Application of Genetic Algorithm Optimization Technique in TIG Welding of 15CDV6 Aerospace Steel

In this work, an attempt is made to identify the optimized parameter combinations in TIG (Tungsten Inert Gas) welding of 15CDV6 steel. 15CDV6 is a high strength low alloy steel which is widely used in aerospace industries. TIG welding was carried out on 15CDV6 steel with current, voltage, welding speed, shielding gas flow rate as input parameters. Tensile strength, hardness, and corrosion current density were measured as performance characteristics. Artificial Neural Network (ANN) was used as modeling technique for predicting the output parameters. Four different learning algorithms, viz. Batch Back Propagation (BBP), Quick Propagation (QP), Incremental Back Propagation (IBP), Legvenberg- Marquardt (LM) back propagation were used to train the model, and it was found that legvenberg- Marquardt (LM) as the best learning algorithm concerning the prediction accuracy. Genetic algorithm (GA) optimization technique was used to identify the optimized parameter combinations, and a confirmation test was carried out with GA predicted optimized parameters. The confirmation test results were in good agreement with GA results.