Modeling Methods of Weld Pool Dynamics During Pulsed GTAW

Abstract

GTAW is a thermal process during which the workpiece melts, solidifies and finally forms the welding seam. As is well known, arc welding is influenced by many complex factors, such as material metallurgy, heat conduction, physical chemistry reactions, etc. Due to its multi-variable coupling, nonlinear, time-varying, random and uncertain properties, GTAW dynamics is difficult to be modelled by classical linear system theory. In this chapter, analysis on the welding dynamics is made to understand the process of welding. Based on the analysis, both identification models and intelligent models, e.g. ANN, fuzzy rules model and RS-based model are discussed. ANN model is a “black box" and it is impossible to directly revise the model. For fuzzy rules model, the number of inputs, outputs and their linguistic variables cannot be too large, or it will lead to “rule explosion". RS model is promising for welding process modeling because compared with NN model, RS model is close in predictive ability; and however its complexity is much lower. GTAW is a thermal process during which the workpiece melts, solidifies and finally forms the welding seam. As is well known, arc welding is influenced by many complex factors, such as material metallurgy, heat conduction, physical chemistry reactions, etc. Due to its multi-variable coupling, nonlinear, time-varying, random and uncertain properties, it is very difficult to model welding dynamics by classical linear system theory. In recent years, some intelligent modeling methods have been introduced to welding. References [1-3] investigated fuzzy reasoning application in modeling, and Refs.[4-8] studied artificial neural networks for modeling. In this chapter, both identification models and intelligent models are discussed for the weld pool dynamics during pulsed GTAW.