Crushing behavior of laterally compressed composite elliptical tubes: Experiments and predictions using artificial neural networks

Abstract Composite materials have been increasingly used in the automobile industry for weight saving and part integration purposes. In this regard, composite elliptical tubes have been effectively employed as energy absorber devices. This increases the need for accurate and simple prediction techniques to optimize these structures. The present work deals with the implementation of artificial neural networks (ANN) technique in the prediction of the crushing behavior and energy absorption characteristics of laterally loaded glass fiber/epoxy composite elliptical tubes. Predicted results are compared with actual experimental data in terms of load carrying capacity and energy absorption capability showing good agreement. This shows that ANN techniques could effectively be used to predict the response of collapsible composite energy absorber devices subjected to different loading conditions. As is the case for experimental findings, the predictions obtained using ANN also show the significant effect of the ellipticity ratio on the crushing behavior of laterally loaded tubes.

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