Geometry and Thermal Regulation of GMA Welding via Conventional and Neural Adaptive Control

• Published in: Journal of intelligent & robotic systems Vol. 19; no. 2; pp. 153 - 186
• Main Authors: Tzafestas, S. G., Rigatos, G. G., Kyriannakis, E. J.
• Format: Journal Article
• Language: English
• Published: Dordrecht Kluwer 01.06.1997; Springer Nature B.V
• Subjects: Adaptative systems; Applied sciences; Computer science; control theory; systems; Control theory. Systems; Exact sciences and technology; Gas metal arc welding; Joining, thermal cutting: metallurgical aspects; Mathematics; Metals. Metallurgy; Model reference adaptive control; Multivariable control; Numerical controls; Product design; Self tuning; Welding; Thermal characteristic; Geometrical shape; Process control; Neural network; Arc welding; Adaptive control; Artificial intelligence; GMA welding; Robotics
• ISSN: 0921-0296; 1573-0409; 1573-0409
• DOI: 10.1023/A:1007968630038

This paper investigates the application of conventional and neural adaptive control schemes to Gas Metal Arc (GMA) welding. The goal is to produce welds of high quality and strength. This can be achieved through proper on-line control of the geometrical and thermal characteristics of the process. The welding process is variant in time and strongly nonlinear, and is subject to many defects due to improper regulation of parameters like arc voltage and current, or travel speed of the torch. Adaptive control is thus naturally a very good candidate for the regulation of the geometrical and thermal characteristics of the welding process. Here four adaptive control techniques are reviewed and tested, namely: model reference adaptive control (MRAC), pseudogradient adaptive control (PAC), multivariable self-tuning adaptive control (STC), and neural adaptive control (NAC). Extensive numerical results are provided, together with a discussion of the relative merits and limitations of the above techniques.