Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
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DOI：
10.1016/j.precisioneng.2022.05.006
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Melih C. Yesilli;Jisheng Chen;Firas A. Khasawneh;Yang Guo
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作者：
Melih C. Yesilli;Jisheng Chen;Firas A. Khasawneh;Yang Guo
表面粗糙度和纹理对工程部件的功能性能至关重要。在许多表面生成过程中，如机械加工、表面机械处理等，需要有效和高效地分析粗糙度和纹理的能力。离散小波变换（DWT）和离散余弦变换（DCT）是表面粗糙度和纹理分析中常用的两种信号分解工具。这两种方法都需要选择一个阈值来将给定的表面分解为三个主要组成部分：形状、波浪度和粗糙度。然而，尽管DWT和DCT是ISO表面光洁度标准的一部分，但没有关于如何计算这些阈值的系统指导，它们通常是根据具体情况手动选择的。这使得利用这些方法来研究表面依赖于用户的判断，并限制了它们的自动化潜力。为此，我们提出了两种基于信息理论和信号能量的自动阈值选择算法。我们使用机器学习来验证我们的算法的成功，既使用模拟表面，也使用加工表面的数字显微镜图像。具体来说，我们为每个表面区域或轮廓生成特征向量并应用监督分类。将我们的结果与启发式阈值选择方法进行比较，结果表明，平均准确率高达95%。我们还将我们的结果与高斯滤波（GF）进行了比较，结果表明，虽然对于区域的GF结果可以产生略高的精度，但对于表面轮廓，我们的结果优于GF。我们进一步表明，我们的自动阈值选择在计算时间方面具有显着优势，与DCT的启发式阈值相比，将模式计算次数减少了一个数量级。
Surface roughness and texture are critical to the functional performance of engineering components. The ability to analyze roughness and texture effectively and efficiently is much needed to ensure surface quality in many surface generation processes, such as machining, surface mechanical treatment, etc. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are two commonly used signal decomposition tools for surface roughness and texture analysis. Both methods require selecting a threshold to decompose a given surface into its three main components: form, waviness, and roughness. However, although DWT and DCT are part of the ISO surface finish standards, there exists no systematic guidance on how to compute these thresholds, and they are often manually selected on case by case basis. This makes utilizing these methods for studying surfaces dependent on the user's judgment and limits their automation potential. Therefore, we present two automatic threshold selection algorithms based on information theory and signal energy. We use machine learning to validate the success of our algorithms both using simulated surfaces as well as digital microscopy images of machined surfaces. Specifically, we generate feature vectors for each surface area or profile and apply supervised classification. Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95%. We also compare our results with Gaussian filtering (GF), and show that while GF results for areas can yield slightly higher accuracies, our results outperform GF for surface profiles. We further show that our automatic threshold selection has significant advantages in terms of computational time as evidenced by decreasing the number of mode computations by an order of magnitude compared to the heuristic thresholding for DCT.