Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis

Abstract

The single weld bead geometry has critical effects on the layer thickness, surface quality, and dimensional accuracy of metallic parts in layered deposition process. The present study highlights application of a neural network and a second-order regression analysis for predicting bead geometry in robotic gas metal arc welding for rapid manufacturing. A series of experiments were carried out by applying a central composite rotatable design. The results demonstrate that not only the proposed models can predict the bead width and height with reasonable accuracy, but also the neural network model has a better performance than the second-order regression model due to its great capacity of approximating any nonlinear processes. The neural network model can efficiently be used to predict the desired bead geometry with high precision for the adaptive slicing principle in layer additive manufacturing.