In this paper the results obtained by the use of new CNN based visual algorithms for the control of welding processes are described. The growing number of laser welding applications from automobile production to micro mechanics requires fast systems to create closed loop control for error prevention and correction. Nowadays the image processing frame rates of conventional architectures [1] are not sufficient to control high speed laser welding processes due to the fast fluctuation of the full penetration hole [3]. This paper focuses the attention on new strategies obtained by the use of the Eye-RIS system v1.2 which includes a pixel parallel Cellular Neural Network (CNN) based architecture called Q-Eye [2].In particular, new algorithms for the full penetration hole detection with frame rates up to 24 kHz will be presented. Finally, the results obtained performing real time control of welding processes by the use of these algorithms will be discussed.
In this paper a new cellular neural network (CNN) based visual algorithm for welding processes is proposed. The idea described in can be used in processes, whose welding direction has a constant orientation well known a priori. The algorithm proposed in the following is omnidirectional in the sense that it does not depend on the welding direction. This fact enables closed loop control systems for welding processes with curved seeds. On Eye-RIS systems processing times of about 110 mus are achievable for both acquisition and evaluation of full frame images
In this paper new CNN based visual algorithms for the control of welding processes are proposed. The high dynamics of laser welding in several manufacturing processes ranging from automobile production to precision mechanics requires the introduction of new fast real time controls. In the last few years, analogic circuits like cellular neural networks (CNN) have obtained a primary place in the development of efficient electronic devices because of their real-time signal processing properties. Furthermore, several pixel parallel CNN based architectures are now included within devices like the family of EyeRis systems [1]. In particular, the algorithms proposed in the following have been implemented on the EyeRis system v1.2 with the aim to be run at frame rates up to 20 kHz
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