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Title: Evaluation of spot welding electrodes using digital image processing and image segmentation techniques
Author: Abdulhadi, Abdulwanis Abdalla
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2013
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The image segmentation algorithm is the most challenging step and requires more computer processing power than the boundary filtering, and the Cullen et al's method, which used the Cullen et al's method to determine the electrodes tip width automatically in the automotive industry in real time. Spot welding is used extensively in the automotive industry. The quality of an individual spot weld is a major concern due to the heavy reliance on their use in the manufacture of motor vehicles. The main parameters that control the quality of a spot weld are current, voltage, welding force, welding time, and the quality of welding electrodes. The condition of the welding electrodes plays a major part in determining the quality of a spot weld. For example, excessive electrode wear can occur during the welding process and can cause weakening in the weld nuggets. As the number of welds increases, the electrode tip wears down and so the contact area between electrode tip and work piece increases. In order to determine the quality of the welding electrodes, a machine vision approach is employed, where images of the electrode tips in real time are captured and are processed using various image-processing algorithms. These algorithms can be used to automatically measure the electrode tip width and hence assess the quality of the electrodes tip in real time. The quality of two types of spot welding electrode tips, namely flat-shaped and dome-shaped tips, is assessed here using image processing techniques. For each tip type, a database of 250 images is used to test the performance of the tested algorithms. Also the tip width in these 250 images is determined manually by counting the number of pixels using an image editor such as Microsoft Paint. An excellent agreement is found between these manual and automatic methods. The tip width for an electrode is measured by first grabbing an image showing the electrode. The electrode in the image is then extracted using an image segmentation algorithm. Then the boundary of the electrode is determined and filtered. The Cullen et aI's method is subsequently applied, which uses the filtered boundary to determine the tip width. A number of image segmentation and boundary filtering algorithms have been used to determine the tip width automatically. For flat tip electrode, the combination of the region growing image segmentation, Minimum Perimeter Polygon, and Cull en et al's techniques was capable of automatically determining the tip width for 250 images with a root mean square error of 7.5 % of the tip width. For dome-shaped electrodes, the combination of the Snake segmentation algorithm, Fourier transform, and the Cullen et al's method was capable of automatically determining the tip width for 250 images with a root mean square error of2.9 % of the tip width. The author has proposed and built an active illumination system that captures a backlit image of the electrode's shadow, this system has different camera with same time then above. The image is then processed using a simple image segmentation method, such as the Canny filtering algorithm to locate the boundary of the electrodes tip. Then the boundary is processed using Minimum-Perimeter Polygon approach and Cull en et aI's method to automatically determine the tip width for 200 experiments images. The proposed system is capable of determining the tip width automatically with a root mean square error of 3.2% of the total tip width for flat tips and 3% for dome tips.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available