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首页> 外文会议>Proceedings of the American Society for Composites Twenty-Ninth technical conference, Proceedings of the 16th US-JAPAN conference on composite materials ASTM-D30 meeting >Identification of Debonding in CFRP Stiffened Panels using Pattern Recognition
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Identification of Debonding in CFRP Stiffened Panels using Pattern Recognition

机译：使用模式识别识别CFRP加筋板中的脱胶

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摘要

Composite materials have been increasingly employed in primary aerospacernstructures; particularly in modern unmanned aerial vehicles (UAV). Unlike metals forrnwhich direct quantitative paths between microstructure and overall mechanicalrnbehavior have been established and are continuously refined, the activation andrnevolution of failure mechanisms in composite materials represents a hierarchicallyrnmore complex mechanics problem, especially in field applications. To address thisrnissue, advanced inspection techniques and nondestructive methods have beenrnimplemented to identify signs of early damage and prevent catastrophic failure.rnAmong them, Acoustic Emission (AE) has shown perhaps the most potential ofrnidentifying the onset of the different failure mechanisms in composite materials inrnlaboratory conditions. Due to recent advances in hardware/software, AE has thernpotential to be applied in actual aircrafts. In this study, AE was used to identify andrndistinguish signals arising from debonding in carbon fiber reinforced polymer (CFRP)rnstiffened panels, primarily used in Unmanned Aerial Vehicle (UAV) wings. To thisrnaim, the authors demonstrate an approach to identify and classify these AE signalsrnthrough pattern recognition (PR). Specifically, CFRP stiffened panels were loadedrnunder both monotonic and fatigue bending conditions and critical feature descriptorsrnsensitive to debonding were identified. The K-means pattern recognition approach wasrnthen attempted to identify the class of signals associated with debonding. The overallrnapproach revealed that the class of signals associated with debonding has similarrnfeature characteristics across the different tested panels, and thus a framework isrnpresented suitable for future robust damage detection for onboard applications.
机译：复合材料已越来越多地用于主要的航空航天结构中。特别是在现代无人机（UAV）中。与金属已经建立并不断完善的在微观结构和整体机械行为之间建立直接定量路径的金属不同，复合材料中失效机理的激活和消退代表了等级上更为复杂的力学问题，尤其是在现场应用中。为了解决这一问题，已实施了先进的检查技术和非破坏性方法来识别早期损坏的迹象并防止灾难性故障。其中，声发射（AE）可能显示出在复合材料实验室条件下识别不同破坏机制的起因的最大潜力。 。由于硬件/软件的最新发展，AE具有在实际飞机中应用的潜力。在这项研究中，AE被用于识别和区分主要在无人飞行器（UAV）机翼中使用的碳纤维增强聚合物（CFRP）增强面板中的脱胶产生的信号。为此，作者展示了一种通过模式识别（PR）识别和分类这些AE信号的方法。具体而言，在单调和疲劳弯曲条件下加载CFRP加劲板，并确定了对脱胶敏感的关键特征描述符。然后尝试使用K均值模式识别方法来识别与脱胶相关的信号类别。总体方法表明，与脱胶相关的信号类别在不同的测试面板上具有相似的特征，因此，提出了一种适用于未来车载应用的稳健损伤检测的框架。

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