Haridas Aswin, Prabhathan P, Pulkit K, Chan Kelvin, Murukeshan V M
Appl Opt. 2020 Jun 1;59(16):5041-5051. doi: 10.1364/AO.389227.
Measurement of surface roughness over a large area is a very challenging task due to the limitations with the existing techniques. Surface roughness measurement techniques including stylus and microscopy are limited by point-by-point data acquisition and a small field of view (FOV). In effect, any solution that would subdue these limitations would be characterized by its full-field nature, large FOV, and the ability to acquire and process data at high speeds. To meet these requirements, large area speckle imaging has been used to obtain areal surface roughness parameters through the processing of spectrally correlated speckle images. An automated optical system is developed for surface roughness evaluation of components with large and curved surface areas. In order to extract areal surface roughness parameters from the captured set of images, processing algorithms are developed. The methodology is first validated using a comparator plate containing areas having an average surface roughness (Ra) ranging between 0.2 µm and 0.6 µm. Further, statistical significance tests are conducted to determine the main factors affecting system performance. The measurement results are compared and validated using a 3D optical microscope. The results obtained from the blind tests performed on aerospace component surfaces as large as 450mm×210mm are also presented.
由于现有技术的局限性，大面积表面粗糙度的测量是一项极具挑战性的任务。包括触针式和显微镜在内的表面粗糙度测量技术受到逐点数据采集和小视场（FOV）的限制。实际上，任何能够克服这些局限性的解决方案都将具有全场特性、大视场以及高速采集和处理数据的能力。为了满足这些要求，大面积散斑成像已被用于通过处理光谱相关的散斑图像来获取表面粗糙度参数。开发了一种自动光学系统，用于评估具有大曲面面积的部件的表面粗糙度。为了从捕获的图像集中提取表面粗糙度参数，开发了处理算法。该方法首先使用包含平均表面粗糙度（Ra）在0.2 µm至0.6 µm之间区域的比较板进行验证。此外，进行统计显著性测试以确定影响系统性能的主要因素。使用3D光学显微镜对测量结果进行比较和验证。还展示了在尺寸达450mm×210mm的航空航天部件表面进行的盲测所获得的结果。
