拉伸-扭转载荷下复合材料前驱体分层损伤的超声测量与检测

S. Patra, Sourav Banerjee
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引用次数: 3

摘要

复合材料损伤状态的前兆量化是结构健康监测领域中极具挑战性的问题。常规超声技术无法预测损伤的早期状态;这可能会导致结构的灾难性破坏。因此,早期状态损伤检测对结构的安全运行至关重要。复合材料在极端环境下工作时,会经历不同类型的载荷条件(如拉力、扭转弯曲等)。复合材料损伤的前兆表现为基体开裂、纤维断裂和分层。本文提出了一种用于碳纤维复合材料(CFRP)前驱体损伤状态量化的机载损伤检测技术。采用美国材料试验学会(ASTM)标准试样进行了张扭疲劳试验。每隔1万次循环进行一次Pitch-catch实验,并使用扫描声学显微镜(SAM)进行超声成像,以检查材料表面和内部的损伤发生情况。光学显微镜也被用来检查材料表面的损伤。采用离散傅立叶变换(DFT)、短时傅立叶变换(STFT)和连续小波变换(CWT)等先进的信号处理技术对传感器信号进行分析,提取疲劳载荷下的损伤增长信息,证明了在线SHM中前体损伤量化的可行性。
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Ultrasonic measurement and detection of precursor delamination damage in composite under tension-torsion loading
Precursor to Damage state quantification in composite material is extremely challenging in the field of structural health monitoring (SHM). Conventional ultrasonic technique is not able to predict the early damage state; this could lead to catastrophic failure of the structure. So, early state damage detection is very imperative for safety and operation of structure. Composite materials experience different type of loading condition (e.g., Tension, torsion bending, etc.) during its operation in extreme environment. Precursor to damage in the composite material can appear in the form matrix cracking, fiber breakage and delamination. In this work, we presented an on board damage detection technique for precursor damage state quantification of Carbon fiber composite material (CFRP). An American society of testing and materials (ASTM) standard specimen was tested under tensor-torsion fatigue lading. Pitch-catch experiments were performed at a regular interval of 10,000 cycles and ultrasonic imaging were performed by using scanning acoustic microscope (SAM) to examine the onset of damage on surface as well as inside the material. Optical microscopy was also performed to examine the damage onset on the surface of the material. Advance signal processing techniques such as Discrete Fourier Transform (DFT), Short-time Fourier transform (STFT) and Continuous Wavelet Transform (CWT) were performed to analyze the sensor signal for extract information of damage growth with fatigue loading to prove that the precursor damage quantification is possible in online SHM.
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