Research on welding quality prediction methods

Abstract

Welding process is a critical part of industrial manufacturing, and the welding quality directly affects the final construction cycle and production cost. Therefore, to predict the welding quality efficiently and accurately. A welding quality prediction method based on real-time data is proposed in this paper. Firstly, a welding quality prediction model combining genetic algorithm and back propagation neural network is proposed to achieve accurate prediction of welding quality. Then, the experiment results demonstrated that this method had satisfactory performance and could be applied to real-time accurate prediction of welding quality. Compared with the traditional BP algorithm prediction model, the coefficient of determination is improved by 44.46%, and the prediction accuracy is over 91.236%.