US EPA

1758575 

Journal Article 

Intelligent methodology for sensing, modeling and control of pulsed GTAW part 2 - Butt joint welding 

Chen, SB; Zhao, DB; Wu, L; Lou, YJ 

2000 

1 

Welding Journal
ISSN: 0043-2296 

79 

6 

164S-174S 

WOS:000088916200018 

This paper addresses intelligent techniques for the quality
control of the pulsed gas tungsten are welding process for butt joints, and it is a development
to Ref. 1. Because there exist some important differences in butt joint welding and bead-on-plate
welding, the modeling and control scheme in Ref. 1 does not completely fit for butt joint
welding. In this paper, the differences between the two were investigated. The shape and size
parameters for the weld pool were used to describe the weld pool geometry. A new real-rime
algorithm was developed for the size and shape parameters. A size and shape neural network model
(SSNNM) was established to predict the maximum backside width. The model accuracy was verified.
Furthermore, a self-learning fuzzy neural network controller (FNNC) was designed for control of
the maximum backside width and the fuzzy rules were modified online. Based on the FNNC, and
combined with an expert system, a double-input and double-output (DIDO) intelligent controller
was developed for controlling the maximum backside width and the shape of the weld pool.
Experiment results showed the DIDO intelligent controller could form a better butt joint weld. 

bead size; bead width; expert system; fuzzy logic; GTAW-P; intelligent controller; neural networks; weld geometry