Rostami, M., Hasanlou, M., Siavashi, M. (2016). Modeling & Comparison of Mechanical Behavior of Foam Filled & Hollow Aluminum Tubes by LS-DYNA & Introducing a Neural Network Model. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 8(4), 275-293.
M. Rostami; M. Hasanlou; M. Siavashi. "Modeling & Comparison of Mechanical Behavior of Foam Filled & Hollow Aluminum Tubes by LS-DYNA & Introducing a Neural Network Model". Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 8, 4, 2016, 275-293.
Rostami, M., Hasanlou, M., Siavashi, M. (2016). 'Modeling & Comparison of Mechanical Behavior of Foam Filled & Hollow Aluminum Tubes by LS-DYNA & Introducing a Neural Network Model', Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 8(4), pp. 275-293.
Rostami, M., Hasanlou, M., Siavashi, M. Modeling & Comparison of Mechanical Behavior of Foam Filled & Hollow Aluminum Tubes by LS-DYNA & Introducing a Neural Network Model. Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering, 2016; 8(4): 275-293.
Modeling & Comparison of Mechanical Behavior of Foam Filled & Hollow Aluminum Tubes by LS-DYNA & Introducing a Neural Network Model
1Lecturer, Mechanical Engineering, Payame Noor University of Qazvin, Qazvin, Iran.
2M.Sc., Mechanical Engineering, University of Guilan, Rasht, Iran
Abstract
Energy absorption capability of thin-walled structures with various cross sections has been considered by researchers up to now. These structures as energy absorbers are used widely in different industries such as automotive and aerospace and protect passengers and goods against impact. In this paper, mechanical behavior of thin-walled aluminum tubes with and without polyurethane foam filler subjected to axial impact has been investigated. The tubes are very thin so that (D/t) ≈ 550 governs for cylindrical specimen. Structure behavior was analyzed through finite element analysis by LS-DYNA. Circular, hexagonal, and square cross sections with the same length, thickness, and circumference of sections were studied. The results show that circular cross section has the highest energy absorption while experiences the lowest change in length compared to hexagonal and square cross sections. Besides, the effects of stress concentration in hexagonal and square sections can be observed on the corners of walls. Also under the dynamic loading circular structure was crushed more symmetric, while hexagonal and square structures tended to the buckling. Also an Artificial Neural Network is introduced to predict load & energy Absorption behavior. The Neural Network's data obtained from LS-DYNA. The introduced model could present acceptable results in comparison with analysis of LS-DYNA
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