一种基于峰谷数据映射的粗糙度轮廓重构方法

A Reconstruction Method for Roughness Profile Based on Data Mapping of Peak and Valley

表面粗糙度具有较强随机与无序性,如何有效地模拟其统计特征是开展微尺度界面使役性能预测与优化的前提。为解决现有重构方法在效率、精度和稳定性方面无法兼备的问题,以采样峰谷分布之间数据映射关系为特征载体,提出一种可保留粗糙度形貌和统计参数、微凸体分布的重构新方法。针对理想高斯和实测非高斯加工表面,对所提方法重构效果进行了讨论,并与现有重构方法进行了对比分析。结果表明:所提方法优化了峰谷映射过程,克服了在自相关分布形式、相关长度和Johnson转换等方面的限制,能适应各种不同类型表面的重构,较其他方法在重构效率、精度和稳定性方面均具显著优势。

As surface roughness has high randomness and disorder,how to effectively simulate its statistical characteristics becomes the primary prerequisite for the prediction and optimization of interface performance at micro-scale.In order to solve the problem that existing methods cannot satisfy efficiency,accuracy and stability simultaneously,a new reconstruction method is put forward.The proposed method takes sampled data mapping relationship between peak and valley distributions as characteristic carrier and can preserve roughness topography parameters,statistical parameters and asperity distribution.The performance of the proposed method is discussed and is compared with those of existing methods for ideal Gaussian and measured non Gaussian machined surfaces.The results show that the proposed method optimizes the peak-valley mapping process and overcomes the limitations on autocorrelation function form,correlation length and Johnson transformation.In consequence,the proposed method can simulate different types of surfaces and has significant advantages over other methods in efficiency,accuracy and stability.