Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

Wei Liu,Xiawei Yang,Wenya Li,Feifan Wang

doi:10.1155/2013/196382

Abstract

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

Highlights

Friction welding (FW) is a solid-state joining process where heat is generated directly by mechanical friction between a rotating or oscillating workpiece and a stationary component under pressure
It should be pointed out that the changing tendencies of these variables during inertia friction welding (IFW) are relatively independent of the processing parameters, and the simulated final upset (6.2 mm) is comparable to experiments with an error of 8.7%
Results and Discussion sets of final upsets under different IFW processing parameters are shown in Table 3, which were used to build and train the radial basis function (RBF) neural network

Summary

Introduction

Friction welding (FW) is a solid-state joining process where heat is generated directly by mechanical friction between a rotating or oscillating workpiece and a stationary component under pressure. FW is being used with metals and thermoplastics in a wide variety of aviation and automotive applications, and various aspects of research have been done on a large scale, which were reviewed in detail by Maalekian [1] Both experimental and FE methods are powerful approaches for the investigation of FW, the ability to perform experiments is seriously limited due to high cost and time required. Inertia friction welding (IFW), continuous drive friction welding (CDFW), and linear friction welding (LFW) are typical FW processes where two components stand against each other with relative motion under a pressure. It follows the subsequent local frictional heat generation and plastic deformation. The RBF algorithm model of upset for each FW process has been developed using results of FE simulations of the process

FE Model of IFW

Simulation Results

Mathematical Prediction Model Settings

Conclusions