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Recurrence quantitative analysis for porosity characterization of CFRP with complex void morphology

机译:具有复杂孔隙形态的CFRP孔隙特征的递归定量分析

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The porosity characterization of Carbon Fibre Reinforced Plastics (CFRP) by ultrasonic attenuation measurement is restricted when the back-wall echoes are weak or absent at high porosity, and the estimation accuracy is decreased with increment of porosity due to complex void morphology. In this paper, Real Morphology Void Models (RMVMs) with different porosity were established to simulate the interaction between ultrasonic waves and voids with complex morphology. Subsequently, Recurrence Quantification Analysis (RQA) was introduced to process ultrasonic backscattered signals generated by voids so as to present Recurrence Plots (RPs) for porosity characterization. Recurrence Rate (RR) was selected as RQA variable to quantitatively analyze RPs, avoiding the determination of additional parameters. The correlation between porosity P and RR was revealed by simulation for the first time, and verified by experiments with CFRP laminates having porosity of 0.8%-4.2%. The complex and random void morphology induced the non-mapping relationship between P and RR, influencing estimation accuracy of porosity. The normalized results indicated that porosity characterization with RR was less affected by void morphology for P > 1.97% in comparison with ultrasonic attenuation measurement. It is concluded that RQA is valuable for porosity characterization of CFRP with high porosity and strong attenuation.
机译:当在高孔隙度下后壁回波较弱或不存在时,通过超声衰减测量对碳纤维增强塑料(CFRP)的孔隙度表征会受到限制,并且由于复杂的孔隙形态,估计精度随孔隙度的增加而降低。本文建立了具有不同孔隙率的真实形态空洞模型(RMVMs),以模拟超声波与具有复杂形态的孔隙之间的相互作用。随后,引入递归定量分析(RQA)来处理由空隙产生的超声反向散射信号,从而提供递归图(RP)进行孔隙度表征。选择复发率(RR)作为RQA变量来定量分析RP,而无需确定其他参数。孔隙度P和RR之间的相关性是首次通过模拟揭示的,并通过孔隙率为0.8%-4.2%的CFRP层压板的实验进行了验证。复杂和随机的孔隙形态导致了P和RR之间的非映射关系,影响了孔隙度的估算精度。归一化的结果表明,与超声波衰减测量相比,RR的孔隙率表征受孔隙形态的影响较小,P> 1.97%。结论是,RQA对于表征高孔隙率和强衰减的CFRP的孔隙率具有重要价值。

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