Correlation analysis of roughness surface height distribution parameters and maximum mises stress

Abstract

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 (σmax) is still unclear and lacks of in-depth study. Through BP neural network, global sensitivity qualitative (Morris) and quantitative (Sobol) analysis methods, the relationship between Sq, Ssk, Sku and σmaxunder different loads is studied. Based on complete polynomial and permutation combination method, the optimal correlation model between height distribution parameters and σmax was established, and particle swarm algorithm was introduced to analyze σmax 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.

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

“…The polynomial nonlinear regression model is the data method with strong nonlinear fitting ability. It is applicable to the situation where the correlation between variables is not clear, so as to explore the influence of variables and realize the explicit expression of independent variables to dependent variables [ 31 , 32 ]. Here explains how it works:…”

“…However, when the polynomial nonlinear regression model is directly used for the research, based on the total number of items in the model, the increase of the independent variables will lead to an explosive growth of the items, and it is easy to produce many redundant terms. These redundant terms are meaningless to improve the accuracy of the model, so the number of terms should be reasonably pruned [ 32 , 33 ] to establish the optimal explicit expression.…”