Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

Abstract

Extrusion connection is a new method of forming and manufacturing heterogeneous welded sheets. The factors that affected the bonding quality are the forming temperature, the extrusion ratio, and the guiding angle of the die, which has brought trouble to the evaluation of bonding strength and quality. A method to establish a predicted model for the bonding strength of welded sheets by integrating finite element simulations, process experiments, and artificial neural networks was developed. Finite element simulations were used to verify the process experiments and provided training data sets for the artificial neural networks. The BP neural network was used to predict the bonding strength. Due to the randomness of the weight and threshold of the BP neural network, its predicted accuracy needs to be improved, in which genetic algorithms were used to optimize consequently. The results showed that the genetic algorithm neural network model had higher reliability, and the predicted accuracy was 99.5%. Compared with the traditional BP neural network, the predicted accuracy was improved by 5.78%, and the error was reduced to 0.5%. It has good generalization ability and provides a new way for intelligent reliability evaluation of high performance heterogeneous welded sheets via extrusion.