Application of Artificial Neural Network Modelling for Optimization of Yb: YAG Laser Welding of Nitinol

Abstract

This article presents the investigation of Ytterbium: Yttrium aluminium garnet (Yb: YAG) laser welding of NiTinol sheet of thickness 1 mm. Welding speed, shielding gas blown distance, focal position, laser power were selected as input parameters and depth of penetration, bead width, hardness, corrosion current density were measured as performance characteristics. Experiment was designed based on L9 Taguchi design with 4 factors and 3 levels in each factor. Modelling was done using artificial neural network in four learning algorithms namely batch back propagation, quick propagation, incremental back propagation and Legvenberg-Marquardt back propagation. A comparison was made between these learning algorithms and it was found that based on least error, Legvenberg-Marquardt model was the best learning algorithm. Genetic algorithm was implemented for predicting the optimised laser welding parameters in joining NiTinol and the confirmation test results were in good agreement with the predicted results.