Dynamic rupture and crushing of an extruded tube using artificial neural network (ANN) approximation method

Abstract

A numerical study of the crushing of thin-walled circular aluminum tubes has been carried out to investigate the crashworthiness behaviors under axial impact loading. These kinds of tubes are usually used in automobile and train structures to absorb the impact energy. Previous researches show that thin-walled circular tube has the highest energy absorption under axial impact amongst different structures. In this work, the crushing between two rigid flat plates and the tube rupture by 4 and 6 blades cutting tools is modeled with the help of ductile failure criterion using the numerical method. The tube material is aluminum EN AW-7108 T6 and its length and diameter are 300 mm and 50 mm, respectively. Using the artificial neural network (ANN), the most important surfaces of energy absorption parameters, including the maximum displacement of the striker, the maximum axial force, the specific energy absorption and the crushing force efficiency in terms of impact velocity and tube thickness are obtained and compared to each other. The analyses show that the tube rupture by the 6 blades cutting tool has more energy absorption in comparison with others. Furthermore, the results demonstrate that tube cutting with the help of multi-blades cutting tools is more stable, controllable and predictable than tube folding.

Keywords

thin-walled structure / rupture / energy absorption / ductile failure criterion / neural network

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