Prediction of Shunt Rate for Consecutive Resistance Spot Welding by Back Propagation Neural Network

Abstract

A prediction model for shunt rate of resistance spot welding in constant current control is developed by a three-layer back propagation(BP) neural network,where the material resistivity,material thickness and dot pitch are the input of the model,and the shunt rate is the output.According to the characteristics of the constant current control and the resistance change during the process of spot welding,a compensation model to calculate the shunt of the consecutive spot welding is established.The training and test samples of the artificial neural network(ANN) from this compensation model are used to train the network and verify the performance of the trained network.By using the trained network,the shunt rates are predicted for 20 mild steel of 2.0 mm thickness and 10 mild steel of 1.5 mm and 1.0 mm thickness respectively.The biggest relative prediction error is 2.83%,1.77% and 3.67% respectively.The experiments with current compensation using the shunt rate of prediction on the resistance spot welding machine are done at different dot pitch.When the dot pitch is 30 mm,as for mild steel of 1mm thickness,the average relative error between the nugget diameters of the second to the fifth spot welded and the one of the first spot welded is 2.86% and as for mild steel of 2.0 mm thickness it is 2.46%.While the dot pitch is 50 mm,for the former the average relative error is 3.99%,the latter it is 3.58%.The test results indicated the proposed BP neural network prediction model has good performance to predict shunt rate for consecutive spot welding of the 10 and 20 mild steel.