Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding

Abstract

The use of fuzzy rule based systems to model the relationship between weld control parameters and the weld bead geometry features is explored in this paper. The Takagi–Sugeno model with linear functions of the inputs is used as the rule consequents. Given some training data, the authors use exploratory data analysis to find an initial rule base. The system parameters, e.g. consequent parameters, are estimated using a mixture of least square error (LSE) method and gradient search. The system is tested on three datasets and the performance is found to be satisfactory compared to the multilayer perceptron (MLP) and radial basis function (RBF) neural networks based systems.