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首页> 外文期刊>Composite Structures >Experimental investigation and artificial intelligence-based modeling of the residual impact damage effect on the crashworthiness of braided Carbon/Kevlar tubes
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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

机译：基于实验研究和人工智能基于剩余抗冲击损伤对编织碳/凯瓦尔管耐力的影响

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摘要

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.
机译：纤维增强塑料复合材料是建立下一代汽车和飞机结构的候选人。然而，这些材料对任何潜在的影响敏感，这可能导致基质微裂纹或内部层间分层损坏。本研究提供了洞察中，对中编织碳/凯瓦尔圆形管的敏感性和基于神经网络的型号，可以预测损坏的碎屑行为（承载能力）的后果。通过对管进行不同能量水平和位置的横向低速撞击来研究这一点。然后，使用准静态压缩测试压碎这些预损坏的管。结果表明，预冲击能量水平对结构强度和挤压行为的恶化具有显着影响。损害赔偿的位置主要负责改变结构的崩溃行为而不是其性能。粉碎力效率不会受到预冲击能量水平的显着影响，但受到预冲击/损坏位置的影响很大。未损坏的管以渐进的方式折叠，而分裂和裂缝繁殖是管中具有残余损伤的主导失效模式。这些裂缝的路径受到损坏位置的管辖。基于人工神经网络的模型进行了比较和改进，目的是模拟预撞击管的承载能力的高度非线性行为。开发模型成功在所有压缩冲程中提供了一种快速准确的评估，其中MSE为0.000191 kn。

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  • 来源
    《Composite Structures》 |2020年第7期|112247.1-112247.7|共7页
  • 作者

    Laban Othman; Gowid Samer; Mahdi Elsadig; Musharavati Farayi;

  • 作者单位

    Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar;

    Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar;

    Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar;

    Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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