Weld bead geometry is an important characteristic of the weld joints in evaluating its quality. However, weld geometry is characterized by three parameters namely weld width, weld height and weld depth. The measurement system of three parameters is often time consuming, particularly for intelligent robots used in the welding processes. Hence, in this paper, we introduced a penetration coefficient (PC) that effectively minimizes the complexities involved in existing measurement system of weld geometry. Further, various machine learning approaches are used to predict the penetration coefficient. Cold metal transfer welded AA6061 sheets are chosen to obtain the data of penetration coefficient. Linear regression (LR), support vector machine (SVM) regression and Gaussian process regression (GPR) models and artificial neural network (ANN) model are used for predictive modelling. The statistical performance factors of the models reveal the superior performance of ANN model. The lowest mean absolute error of 0.15 is observed for ANN followed by the SVM (0.31), GPR (0.39) and LR (0.41).
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