Prediction of axial shortening in inertia friction welding by RBF and SVM methods

The accurate control of axial shortening is a key factor for precise inertia friction welding. The inertia friction welding of high-temperature alloy was numerically simulated with ABAQUS finite element (FE) software, and the effects of welding parameters on the axial shortening were investigated. According to the simulated results, two models based on support vector machine (SVM) algorithm and radial basis function (RBF) neural network were developed to predict the axial shortening. By comparing two models, it is found that RBF neural network model showed a better agreement with the FE simulations than the SVM algorithm. Therefore, the RBF neural network could be helpful for FE modeling of inertia friction welding and reducing the time cost.