Gas metal arc welding

About: Gas metal arc welding is a research topic. Over the lifetime, 11706 publications have been published within this topic receiving 109555 citations. The topic is also known as: metal active gas welding & GMAW.

Prediction of metal deposition from arc sound and weld temperature signatures in pulsed MIG welding.

Abstract: The weld deposition efficiency is an important economic factor like productivity and weld quality in gas metal arc welding (GMAW). There is a strong relationship between arc sound signals and arc stability (or deposition efficiency) in GMAW. In this work, the variation of weld deposition efficiency with various pulse parameters in pulsed metal inert gas welding was investigated. The arc sound signal along with current and voltage signals were acquired and analyzed in time domain as well as in frequency domain. The sound signal kurtosis and arc power were found to be highly correlated with welding process stability. The weld deposition efficiency was also related to weld surface peak temperature. Finally, an attempt was made to correlate the sound time domain as well as frequency domain features of sound signal with the deposition efficiency. The variation of pulse shape with the duty factor also influenced the deposition efficiency as evidenced by in fast Fourier transform analysis.

On-line learning of welding bead geometry in industrial robots

Abstract: 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.

Automatic arc welding electrode with an electrically conductive flux coating

Abstract: The invention consists of a continuous electric arc welding electrode of indefinite length consisting of a metal core and an outer coating which is electrically conductive so that the electrode can be energized by sliding it across conventional electrical contacts. The coating is comprised of: a mixture of an organic resin, which chars when heated to elevated temperatures; carbon or metal powders which conduct current through the resin; and, possibly other known fluxing ingredients. A thin outer metallic coating is often also used which conducts current from the contacts circumferentially and longitudinally to the electrically conductive coating.