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Experimental investigation and Artificial Intelligence- Based Modeling of the Residual Impact Damage Effect on the Crashworthiness of Braided Carbon/Kevlar Tubes
Composite Structures ( IF 6.3 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.compstruct.2020.112247
Othman Laban , Samer Gowid , Elsadig Mahdi , Farayi Musharavati

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.

中文翻译:

残余冲击损伤对编织碳/凯夫拉管耐撞性影响的实验研究和基于人工智能的建模

摘要 纤维增强塑料复合材料是构建下一代汽车和飞机结构的有希望的候选材料。然而,这些材料对任何潜在的冲击都很敏感,这可能会导致基体微裂纹或内部层间分层损坏。这项研究提供了对编织碳/凯夫拉尔圆管对外部损坏的敏感性以及基于神经网络的模型的见解,这些模型可以预测损坏对挤压行为(承载能力)的影响。这是通过使管在不同的能量水平和位置受到横向低速冲击来研究的。然后,使用准静态压缩试验压碎这些预先损坏的管子。结果表明,冲击前能级对结构强度和压碎行为的恶化都有显着影响。损坏的位置主要负责改变结构的倒塌行为而不是其性能。挤压力效率不受冲击前能量水平的显着影响,但受冲击前/损坏位置的影响很大。未损坏的管子以渐进的方式倒塌,而分裂和裂纹扩展是具有残余损坏的管子的主要失效模式。这些裂缝的路径由损坏位置决定。开发了基于人工神经网络的模型,与模拟预冲击管承载能力的高度非线性行为的目标进行比较和改进。开发的模型成功地提供了对所有压缩冲程的快速准确评估,MSE 为 0.000191 KN。
更新日期:2020-07-01
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