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This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process. [ABSTRACT FROM AUTHOR]

In this paper, we propose an architecture based on an artificial neural network (ANN), to learn welding skills automatically in industrial robots. With the aid of an optic camera and a laser-based sensor, the bead geometry (width and height) is measured. We propose a real-time computer vision algorithm to extract training patterns in order to acquire knowledge to later predict specific geometries. The proposal is implemented and tested in an industrial KUKA KR16 robot and a GMAW type machine within a manufacturing cell. Several data analysis are described as well as off-line and on-line training, learning strategies, and testing experimentation. It is demonstrated during our experiments that, after learning the skill, the robot is able to produce the requested bead geometry even without any knowledge about the welding parameters such as arc voltage and current. We implemented an on-line learning test, where the whole experiments and learning process take only about 4 min. Using this knowledge later, we obtained up to 95 % accuracy in prediction. [ABSTRACT FROM AUTHOR]