Body tube wear prediction based on optimization algorithm and gray theory

Abstract:
The bore of the tank gun is subjected to high temperature and pressure during the firing process, and bore ablation and wear is an important factor that restricts the life of the tank gun and determines the ability of the tank gun to function in combat. For this reason, a tank gun firing armor-piercing ammunition is used as the research object to predict the remaining life of the tank gun barrel based on multiple cross-sections using the gray GM(1,1) model, gray Verhulst model, BP neural network model and combined gray neural network prediction model, respectively. The results show that the combined grey neural network prediction model has better accuracy and less error in prediction. The BP neural network was finally identified as more suitable for life prediction of tank gun body tube.