On the linear transform technique for generating rough surfaces

Li, Lin; Tang, Jinyuan; Ding, Han; Liao, Dongri; Lei, Duncai

doi:10.1016/j.triboint.2021.107182

Cited by 10 publications

(8 citation statements)

References 41 publications

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“…According to the Johnson transformation method [ 25 ], the height probability density function can be obtained by three characteristic coefficients, while the autocorrelation function is controlled by four characteristic coefficients. These coefficients are defined as follows: Characteristic coefficient of height probability density function …”

Section: Basic Concepts and Research Methodsmentioning

confidence: 99%

Rough Surface Characterization Parameter Set and Redundant Parameter Set for Surface Modeling and Performance Research

et al. 2022

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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.

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Section: Basic Concepts and Research Methodsmentioning

confidence: 99%

Rough Surface Characterization Parameter Set and Redundant Parameter Set for Surface Modeling and Performance Research

et al. 2022

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“…Since Nayak [12] introduced the random process theory to reconstruct rough surfaces, two main methods about rough surfaces reconstruction have been developed: the linear transformation method [13,19,30] and the fast Fourier transform method [14][15][16]. Both two methods use the height probability density function and the autocorrelation function to characterize the surface topography.…”

Section: Model Algorithm 231 Random Process Methodsmentioning

confidence: 99%

“…The fast Fourier transform method is based on the time-frequency model [20] with the fast Fourier transform introduced to accelerate the solution of the height matrix. Compared with the linear transformation method, the accuracy of the fast Fourier transform method is slightly lower but with hundreds or even thousands of times faster than the linear transformation method [30], which is more suitable for the engineering. Taking efficiency and accuracy into conside-ration, the research adopts the fast Fourier transform method as the target.…”

Section: Model Algorithm 231 Random Process Methodsmentioning

confidence: 99%

Novel method for numerical reconstruction of grinding surfaces with specified roughness parameter set

Tang¹,

Xia²,

Yang³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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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.

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“…Shortly, the error problem in the Liao's model is further improved by using a symmetric linear transformation matrix [20] . Finally, the linear filtered reconstruction problems for rough surfaces with arbitrary texture directions are combined into a unified model [21] that is very accurate but time-consuming to solve compared to the FFT method.…”

Section: Overviewmentioning

confidence: 99%