Using Fundamental Parameters and Artificial Neural Network for Controlling the Laser Welding of Metals

Abstract

The present work aims to design an artificial neural network (ANN) for controlling the geometry of the fusion zone in different welding conditions. AA5754 aluminium plates with 6 mm thickness were welded in butt configuration by using an Yb-doped fiber laser in continuous wave regime. Laser power, travel speed, beam diameter and shielding gas were considered as process-related factors, while the weld geometry was evaluated in terms of penetration depth and bead width. The accuracy of the model was endorsed by using untrained test data. Results showed a good agreement between the ANN output and the experimental values.