基于 B 样条神经网络的熔铸装药温度场预测

The temperature field distribution and evolution inside the mold play a crucial role in determining the casting quality of melt-cast explosive processes. A fast prediction model is developed based on a B-spline neural network for the transient temperature field in a melt-cast explosive process with a water/oil bath. The model is created by first obtaining temperature evolution data samples under different processing conditions through orthogonal numerical experiments. The B-spline neural network is then trained on these data samples to establish a prediction model that represents the relationship between temperature-control parameters and the temperature field inside the grain. This model enables rapid and accurate prediction of the temperature field and solidification front, providing an efficient prediction method for parameter optimization and online control of melt-cast explosive processes. This study serves as a valuable reference for predicting other physical fields in the intelligent development of similar processes in the future.