Optical measurement techniques are often employed to digitally capture three dimensional shapes of components. The digital data density output from these probes range from a few discrete points to exceeding millions of points in the point cloud. The point cloud taken as a whole represents a discretized measurement of the actual 3D shape of the surface of the component inspected to the measurement resolution of the sensor. Embedded within the measurement are the various features of the part that make up its overall shape. Part designers are often interested in the feature information since those relate directly to part function and to the analytical models used to develop the part design. Furthermore, tolerances are added to these dimensional features, making their extraction a requirement for the manufacturing quality plan of the product. The task of “extracting” these design features from the point cloud is a post processing task. Due to measurement repeatability and cycle time requirements often automated feature extraction from measurement data is required. The presence of non-ideal features such as high frequency optical noise and surface roughness can significantly complicate this feature extraction process. This research describes a robust process for extracting linear and arc segments from general 2D point clouds, to a prescribed tolerance. The feature extraction process generates the topology, specifically the number of linear and arc segments, and the geometry equations of the linear and arc segments automatically from the input 2D point clouds. This general feature extraction methodology has been employed as an integral part of the automated post processing algorithms of 3D data of fine features.
It is very difficult to measure the inner profile geometry of small holes of less than a millimeter in size, yet that geometry may be important for some manufacturing operations. This paper will present a method to measure key dimensional parameters of small holes used in a variety of applications from cooling to lubrication. Precision shaped holes can consist of a hole at some angle to the surface of the part and an area around the entrance to the hole for the purpose of diffusing the air or lubricant across the surface of the part to achieve the most effective performance. The drive towards smaller and more complex hole geometries means that previous methods such as conventional touch probes do not provide a good mapping in a time that can be used as part of production. The advanced designs of the holes means simple pin gages do not provide enough information. This paper will discuss tests of various methods considered for mapping small hole inner diameters, and present some sample results of a possible solution.
Due to the high temperatures and stresses present in the high-pressure section of a gas turbine, the airfoils
experience creep or radial stretching. Nowadays manufacturers are putting in place condition-based maintenance
programs in which the condition of individual components is assessed to determine their remaining lives. To
accurately track this creep effect and predict the impact on part life, the ability to accurately assess creep has become
an important engineering challenge. One approach for measuring creep is using moiré imaging. Using pad-print
technology, a grating pattern can be directly printed on a turbine bucket, and it compares against a reference pattern
built in the creep measurement system to create moiré interference pattern. The authors assembled a creep
measurement prototype for this application. By measuring the frequency change of the moiré fringes, it is then
possible to determine the local creep distribution. However, since the sensitivity requirement for the creep
measurement is very stringent (0.1 micron), the measurement result can be easily offset due to optical system
aberrations, tilts and magnification. In this paper, a mechanical specimen subjected to a tensile test to induce plastic
deformation up to 4% in the gage was used to evaluate the system. The results show some offset compared to the
readings from a strain gage and an extensometer. By using a new grating pattern with two subset patterns, it was
possible to correct these offset errors.
Moiré imaging has been used to measure creep in the airfoil section of gas turbine blades. The ability to accurately
assess creep and other failure modes has become an important engineering challenge, because gas turbine
manufacturers are putting in place condition-based maintenance programs. In such maintenance programs, the
condition of individual components is assessed to determine their remaining lives. Using pad-print technology, a
grating pattern was printed directly on a turbine blade for localized creep detection using the spacing change of
moiré pattern fringes. A creep measurement prototype was assembled for this application which contained a lens,
reference grating, camera and lighting module. This prototype comprised a bench-top camera system that can read
moiré patterns from the turbine blade sensor at shutdown to determine creep level in individual parts by analyzing
the moiré fringes. Sensitivity analyses and noise factor studies were performed to evaluate the system. Analysis
software was also developed. A correlation study with strain gages was performed and the measurement results
from the moiré system align well with the strain gage readings. A mechanical specimen subjected to a one cycle
tensile test at high temperature to induce plastic deformation in the gage was used to evaluate the system and the
result of this test exhibited good correlation to extensometer readings.
In this paper, the design and evaluation of a 3D stereo, near infrared (IR), defect mapping system for CZT inspection is
described. This system provides rapid acquisition and data analysis that result in detailed mapping of CZT crystal defects
across the area of wafers up to 100 millimeter diameter and through thicknesses of up to 20 millimeter. In this paper,
system characterization has been performed including a close evaluation of the bright field and dark field illumination
configurations for both wafer-scale and tile-scale inspection. A comparison of microscope image and IR image for the
same sample is performed. As a result, the IR inspection system has successfully demonstrated the capability of
detecting and localizing inclusions within minutes for a whole CZT wafer. Important information is provided for
selecting defect free areas out of a wafer and thereby ensuring the quality of the tile. This system would support the CZT
wafer dicing and assembly techniques that enable the economical production of CZT detectors. This capability can
improve the yield and reduce the cost of the thick detector devices that are rarely produced today.
Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.