A machine learning model for failure of perforated plates under impact

Abstract

In this work, a new attempt has been made using machine learning algorithms for assessing failure mode of austempered ductile iron perforated plates. This aims at providing some insights into these problems by comparing the performance of machine learning models which are part of artificial intelligence. The ballistic performance could be assessed by k-nearest neighbors (KNN), support vector machine (SVM), logistic regression, and decision tree (DT) algorithms. Precision of KNN, SVM, logistic regression and DT models is found to be 0.75, 0.75, 0.8, and 1, respectively. F1 score of KNN, SVM, logistic regression and DT models is found to be 0.86, 0.86, 0.89, and 1, respectively for smooth bulge formation. Eventually, the DT model is established and the optimal prediction model is derived by fine-tuning the parameters.

Keywords:

• Decision tree
• machine learning
• perforated plate

Disclosure statement

No potential conflict of interest was reported by the authors.