Nondestructive Examination of Polymer Composites by Analysis of Polymer-Water Interactions and Damage-Dependent Hysteresis
Nondestructive Examination of Polymer Composites by Analysis of Polymer-Water Interactions and Damage-Dependent Hysteresis
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DOI：
10.1016/j.compstruct.2022.115377
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发表时间：
2022-02
影响因子：
6.3
通讯作者：
O. Idolor;Katherine Berkowitz;Rishabh Debraj Guha;L. Grace
中科院分区：
文献类型：
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作者：
O. Idolor;Katherine Berkowitz;Rishabh Debraj Guha;L. Grace
聚合物复合材料目前在要求设计灵活性、高强度重量比和耐腐蚀性的应用中取代了金属。然而，这些材料的损伤模式与金属非常不同，需要专门的技术来检测内部缺陷，即使在没有可见表面损伤的情况下也可能存在。本研究提出了一种聚合物复合材料损伤检测技术，该技术使用自然吸收的水分作为“成像”剂。损伤区域处于“自由”状态的局部较高的水浓度，以及这种水快速进出损伤部位的趋势——表现出损伤相关的滞后——被用于损伤检测。为了识别受损区域，采用机器学习方法，使用逻辑回归将局部区域分类为“未受损”或“受损”。强调了由损伤相关滞后实现的更高灵敏度水平所产生的新的可能性，为现场部署新的损伤检测技术提供了途径。
Polymer composites are currently replacing metals in applications requiring design flexibility, high strength-to-weight ratio, and corrosion resistance. However, the damage modes in these materials are very different from metals and require specialized techniques to detect internal flaws which may exist even in the absence of visible surface damage. This study proposes a technique for damage detection in polymer composites which uses naturally absorbed moisture as an ‘imaging’ agent. The locally higher concentration of water in the ‘free’ state at damaged regions and the tendency of such water to quickly migrate to and from damage sites—exhibiting damage-dependent hysteresis—is leveraged for damage detection. To identify damaged regions, a machine learning approach is adopted using logistic regression to classify local regions as ‘undamaged’ or ‘damaged’. New possibilities resulting from higher sensitivity levels achievable by damage-dependent hysteresis are highlighted, providing a pathway to field deployment of the novel damage detection technique.