Titanium alloy is widely used in different industrial applications. For the surface modification to improve the tribological properties, laser irradiation is a promising technology, which offers high efficiency, and high automation potential and geometrical flexibility. In this study, a novel method of surface modification for titanium alloys by carbonization using low fluence of laser irradiation in the atmosphere of PAO oil is proposed. Results show that the carbon content on the surfaces significantly increases with the laser shot number, with the laser irradiated spot showing little change of Ra. XPS analysis confirms that the carbon from the oil has bonded to the Titanium inside the alloy. By comparison with that irradiated without oil, the hardness of that irradiated in oil is much higher, demonstrating the feasibility of the surface modification of titanium carbide layer generation. To investigate the tribological properties, laser scanning irradiation in oil with different laser pulses were carried out to create laser modified areas and reciprocating ball-on-disk friction tests under oil lubrication were conducted. The laser modified areas show friction of 0.13, much lower than that of the unirradiated which is approximate 0.55, and the sliding lifetime of low friction is also increased with the laser pulse number. Moreover, by introducing patterning laser irradiation onto the uniformly irradiated area, the wear resistance can be further greatly improved, and the sliding lifetime can extend to 13 times of the optimal result of uniform irradiation.

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To reduce the differences between the data scales and volume of multi-source cross-scale point cloud data， this study proposes a multi-scale decomposition method of point cloud data based on wavelet transform. This study examines the multi-scale decomposition of small-scale point cloud data with consid• erable attention and the application of scale decomposition in cross-scale point cloud data registration. First， the small-scale point cloud is grid modeled， and the global point cloud binary expression function is established. Subsequently， according to the theory of discrete wavelet transformation， three-dimensional wavelet decomposition of the grid point cloud is performed several times， and the low-pass characteristics 文章编号 1004-924X (2023)03-0340-12 收稿日期： 2022-06-29； 修订日期： 2022-08-02.52075238)； 航空科学基金资助项目(No. 2020Z050052002) 第 3 期 高凯元， 等： 基于小波变换的精密测量点云多尺度分解 of the wavelet scale function are used to retain the low-frequency information to obtain the approximate scale data of the original small-scale point cloud. The similarity with the original data is then characterized based on the surface dimension and the difference in body dimension， and the effective wavelet decomposi• tion series is determined. Finally， the point cloud data obtained by decomposition at various levels are ac• curately registered with the large-scale point cloud data， and the registration relationship is applied to the original point cloud to increase the registration accuracy of the cross-scale point cloud data. The experimen• tal results show that the multi-scale decomposition method proposed in this paper can effectively decom • pose the data. When applied to the multi-scale measurement of an aero-engine blade， the registration accu• racy of the local cooling holes small-scale point cloud data and the overall blade structure light data of mi• crometry increased by 61. 36%. The proposed decomposition method is applied to the multi-scale mea• surement of blade edge and grid parts， and the registration accuracy is increased by 48. 59% and 43. 86%， respectively. The proposed multi-scale decomposition method of the point cloud can effectively decom • pose small-scale point cloud data， and ultimately improve the registration accuracy of cross-scale data.

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A new algorithm is proposed about the research on classification assisted with Multi-user Detection based on MMSE. This paper is divided into two parts. Firstly, we analyzes the MMSE multi-user detection algorithm, and measures its performance through different SNR and other indicators. After that, a new signal modulation classification algorithm is proposed, which used the fuzzy density to fuse multiple classifiers and improve the reliability. And using the Matlab simulation to verify the performance.