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High-speed Visual Control of Laser Welding Processes by Cellular Neural Networks (CNN) Marc Geese * , Ronald Tetzlaff § , Daniel Carl † , Andreas Blug † , Heinrich H¨ ofler † and Felix Abt ‡ * Johann Wolfgang von Goethe University Frankfurt am Main, Germany Email: [email protected] † Fraunhofer Institute for Physical Measurement Techniques IPM Freiburg, Germany ‡ Forschungsgesellschaft f¨ ur Strahlwerkzeuge mbH (FGSW) Stuttgart, Germany § Technische Universit¨ at Dresden Dresden, Germany I. I NTRODUCTION Former investigations showed that many errors in laser welding processes are detectable by analyzing the parameters of the keyhole shape and the melt. By performing this analysis in real time, the welding process can be controlled and errors can be eliminated as they occur. The high dynamics of the process require constant image processing frame rates of about 10 kHz. Therefore, we decided to use a CNN based camera architecture allowing a pixel-parallel processing with frame rates of up to 10 kHz. To observe the welding process, the camera is connected to the optics of the welding machine coaxially by a beam splitter. The camera input is filtered to obtain wave lengths of infrared light. The image shows the interaction zone and its environment as seen by the welding beam. Fig. 1. A welding seam with variation of the beam power is given on the left hand side. Above and right hand side some input images with results are presented Fig. I shows the detection of a full penetration. Full pen- etration occurs when the beam melts through the workpiece. We developed an algorithm and implemented it on the EyeRIS 1.1 system of ANAFOCUS Ltd. Our algorithm detects the full penetration with a frame rate of 9 kHz. Furthermore by using the image sensors of the Eye-RIS system, first successful tests with detection rates of 1.6 kHz were performed. The slowdown of the detection speed is caused by the low sensitivity of the camera sensors. With the newly developed Eye-RIS v.1.2- system, such problems are expected to disappear. The right- hand side of Fig.1 shows the detection of the quality feature ”full penetration” exemplarily. In the progress of this project the detection of more relevant features will be implemented and used within a process control system to avoid errors in the welding process. 11th International Workshop on Cellular Neural Networks and their Applications Santiago de Compostela, Spain, 14-16 July 2008 CNNA2008 978-1-4244-2090-2/08/$25.00 ©2008 IEEE 9
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