Prediction of Deformation of Hexagonal Honeycomb Blast Structure Under Explosive Loading Using Deep Learning

Abstract

Honeycomb composites are widely used in blast structure under explosive loading because of mechanical properties. The simulation of high-pressure explosion is time consuming in order to simulate an important number of scenarios. New deep learning neural models might approximate results with low computational resources outputting the result very fast. The purpose of this study is to propose using deep learning model using a relative low amount of training fata to approximate deformation in honeycomb structures subjected to a blast load. This study employed variation of hexagonal honeycomb dimensions to determine the deformation using deep learning model.