Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm

The laser welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. In particular mechanical properties should be controlled to obtain good welded joints. In this study, the weld bead geometry such as depth of penetration (DP), bead width (BW) and tensile strength (TS) of the laser welded butt joints made of AISI 904L super austenitic stainless steel are investigated. Full factorial design is used to carry out the experimental design. Artificial neural networks (ANNs) program was developed in MatLab software to establish the relationship between the laser welding input parameters like beam power, travel speed and focal position and the three responses DP, BW and TS in three different shielding gases (argon, helium and nitrogen). The established models are used for optimizing the process parameters using genetic algorithm (GA). Optimum solutions for the three different gases and their respective responses are obtained. Confirmation experiment has also been conducted to validate the optimized parameters obtained from GA.

► Super austenitic stainless steel has successfully welded by laser welding with three different shielding gases.
► Among the three shielded joints, the helium shielded weld has more tensile strength.
► Neural network model was developed to predict the depth of penetration, bead width and tensile strength of the joints.
► The developed ANN model is suitably integrated with GA for optimization.