Modeling of Friction Stir Welding of AL7075 Using Neural Networks

Friction stir welding (FSW) is a solid state welding process, which is used for the welding of aluminum alloys. It is important to note that the mechanical properties of the FSW process depends on various process parameters, such as spindle speed, feed rate and shoulder depth. Two different tool materials, such as High speed steel (HSS) and H13 are considered for the welding of Al 7075. The present paper deals with the modeling of FSW process using neural networks. A three layered feed forward neural network (NN) has been used to model the FSW of aluminum alloys. It is important to note that the connection weights and bias values of the NN are optimized with the help of a binary coded genetic algorithm (GA). The training of the NN with the help of GA is a time consuming process. Hence, offline training has been provided to optimize the connection weights and bias values of the neural network. Once, the training is over, the GA trained neural network will be used for online prediction of the mechanical properties of FSW process at different operating conditions.