Design of composite rectangular tubes for optimum crashworthiness performance via experimental and ANN techniques
Monzure-Khoda Kazi, Fadwa Eljack, E. Mahdi
Abstract
This paper examines the crashworthiness performance of composite rectangular tubes using experimental and artificial neural network (ANN) techniques. Based on experimentally obtained values of different crashworthiness parameters under various loading conditions, ANN models are constructed to identify the optimum cross-sectional aspect ratio of cotton fiber/epoxy laminated composite to achieve the targeted mechanical properties such as load carrying and energy absorption capability. Experimental findings show that axially and laterally loaded rectangular tubes were significantly affected by their aspect ratio. Furthermore, the predictions obtained from the ANN models showed consistency with the experimental data. In addition, the developed ANN captured the complicated nonlinear relationship among crashworthiness parameters to obtain insight into the practical design of the composite materials.