Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set (CPS) lack scientificity and unity, resulting in application confusion. The current CPS comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing CPS in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into CPS and redundant parameter sets (RPS) with the help of reconstructed surfaces and machining experiments, and the mapping relationships between CPS and RPS are established. The research shows that RPS accounts for 50%, and CPS, of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of CPS provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.

Roughness surfaces contact analysis is cutting-edge research in interface design. The 3D rough surface amplitude distribution characterized by height distribution parameters( Sq (root mean square), Ssk (skewness), Sku (kurtosis)) has a great influence on the extreme value and distribution of the interface contact stress. However, the relationship between height distribution parameters and surface maximum mises stress ( σ _max ) is still unclear and lacks of in-depth study. Through BP(Back Propagation) neural network, global sensitivity qualitative ( Morris ) and quantitative ( Sobol ) analysis methods, the relationship between Sq , Ssk , Sku and σ _max under different loads is studied. Based on complete polynomial and permutation combination method, the optimal correlation model between height distribution parameters and σ _max was established, and particle swarm algorithm was introduced to analyze σ _max extreme values under different Sq . The results show that: (1) Under different loads, the order about height distribution parameters influence on surface contact stress is: Sq   > Ssk   > Sku , and as the load increases, the influence of Ssk and Sku gradually decreases. (2) In different roughness surfaces, the influence of Ssk and Sku on the contact performance is significantly full of discrepancy. The research results provide reference and technical support for active design of rough surface microstructure to improved contact performance.

With surface roughness restricted by grinding parameters, the characterization of roughness parameters and the inversion of grinding parameters are of great significance for improving surface performance and realizing active surface machining. This research proposes a combination of statistical theory and data-driven analysis to solve the above problems. Pearson correlation analysis and multivariate variance analysis indicate the correlation characterization parameter set (CPS) consists of Sa, Vmp, Vvv, and Sz and that there are differences in the influence of grinding parameters on the parameters in CPS. Adjustment of support vector machine (SVM) core parameters makes it possible to construct expansion parameter set (EPS) optimal inversion models. By designing pseudo-surface random roughness parameters and grinding experiments, the reliability of inversion models is verified. The results show: (1) The better generalization of inversion model indicates skewness Ssk and kurtosis Sku in EPS have important implications for the optimal inversion model and surface characterization and (2) The data-driven model based on support vector machine provides machining guidance for obtaining the expected ultrasonic grinding surface.

Shot peening is the efficient method for metal surface modification and performance improvement. However, there is still no effective way to establishing the mathematical model for shot peening surface reconstruction, resulting in restricting the correlation study between shot peening surface properties and morphology. The difficulty of shot peening surface reconstruction lies in how to accurately characterize the roughness surface height and texture features. Therefore, a novel method of generating non-Gaussian sequences with specified height roughness parameters is proposed in this paper. Fast Fourier transform (FFT) method gets improved combined with the new method, which overcome the predicament that the unimproved FFT cannot ensure the height features in the reconstructed shot peening surfaces. In addition, a new autocorrelation function in shot peening surfaces is proposed to accurately characterize the reconstructed surface texture. The experimental results show that with the improved FFT method and the new autocorrelation function introduced to shot peening surface reconstruction, the maximum error of the seven height roughness parameters in the reconstructed surface is 2.201%. And the texture features and height distribution of the reconstructed surface are in good agreement with the measured surface.

Surface roughness parameters play a decisive role on grinding surface service performance. Among 26 surface roughness parameters, closely related to the surface performance and defined by ISO25178, in the 6 categories, numerical reconstruction of grinding surfaces can only control 7 height parameters and 2 spatial parameters with inability to achieve precise control of other parameter-defined features. Therefore, a novel numerical reconstruction method for grinding surfaces with specified rough parameter set (SRPS) was proposed to solve the problem of feature loss caused by other uncontrollable parameters. Combine three characteristic coefficients of height probability density function in Johnson transformation method with four characteristic coefficients of autocorrelation function expression to construct surface reconstruction coefficient set (SRCS) and build SRPS with number of 22 in consideration of the measurement error influence and industrial application frequency. Use BP neural network to establish quantitative mapping model between SRCS and SRPS for inversion. Through introducing genetic algorithm to invert SRPS corresponding SRCS, numerical reconstruction of grinding surfaces with SRPS will be realized. Experimental results show that compared with the measured grinding surfaces, the average error in SRPS of reconstructed surfaces is basically within 10%. The research provides a new means for the study of the surface performance, which solves the problem that the traditional rough surface modeling method based on random process theory could only associate 9 roughness parameters.

Contact pattern measurement is commonly applied to assess gear transmission and meshing performance in aviation spiral bevel gear (ASBG) manufacturing enterprises. However, these patterns cannot be directly segmented and obtained from point clouds or images because each tooth of the spiral bevel gear has complex 3D spiral surface and texture information. Moreover, research on the high-precision 3D colour reconstruction of spiral gear tooth surfaces is relatively rare. Aimed at these problems, the current work proposed a high-precision 3D colour reconstruction method of spiral bevel gear. This method is based on the fusion of the image and point-cloud information. It includes two core steps to satisfy the practical demands of high precision and high efficiency. First, dense point clouds of the 3D calibration block are converted to a plane using the bird’s eye view method for corner detection. Second, the corners are quickly and accurately detected using a two-stage corner detection algorithm based on the inherent geometric features of the calibration block. The identified corners are then used to solve the transformation matrix from the pixel coordinate system to the line laser coordinate system. Image textures can be precisely projected and fused onto a 3D geometry for the coloured reconstruction of the gear tooth surface on the basis of this transformation matrix. The proposed method was validated by applying it to the 3D colour reconstruction of tooth surfaces of 10 distinct types of ASBGs. The experimental results demonstrated that the re-projection error of the proposed method is less than 0.1 millimetres. The minimal margin of error enables high-precision 3D colour reconstruction of the spiral bevel gear tooth surface. Compared to other classic 3D colour reconstruction methods, the proposed method achieves superior accuracy and efficiency. An experimental device was also developed for the 3D colour reconstruction of the tooth surface of ASBGs, as illustrated in figure 1.