Experimental investigation and artificial intelligence-based modeling of the residual impact damage effect on the crashworthiness of braided Carbon/Kevlar tubes

Abstract

Fiber reinforced plastic composites are promising candidates for building the next generation of automotive and aircraft structures. However, these materials are sensitive to any potential impact, which may cause matrix micro-cracking or internal inter-laminar delamination damages. This study provides insights into the sensitivity of braided Carbon/Kevlar round tubes to external damages and neural network-based models that can predict the consequences of damages on the crush-behavior (load-bearing capability). This was investigated by subjecting the tube to transverse low-velocity impacts at different energy levels and locations. Then, these pre-damaged tubes were crushed using a quasi-static compression test. The results indicate that the pre-impact energy levels have a significant effect on the deterioration of both the structure strength and the crush behavior. The locations of the damages are mainly responsible for altering the collapse behavior of the structure rather than its performance. The crush force efficiency is not significantly affected by the pre-impact energy levels, but it is highly affected by the pre-impact/damage locations. The undamaged tubes were collapsed in a progressive manner, whereas splitting and crack propagation were the dominant failure modes in the tubes with residual damages. The path of those cracks was governed by the damage location. Artificial neural network-based models were developed, compared and improved with the objective to model the highly non-linear behavior of the load carrying capacity of the pre-impacted tubes. The developed model successfully provides a quick and accurate assessment at all compression strokes with an MSE of 0.000191 KN.