Real time control of curved laser welding processes by cellular neural networks (CNN): first results

In the last decades the laser beam welding (LBW) has outclassed older welding techniques in the industrial scenario. Despite the improvement in welding technology, sophisticated methods of fault detection are not commonly used in commercially available equipments yet. A recent analysis of process images have revealed the possibility to build up a real time closed loop control system. By the use of image based quality features, a feedback signal can be provided to maintain the process in the desired state. The development of the presented visual control system has been focused on the adjustment of the laser power according to the detection of the so called full penetration hole. Due to the high dynamics of the laser welding, a fast real time image processing with controlling rates in the multi kilo Hertz range is necessary to have a robust feedback control. In this paper an algorithm for the real time control of welding processes is described. It has been implemented on the Eye-RIS v1.2, a visual system which mounts a cellular structure. By applying this algorithm in real time applications, controlling rates of about 7 kHz can be reached. In the following some real time control results are also described.