Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

Abstract

In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.

Keywords:

• INFRARED THERMAL IMAGE
• WELD BEAD GEOMETRY
• ARTIFICIAL NEURAL NETWORK
• MULTILAYER PERCEPTRON
• RADIAL BASIS FUNCTION
• ONLINE FEATURE SELECTION