Fusion of Ensemble Learning and Terminal Ballistics: A Multiscale Predictive Framework for Penetration Depth Estimation

Abstract

In order to solve the problems related to the prediction of penetration depth of composite materials, a new method combining physical mechanism and data driving is proposed in this paper. This method integrates 53 groups of ballistic experimental data and 278 groups of LS-DYNA simulation data to construct a data set with 23 characteristic parameters. Previous studies have shown that the ratio of the radius of the circular arc of the warhead to the diameter of the projectile (CRH) has a significant influence on the penetration depth of the projectile. This paper uses the hyperbolic tangent function (tanh(2CRH)) to calculate its saturation effect and conduct in-depth analysis. An adaptive noise injection method of concrete type is used in data processing, which can reduce the data distribution difference between C80 and C150 to 42%. At the same time, because of the modified sequential forward selection algorithm, the input dimension of the model is reduced by 56% and its important physical characteristics are retained. Finally, a Bayesian-optimized Bagging integrated model is constructed, which realizes the high accuracy prediction of RMSE (Root mean square error)=0.23 m and R2=0.90 on the test set. The test data show that when the fiber content reaches about 1%, the penetration resistance of the material is obviously improved. This discovery provides a new direction for optimizing protective materials. Compared with the traditional Forrestal theoretical model, this model not only reduces the prediction error by 92%, but also verifies the effectiveness and universality of the "physical mechanism-guided + data-driven correction" hybrid modeling method in complex penetration problems, providing a generalizable research paradigm for similar engineering mechanics problems that are difficult to fully parameterize.