Machine learning of weld joint penetration from weld pool surface using support vector regression

Abstract

Skilled human welders can control the weld joint penetration through observing the molten pool. This suggests that a model may be developed to predict the backside bead width, that quantitively measures the weld joint penetration, from the weld pool surface. However, the weld pool surface is specular and subject to the radiation of the arc such that its measurement is challenging. At the University of Kentucky, the weld pool surface is measured using an innovative a 3-D vision sensor that can overcome the challenges caused by the specular nature and arc radiation; and the measured surface is characterized by three parameters. Because of the lack of physics based model, neural networks would typically be used to approximate the unknown correction, which is nonlinear in general, between the backside bead width and the characteristics parameters. Unfortunately, neural networks require large amount of data to train for adequate model accuracy. While the weld pool surface can be measured using the innovative 3D sensor, the ground truth for the backside bead width needs to be measured off-line after the experiment and to this end the work-work needs to appropriately cleaned/processed. Large amount of training data needed may not be easily obtained. To improve the critical ability to accurately predict the backside bead width, models need to be established from relatively small amount of training data. To this end, the authors propose to use the support vector regression (SVR) method and hypothesize that a SVR model trained using the small amount of the training data available would perform better than that a multi-layer perceptron (MLP) artificial neural network model trained using the same data. Modeling results show that for the relatively small training data available, the optimized SVR model provides a more accurate prediction to the backside bead width. As such, the authors systematically advanced the ability to accurately predict the weld joint penetration. The use of the innovative 3D sensor to obtain the 3D weld pool surface and the proposed use of the support vector method to address the small data issue played crucial roles.