Application of Deep Learning Techniques to Predict the Mechanical Strength of Al-Steel Explosive Clads

Abstract

In this study, the tensile and shear strengths of aluminum 6061-differently grooved stainless steel 304 explosive clads are predicted using deep learning algorithms, namely the conventional neural network (CNN), deep neural network (DNN), and recurrent neural network (RNN). The explosive cladding process parameters, such as the loading ratio (mass of the explosive/mass of the flyer plate, R: 0.6–1.0), standoff distance, D (5–9 mm), preset angle, A (0°–10°), and groove in the base plate, G (V/Dovetail), were varied in 60 explosive cladding trials. The deep learning algorithms were trained in a Python environment using the tensile and shear strengths acquired from 80% of the experiments, using trial and previous results. The remaining experimental findings are used to evaluate the developed models. The DNN model successfully predicts the tensile and shear strengths with an accuracy of 95% and less than 5% deviation from the experimental result.