Hypervelocity impact damage prediction of stuffed Whipple shield based on Adaboost

The explicit ballistic limit equation of stuffed Whipple shield may cause some deviations between the prediction results and the measured data when the projectile is subjected to hypervelocity impact damage prediction because of different filling materials and filling methods. In this regard, the machine learning method is used to transform the problem into a binary problem. The projectile impact parameters and protective structure parameters in the collision process are used as the classification features to construct a hypervelocity impact damage prediction model of stuffed Whipple shield based on Adaboost. The model uses the classification and regression tree (CART) as a weak classifier to generate a strong classifier by weighted combination of a series of weak classifiers. Through the cyclic use of training samples, the impact damage prediction under a small sample set is achieved. The experimental results show that the established Adaboost prediction model has good prediction effect on the hypervelocity impact damage of stuffed Whipple shield. Both the totality prediction rate and the safety prediction rate of Adaboost prediction model increase by 14.3% compared with NASA's ballistic limit equation, and the established model has more versatility. Cross check under different training sample sizes proves that the model has good robustness and accuracy.