Quality prediction of internal thread cold extrusion based on genetic algorithm optimized BP neural network

Abstract

The extrusion process parameters have a great impact on the quality of the extrusion thread. In order to ensure the extrusion quality and the smooth progress of the extrusion process, a prediction model based on BP-GA neural network is established. The model takes the diameter of the bottom hole, extrusion speed, and friction coefficient as the input layer, and the extrusion torque, extrusion temperature, and tooth height as the output layer. After training the model with sample data, the extrusion internal thread quality prediction is carried out. The results show that the BP-GA neural network prediction model has a high accuracy in predicting the extrusion torque, extrusion temperature and tooth height rate during the cold extrusion of internal threads. The error between the experimental value and the predicted value of the extrusion torque is between 10% and 15%, the experimental value of the extrusion temperature is consistent with the predicted value, and the error between the experimental value and the predicted value of the tooth height rate is less than 5%. The BP-GA neural network prediction model can accurately predict the extrusion temperature, extrusion torque, and tooth height rate, providing a new way for the re-search of internal thread cold extrusion technology.