in terms of their degree of selectivity for the various stimulus characteristics. On average, V2 cells responded better to grating stimuli but were more selective for contour stimuli. Metric multidimensional scaling and principal components analysis showed that, as a population, V2 cells show strong correlations in how they respond to different stimulus types. The first two and five principal components accounted for 69% and 85% of the overall response variation, respectively, suggesting that the response correlations simplified the population representation of shape information with relatively little loss of information. Moreover, smaller random subsets of the population carried response correlation patterns very similar to the population as a whole, indicating that the response correlations were a widespread property of V2 cells. Thus, V2 cells extract information about a number of higher order shape cues related to contours and surface textures and about similarities among many of these shape cues. This may reflect an efficient strategy of representing cues for image segmentation and object shape using finite neuronal resources.