High-speed Visual Control of Laser WeldingProcesses by Cellular Neural Networks (CNN)

Marc Geese∗, Ronald Tetzlaff§, Daniel Carl†, Andreas Blug†, Heinrich Hofler† and Felix Abt‡∗Johann Wolfgang von Goethe University

Frankfurt am Main, GermanyEmail: marc@geese-online.de

† Fraunhofer Institute for Physical Measurement Techniques IPMFreiburg, Germany

‡Forschungsgesellschaft fur Strahlwerkzeuge mbH (FGSW)Stuttgart, Germany

§Technische Universitat DresdenDresden, Germany

I. INTRODUCTION

Former investigations showed that many errors in laserwelding processes are detectable by analyzing the parametersof the keyhole shape and the melt. By performing this analysisin real time, the welding process can be controlled and errorscan be eliminated as they occur. The high dynamics of theprocess require constant image processing frame rates of about10 kHz. Therefore, we decided to use a CNN based cameraarchitecture allowing a pixel-parallel processing with framerates of up to 10 kHz. To observe the welding process, thecamera is connected to the optics of the welding machinecoaxially by a beam splitter. The camera input is filtered toobtain wave lengths of infrared light. The image shows theinteraction zone and its environment as seen by the weldingbeam.

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 imageswith 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 EyeRIS1.1 system of ANAFOCUS Ltd. Our algorithm detects the fullpenetration with a frame rate of 9 kHz. Furthermore by usingthe image sensors of the Eye-RIS system, first successful testswith detection rates of 1.6 kHz were performed. The slowdownof the detection speed is caused by the low sensitivity ofthe 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 relevantfeatures will be implemented and used within a process controlsystem to avoid errors in the welding process.

11th International Workshop on Cellular Neural Networks and their ApplicationsSantiago de Compostela, Spain, 14-16 July 2008

CNNA2008

978-1-4244-2090-2/08/$25.00 ©2008 IEEE 9