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中南大学学报(英文版)

Journal of Central South University

Vol. 23    No. 4    April 2016

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Dynamic rupture and crushing of an extruded tube using artificialneural network (ANN) approximation method
Javad Marzbanrad1, Behrooz Mashadi1, Amir Afkar1, 2,Mostafa Pahlavani1

1. School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran;
2. Faculty of Electrical, Mechanical and Construction Engineering, Department of Automotive Engineering,
Standard Research Institute (SRI), Karaj P.O. Box 31745-139, Iran

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 are300mm and 50mm, 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.

 

Key words: thin-walled structure; rupture; energy absorption; ductile failure criterion; neural network

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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