By Nitin R. Nayak PhD, MS, BS, Asok Ray PhD, PE (auth.)
Intelligent Seam monitoring for robot Welding is a part of the Advances in commercial Control sequence, edited via Professor M.J. Grimble and Dr. M.A. Johnson of the commercial keep an eye on Unit, collage of Strathclyde. This ebook discusses extensive the advance of a seam monitoring approach for robot welding. a variety of issues are coated together with the speculation of seam monitoring, information of the sub-systems comprising the clever seam tracker and the operation of the seam monitoring method with coordinated interplay among some of the sub-systems. The resources of assorted seam monitoring blunders and latest seam monitoring structures working in either established and unstructured welding environments also are addressed. The paintings said builds upon the study performed throughout the process the venture ARTIST (Adaptive, RealTime, clever Seam Tracker) on the utilized examine Laboratory of the Pennsylvania country collage. even supposing the booklet is gifted within the context of seam monitoring, concerns relating to structures integration are normal in nature and relate to different purposes as well.
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Additional info for Intelligent Seam Tracking for Robotic Welding
Sample text
Gennery is reported to have used a target with uniformly spaced lines and dots for this purpose (Gennery 1982). This comparison process, known as calibration, can be used to compute the error, which is usually represented in parametric form. Subsequent images of unknown objects can now be subjected to the inverse transformation, known as decalibration, to find the correct coordinates of the image points. However, the process of calibrationldecalibration can result in many image points from the original image to be mapped onto the same coordinates in the transformed image, causing the corrected image to be blurred with loss of resolution.
If the range data for a particular scanning position of the beam does not match the expected trend for the entire scan, then such range data can be regarded as erroneous and should be either measured correctly or ignored. While tracking vee-jointed welding seams, the geometry of the seam causes any multiple reflections on the CCD array to reduce the true range value. The heuristic for bad data suppression is based on this knowledge. Another concern while tracking unstructured 3D seams is the frequently changing position and orientation of the sensor in relation to the seam.
1984) and assumes a segmentable range image. 4 "\ ......... 2 I \. J , '\ ...... 6 Characteristic features of a vee-joint as seen in the range image. ••.. p: P3 ..... ·0. ~. / • ~ N J. J. dl =..... P J. "0. /' ~-+-------------. It. - .... --~T o - -0- 0(). ' • ~ Q. " / 'Q . J. 7(a) Acceptable variations in vee-groove range images for feature extraction (segmentable range images). e. region outside the vee, within a specified tolerance band associated with the joint. 7(a,b) illustrate examples of segmentable and nonsegmentable range images.