Intelligent Methodology for Sensing, Modeling and Control of Pulsed GTAW Part 2 Butt Joint Welding Double variable intelligent control incorporated with a fuzzy neural network and expert system is proposed for bead shape control during butt joint welding

This paper addresses intelligent techniques for the quality control of the pulsed gas tungsten arc 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-time 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.