Reducing Noises in Digital Surface Inspection Using a Data Clustering Approach

Abstract

Abstract Online inspection of manufactured product surfaces is an essential task in today’s industry for the production or product quality control, and coordinate metrology is used for this purpose as the main inspection method in a large variety of manufacturing systems. Optical coordinate metrology which allows acquiring thousands of points from the product surface in a fraction of a minute, is utilized in online and dynamic surface measurement process. However, 3D data points i.e. Point Clouds (PCs) acquired using the optical devices are highly affected by various sources of noises. Hence PCs denoising is a crucial task before any use of PCs in product inspection. Most of the literatures deal with this issue by mesh denoising, which itself requires mesh generation from noise contaminated PCs. Therefore, raw PCs denoising seems a better procedure. To investigate detection of noisy points in PCs, two methodologies are presented in this paper. The methodologies are developed based on a common strategy of gradual learning and developing knowledge from the data points through iterative clustering processes. The developed learning procedures allow self-calibration and defining the clustering parameters. As a result, the clustering of the raw PCs will be conducted smartly considering the specific behaviors recognized in data-points. The developed methodologies are capable to detect noisy clusters for any unorganized data obtained from planer surfaces. The methodologies result in marking data points with high probability of noise presence. The two developed methodologies are compared and their effectiveness are studied. Variety of metrology sensors and inspection tools can adopt the developed methodologies for noise reduction or filtration and significant enhancement in inspection accuracy is expected to be acheived.