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Geometric Surface Feature Detection Using Statistical Based Metrics

Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechn...(2019)SCI 2区SCI 3区

Univ N Carolina

Cited 4|Views8
Abstract
The nature and quality of a component's surface affects its final functionality, expected life span, and perceived value. The economic advantage gained by achieving the desired surface quality drives interest in methods capable of detecting and quantifying surface topography features. This paper presents a new statistically based approach capable of providing insights on the geometric characteristics of a surface, and both detecting and quantifying isolated geometric surface features. In this approach an areal surface measurement, post removal of form and waviness components, is considered as a set of constituent profile measurements, i.e. each column of data is considered as a single profile. Analysis of this data set's variance can detect the presence of surface anisotropy, the spatial wavelength of repeating features, the presence of scratches and localized surface imperfections (dirt or digs). The paper outlines the method's capabilities and limitations, and draws parallels with surface texture parameters described within the ISO 25178-2 standard.
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Surface metrology,Feature characterization,Statistical analysis
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要点】:本文提出了一种基于统计学指标的几何表面特征检测方法,能够对表面质量特性进行检测和量化,具有在表面质量控制和制造流程优化中的潜在应用价值。

方法】:该方法通过将面域表面测量数据分解为构成轮廓测量数据,分析数据集的方差来检测表面各向异性、重复特征的波长、划痕和局部表面缺陷。

实验】:论文中未详细描述具体实验过程,但提出的方法与ISO 25178-2标准中的表面纹理参数进行了比较,实验所使用的数据集未明确提及,结果展示了方法在检测和量化表面特征方面的有效性。