Using Support Vector Machine for Modeling of Pulsed GTAW Process

Abstract

AbstractThis paper investigates modeling of the pulsed gas tungsten arc welding (GTAW) process using support vector machine (SVM). Modeling is one of the key techniques in the control of the arc welding process, but is still a very difficult problem because the process is multivariable, time-delay and nonlinear. We analyze the characteristics of SVM for solving the challenge problem and give the main steps of modeling, including selecting input/output variables, kernel function and parameters according to our specific problem. Experimental results of the SVM, neural network and rough set methods show the feasibility and superiority of our approach.KeywordsSupport Vector MachineWeld PoolSupport Vector RegressionWeld CurrentWeld SeamThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.