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

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 conventional neural network (CNN), (DNN), recurrent (RNN). The cladding process parameters, such as loading ratio (mass explosive/mass flyer plate, R: 0.6–1.0), standoff distance, D (5–9 mm), preset angle, A (0–10°), groove in base G (V/Dovetail), were varied 60 trials. algorithms trained a Python environment acquired from 80% experiments, trial previous results. remaining experimental findings used to evaluate developed models. DNN model successfully predicts with an accuracy 95% less than 5% deviation result.