Digitalized simulation method of any three-dimensional (3D) rough surface

Abstract

The invention discloses a digitalized simulation method of any three-dimensional (3D) rough surface, which improves simulation efficiency and simulation accuracy of statistical parameters. Random initial phase angle sequences are obtained through inverse Fourier transform of white noise sequences, and novel white noise and Fourier transform are obtained by utilizing the initial phase angle sequences. Processing including dispersion and Fourier transform and the like is performed through appointed autocorrelation functions to obtain power spectral density and transfer functions of Gaussian rough surface height sequences. The simulation of the Gaussian rough surface height sequences is finished by utilizing frequency domain dot product and a method for obtaining the inverse Fourier transform. On the basis, non-Gaussian rough surfaces are generated through height distribution statistical characteristic parameters including appointed skewness degree, kurtosis and the like and by utilizing combined Pearson and Johnson non-Gaussian transition systems. If the simulation accuracy of the skewness degree and the kurtosis is below standard, the phase angle sequences and the Fourier transform of the white noise are updated to re-perform Gaussian filtering and non-Gaussian transition till the given accuracy requirement is met.

Description

technical field [0001] The present invention relates to a digital simulation method for 3D microcosmic arbitrary rough surfaces. More specifically, the present invention can efficiently and accurately construct A rough surface with the same statistical characteristics. Background technique [0002] The development of high-speed and precision machining technology and the continuous improvement of product reliability requirements have promoted the rapid development of rough surface processing and manufacturing, testing, digital simulation and performance prediction. [0003] In recent years, domestic and foreign researches in the field of digital simulation of rough surfaces have mainly focused on rough surfaces whose roughness height conforms to Gaussian distribution. For Gaussian data, non-Gaussian transformation systems based on Johnson and Pearson have emerged. However, in the digital simulation of non-Gaussian rough surfaces, the non-Gaussian sequence after non-Gaussian...

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Problems solved by technology

[0006] Even if the target skewness and kurtosis meet the necessary conditions for non-Gaussian transformation: However, after considering the interaction between the filter coefficient and the autocorrelation function, S kη 、K uη It is possible that the necessary conditions for a non-Gaussian transformation are no longer satisfied: As a result, non-Gaussian sequences cannot be generated by non-Gaussian transformation systems such as Johnson or Pearson; even if they can be transformed, new errors will be introduced in the filtering stage, which will affect the accuracy of the digital simulation of the filtered non-Gaussian surface

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