Wear of railway wheels - especially for engines - has to be checked in regular intervals. For this check, some key dimensions of the cross section of the running surface like flange height, flange gradient or flange thickness have to be measured with accuracies in the range of 0.1 mm. State of the art is either the use of mechanical gauges or stationary optical measurement systems where the carriage has to be driven to a maintenance stand in a servicing centre.
We present a new technique for building up a small portable hand held measurement system, which allows to perform data acquisition and dimension evaluation in an easy and convenient way by simply freehand scanning the interesting section of the wheel. The technique is based on the laser light sectioning technique. A special setup with multiple laser lines in combination with appropriate algorithms allows to correct errors due to inaccurate sensor slant as well as fusion of measurement data to generate a cross section of the wheel out from multiple partial measurements.
A novel solution for automatic hardwood inspection is presented. A sophisticated multi sensor system is required for reliable results. Our system works on a data stream of more than 50 MByte/Sec in input and up to 100 MByte/Sec inside the processing queue. The algorithm is divided into multiple steps. Along a fixed grid the images are decomposed into small squares. 55 texture- and color features are computed for each square. A Maximum Likelihood classifier assigns each square to one out of 12 defect classes with a recognition rate better than 97%. Depending on the defect type a dedicated threshold operation is performed for segmentation. Threshold levels and the selection of the input channel (RGB + filtered images) is the result of the former classification step. A fast algorithm computes bounding rectangles from blobs. Defect type dependent rules are used to combine rectangles. Two additional fast high resolution 3D measurement systems add board shape and 3D defect information. All defect rectangles are passing an additional plausibility check in the last data fusion process before they are delivered to the optimization computer. To guarantee a short response time, image acquisition and image processing are performed in parallel on parallel computing hardware.
A machine vision application for the fully automatic straightening of steel bars is presented. The bars with lengths of up to 6000 mm are quite bent on exit of the rolling mill and need to be straightened prior to delivery to a customer. The shape of the steel bar is extracted and measured by two video resolution cameras which are calibrated in position and viewing angle relative to a coordinate system located in the center of the roller table. Its contour is tracked and located with a dynamic programming method utilizing several constraints to make the algorithm as robust as possible. 3D camera calibration allows the transformation of image coordinates to real-world coordinates. After smoothing and spline fitting the curvature of the bar is computed. A deformation model of the effect of force applied to the steel allows the system to generate press commands which state where and with what specific pressure the bar has to be processed. The model can be used to predict the straightening of the bar over some consecutive pressing events helping to optimize the operation. The process of measurement and pressing is repeated until the straightness of the bar reaches a predefined limit.
This paper describes the key elements of a system for detecting quality defects on leather surfaces. The inspection task must treat defects like scars, mite nests, warts, open fissures, healed scars, holes, pin holes, and fat folds. The industrial detection of these defects is difficult because of the large dimensions of the leather hides (2 m X 3 m), and the small dimensions of the defects (150 micrometers X 150 micrometers ). Pattern recognition approaches suffer from the fact that defects are hidden on an irregularly textured background, and can be hardly seen visually by human graders. We describe the methods tested for automatic classification using image processing, which include preprocessing, local feature description of texture elements, and final segmentation and grading of defects. We conclude with a statistical evaluation of the recognition error rate, and an outlook on the expected industrial performance.
This paper describes a computer vision system for the high-precision inspection of bearing shells. We have developed algorithms to solve the problem of inspecting the wearing surfaces of sputter-coated metal shells for surface defects (high spots, cavities, blisters, grooves, and pores). The quality goal to be achieved was 0.3 m2/h, which for a typical 90 mm bearing shell being measured would mean about 0.5 minutes/shell. The resolution to be achieved was of each pixel covering an area of 24 micrometers by 24 micrometers . The analysis method was based on gray-scale rather than a binary algorithm. The quality standards were those defined by the Motoren and Turbinen- Union GmbH, Germany, and Daimler-Benz AG.
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