使用光谱分析的表面形貌的定量表征
Quantitative characterization of surface topography using spectral analysis
摘要：
粗糙度决定了表面的许多功能特性，如粘附力、摩擦力和（热和电）接触电导。最近的分析模型和模拟能够根据表面形貌的功率谱密度（PSD）知识定量预测这些属性。PSD的实用性在于它包含的统计信息不受研究人员选择的特定扫描尺寸和像素分辨率的影响。在这篇文章中，我们首先回顾PSD的数学定义，包括一维和二维的情况下，每个常见的变化。然后，我们讨论的策略，重建一个准确的PSD的表面形貌测量在不同的尺寸尺度。最后，我们讨论了在最小尺度上检测和减轻伪影，并计算从模型中获得的功能属性的上/下限。我们伴随着我们的讨论与虚拟测量计算机生成的表面。本讨论总结了如何分析形貌测量，以重建一个可靠的PSD。分析模型证明了通过合理调整表面形貌来调整功能特性的潜力-然而，这种潜力只能通过准确，定量重建真实世界表面的功率谱密度来实现。
英文摘要：
Roughness determines many functional properties of surfaces, such as adhesion, friction, and (thermal and electrical) contact conductance. Recent analytical models and simulations enable quantitative prediction of these properties from knowledge of the power spectral density (PSD) of the surface topography. The utility of the PSD is that it contains statistical information that is unbiased by the particular scan size and pixel resolution chosen by the researcher. In this article, we first review the mathematical definition of the PSD, including the one- and two-dimensional cases, and common variations of each. We then discuss strategies for reconstructing an accurate PSD of a surface using topography measurements at different size scales. Finally, we discuss detecting and mitigating artifacts at the smallest scales, and computing upper/lower bounds on functional properties obtained from models. We accompany our discussion with virtual measurements on computer-generated surfaces. This discussion summarizes how to analyze topography measurements to reconstruct a reliable PSD. Analytical models demonstrate the potential for tuning functional properties by rationally tailoring surface topography - however, this potential can only be achieved through the accurate, quantitative reconstruction of the power spectral density of real-world surfaces.
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