New CNN based algorithms for the full penetration hole extraction in laser welding processes

• Published in: 2009 IEEE International Symposium on Circuits and Systems pp. 2713 - 2716
• Main Authors: Nicolosi, L., Tetzlaff, R., Abt, F., Hofler, H., Blug, A., Carl, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: Automobiles; Cellular neural networks; Circuits; Manufacturing processes; Optical control; Process control; Production; Signal processing algorithms; Vehicle dynamics; Welding
• ISBN: 1424438276; 9781424438273
• ISSN: 0271-4302
• DOI: 10.1109/ISCAS.2009.5118362

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.