KEYWORDS: Data processing, Inspection, Feature extraction, Clouds, Data acquisition, Lead, 3D metrology, Detection and tracking algorithms, Statistical analysis, Denoising
The increase in awareness of the need to improve quality control on part machining efficiency has led to a great deal of
research aimed at cutting tool geometry analysis. This paper presents a framework of preprocessing point-based data and
extracting parameters after feature detection and data segmentation for cutting tool inspection, assuming unorganized
measurement data. The data processing method, including data decimating, smoothing, normal and curvature estimating,
denoising, sorting, as well as re-sampling, are exploited to meet the demands for high quality, data simplification for
geometric analysis. We will discuss the geometry analysis for parameter extraction, including key feature point
detection and key area segmentation based on general reverse engineering solutions and specific cutting tool
characteristics. Based on the presented simplification methods using virtual slicing and rotary axial projection data, some
cutting tool dimensional parameters can be extracted directly. Alternately, based on 2D points on a given cross section, a
plurality of curves can be generated, and optimized by minimizing deviations between the set of points and the plurality
of curves. Section parameters can then be extracted from the optimized curves. Furthermore, the methods and processes
of multi-section based spatial parameter extraction will be illustrated. This paper presents experimental results and field
tests. The experimental results show that the preprocessing is very robust and the parameter extraction results agree with
what is expected.
Phase shift analysis sensors are popular in inspection and metrology applications. The sensor's captured image contains the region of interest of an object overlaid with projected fringes. These fringes bend according to the surface topography. 3D data is then calculated using phase shift analysis. The image profile perpendicular to the fringes is assumed to be sinusoidal. A particular version of phase shift analysis is the image spatial phase stepping approach that requires only a single image for analysis, but it is sensitive to noise. When noise, such as surface texture, appears in the image, the sinusoidal behavior is partially lost. This causes an inaccurate or noisy measurement. In this study, three digital de-noising filters are evaluated. The intent is to retrieve a smoother sine-like image profile while precisely retaining fringe boundary locations. Four different edge types are used as test objects. "Six Sigma" statistical analysis tools are used to implement screening, optimization, and validation. The most effective enhancement algorithms of the selection comprise (1) line shifting followed by horizontal Gabor filtration and vertical Gaussian filtering for chamfer edge measurement and (2) edge orientation detection followed by 2-D Gabor filter for round edges. These algorithms significantly improve the gauge repeatability.
In industry, there are needs to accurately measure the 3-D profile of edges parts in order to evaluate edge
condition. Optical methods are increasingly used for this purpose due to its advantages such as being non-contact,
fast, accurate, and easy to integrate with software for data acquisition and feature analysis. We
utilized structured line projection technology to measure edge's profile. In this method a structured light
distribution, created by the transmission of a sinusoidal grating, was projected onto the inspected parts at a
certain incidence angle. The projected light lines were deformed due to the depth change on the edge
surface. A CCD camera sitting at a different angle was used to record the deformed fringes. From the
deformed fringes the 3D surface profile was extracted based on triangulation principal. Because the
projected grating pattern can be interpreted as an interference pattern, we used the spatial carrier phase
shifting and phase unwrapping method as in classic interferometry to extract the phase information from
the intensity distribution of fringes. Due to the limited depth-of-range of the fringe image and depth-of-focus
of the imaging lens on the CCD camera, the observed deformed fringes have different widths and
frequencies at different depth. According to the definition of depth-of-focus, the fringe width out of focus
can be on the order of the square root of 2 wider than that which is in focus. The width change can also be
due to a tilt across the object. This width change affects the accuracy of the spatial carrier phase shifting
and subsequently the accuracy of extracted profile. In this paper, we proposed to monitor the change of the
fringe width with imaging depth. According to the width of the fringes, we defined a parameter called
compressing rate, to use in computing the edge profile. For different edge types, the compressing rate was
optimized in order to get the profile that can match the results from traditional the methods. By using this
method, the system repeatability can be improved significantly.
Conference Committee Involvement (1)
Two- and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II
26 October 2004 | Philadelphia, Pennsylvania, United States
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