Title: A compact vision sensor for weld pool surface sensing
Authors: Gohar Saeed, Yu Ming Zhang, Sarah Cook
Addresses: Department of Electrical and Computer Engineering, Center for Manufacturing, University of Kentucky, Lexington, KY 40506, USA. ' Department of Electrical and Computer Engineering, Center for Manufacturing, University of Kentucky, Lexington, KY 40506, USA. ' Department of Electrical and Computer Engineering, Center for Manufacturing, University of Kentucky, Lexington, KY 40506, USA
Abstract: The purpose of vision-based sensing devices in the welding industry is to electronically replicate the role of a skilled welder and emulate the human eye with a light-sensing device such as camera and the human brain with a computer algorithm that interprets the images. Just as an optical feedback from human eye guides the human welder, optical feedback in this electronic system would be used to control a welding process, in the case of this research, the Gas Tungsten Arc Welding (GTAW). Such sensing systems have been developed, but the purpose of this study is to build one using more commonly available elements and on a much smaller scale, as to be able to attach it to an already-existing welding system without imposing dramatic space requirements on the system. This paper discusses the procedure employed in developing the knowledge base and the experimental system used for building this compact sensor. Experiments have been performed to determine the positioning of the lens, its focal length and size. A study of the illuminating system is also documented towards understanding how light is dispersed under the welding environment. The illumination system is based on structured laser light, where a laser line is projected on the weld pool. The weld pool is divided into three parts: front (deepest), middle and back (shallow). Experiments have been performed to determine the position where the laser light needs to hit the weld pool and how it is reflected from various points of the weld pool.
Keywords: weld pool surface; vision-based sensing; machine vision; image; weld penetration; joint penetration; gas tungsten arc welding; GTAW; automation; manufacturing; laser sensing.
DOI: 10.1504/IJMIC.2006.010104
International Journal of Modelling, Identification and Control, 2006 Vol.1 No.2, pp.94 - 100
Published online: 16 Jun 2006 *
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