Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

The present study is to optimize the process parameters for friction welding of duplex stainless steel (DSS UNS S32205). Experiments were conducted according to central composite design. Process variables, as inputs of the neural network, included friction pressure, upsetting pressure, speed and burn-off length. Tensile strength and microhardness were selected as the outputs of the neural networks. The weld metals had higher hardness and tensile strength than the base material due to grain refinement which caused failures away from the joint interface during tensile testing. Due to shorter heating time, no secondary phase intermetallic precipitation was observed in the weld joint. A multi-layer perceptron neural network was established for modeling purpose. Five various training algorithms, belonging to three classes, namely gradient descent, genetic algorithm and Levenberg–Marquardt, were used to train artificial neural network. The optimization was carried out by using particle swarm optimization method. Confirmation test was carried out by setting the optimized parameters. In conformation test, maximum tensile strength and maximum hardness obtained are 822 MPa and 322 Hv, respectively. The metallurgical investigations revealed that base metal, partially deformed zone and weld zone maintain austenite/ferrite proportion of 50:50.