Intelligent Prediction of FSW Physical Quantity and Joint Mechanical Properties

Abstract

Friction stir welding (FSW) process parameters influence welding temperature field and axial force, which affect welding strength. At present, how the FSW process parameters of aluminum alloy 2219-T8 thick plates influence process physical quantity and how the process physical quantity changes the tensile strength about the welded joint are unknown. We focus on the intelligent prediction of FSW temperature, axial force, and mechanical properties, to provide a basis for FSW process control of aluminum alloy 2219-T8 thick plate. Firstly, we conducted the FSW experiment of aluminum alloy 2219-T8 thick plate. Then, we input the welding process parameters, set up a prediction model by particle swarm optimization-back propagation (PSO-BP) neural network to predict the peak temperature and axial force. Finally, we input the peak temperature and axial force, use genetic algorithm-back propagation (GA-BP) neural network to establish a weld tensile strength estimation model, and comply with the prediction of tensile strength.