CAJ | 학술논문

碳纤维增强复合材料(CFRP)以其特殊的性能广泛应用在不同领域,然而缺陷的存在严重影响材料的性能,造成重大的经济损失,甚至存在安全隐患,因此,碳纤维复合材料的无损检测与缺陷识别已成为该领域的研究热点之一.利用超声相控阵系统检测CFRP中3种常见缺陷——分层、夹杂和脱粘,得到原始A扫信号,运用小波包变换理论,提取样本特征值,建立并训练 BP 神经网络,对 3 种缺陷进行识别,识别率达到 95.7%.结果表明:利用超声相控阵技术可以提高缺陷检测效率,对缺陷有良好的成像效果;小波包与 BP 神经网络的结合,对 CFRP中的不同缺陷有较高的识别率.
탄섬유증강복합재료(CFRP)이기특수적성능엄범응용재불동영역,연이결함적존재엄중영향재료적성능,조성중대적경제손실,심지존재안전은환,인차,탄섬유복합재료적무손검측여결함식별이성위해영역적연구열점지일.이용초성상공진계통검측CFRP중3충상견결함——분층、협잡화탈점,득도원시A소신호,운용소파포변환이론,제취양본특정치,건립병훈련 BP 신경망락,대 3 충결함진행식별,식별솔체도 95.7%.결과표명:이용초성상공진기술가이제고결함검측효솔,대결함유량호적성상효과;소파포여 BP 신경망락적결합,대 CFRP중적불동결함유교고적식별솔.
Carbon fiber reinforced plastics(CFRP)composite materials are widely used in different fields for their special properties. However,the defects in carbon fiber reinforced plastics composite materials seriously affect their performance and may cause significant economic loss,and even security problems. So both non-destructive testing and defect recognition of CFRP composite materials have become the hot research spots in this field. Ultrasonic phased array system was used to inspect the CFRP composite materials which contain such defects as de-lamination, inclusion and de-bonding. The original A scan signals from these materials were analyzed by wavelet packet transform and the characteristic values of these samples were extracted. BP neural network was builtand trained for identifying those defects. The recognition rate could reach 95.7%. The result shows that ultrasonic phased array technology can improve the inspection efficiency obviously,and has the good imaging effect. The combination of wavelet packet with the BP neural network has a high recognition rate for the defects of de-lamination,inclusion and de-bonding in CFRP.