Structure Optimization of Electromagnetic Railgun Armature Based on Machine Learning

Abstract The armature is an important part of the electromagnetic railgun. It may break, wear, and transition during the working process, which increases the difficulty of maintaining the railgun and impairs the service life of the railgun. Therefore, studying the armature structure of the electromagnetic railgun is of great significance to the design of the electromagnetic railgun. This paper selects the five armature structure parameters of armature shoulder thickness, tail length, wing tip thickness, front end thickness, and interference, and focuses on "using armature structure parameters to predict prestress" and "using prestress and other armature to predict the interference of structural parameters". This paper uses the linear regression method in machine learning to construct the model, and use the normal equation method and the regularized normal equation method to optimize the loss function. The results show that the normal equation method is generally effective for the electromagnetic railgun structure optimization problem discussed in this paper.