Research on defect identification of carbon fiber composite materials based on ultrasonic phased array

IF 4.8 2区 材料科学 Q2 MATERIALS SCIENCE, COMPOSITES
Ziang Jing, Gaoshen Cai, Xiang Yu, Bingxu Wang
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Abstract

It is more and more difficult to identify defects in carbon fiber composite materials due to the difficulty in making defect samples and the single signal analysis method. In order to better solve the problem of defect identification in carbon fiber composite materials, this study uses ultrasonic phased array equipment to quantitatively locate and detect carbon fiber composite laminates with embedded delamination defects, so as to more intuitively and effectively display the appearance of different delamination defects. The time domain analysis of the collected ultrasonic original signal and the time‐frequency domain analysis using wavelet packet are carried out. A total of 6 eigenvalues were extracted to reflect the ultrasonic signals of different delamination defects. By using genetic algorithm to optimize BP neural network, the recognition accuracy of delamination defects of different sizes is more than 95%, and the recognition accuracy of delamination defects of different depths is 100%, so as to realize the effective intelligent recognition of delamination defects of different sizes and depths of carbon fiber composites. This study is of great significance to improve the accuracy and reliability of defect identification of carbon fiber composite materials.Highlights The ultrasonic phased array equipment is used to quantitatively locate the carbon fiber composite laminates with embedded delamination defects, so that the appearance of different defects can be displayed more intuitively and effectively. Using time domain analysis and time‐frequency domain analysis based on wavelet packet, the combination of the two can more comprehensively extract the effective features of the defect signal. The BP neural network is optimized by genetic algorithm, and the results can effectively and automatically identify different layered defects, which lays a good foundation for the rapid and accurate identification of more defects in the future.

Abstract Image

基于超声相控阵的碳纤维复合材料缺陷识别研究
由于碳纤维复合材料缺陷取样困难、信号分析方法单一等原因,碳纤维复合材料的缺陷识别越来越困难。为了更好地解决碳纤维复合材料的缺陷识别问题,本研究利用超声相控阵设备对嵌入分层缺陷的碳纤维复合材料层压板进行定量定位和检测,从而更直观有效地显示不同分层缺陷的外观。对采集到的超声波原始信号进行时域分析,并利用小波包进行时频域分析。共提取了 6 个特征值来反映不同分层缺陷的超声波信号。通过遗传算法优化 BP 神经网络,不同尺寸分层缺陷的识别准确率达到 95% 以上,不同深度分层缺陷的识别准确率达到 100%,从而实现了对碳纤维复合材料不同尺寸和深度分层缺陷的有效智能识别。该研究对提高碳纤维复合材料缺陷识别的准确性和可靠性具有重要意义。 亮点 利用超声相控阵设备对嵌入分层缺陷的碳纤维复合材料层压板进行定量定位,从而更直观有效地显示不同缺陷的外观。利用时域分析和基于小波包的时频域分析,二者结合能更全面地提取缺陷信号的有效特征。通过遗传算法对 BP 神经网络进行优化,结果可以有效自动识别不同层次的缺陷,为今后快速准确地识别更多缺陷奠定了良好的基础。
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来源期刊
Polymer Composites
Polymer Composites 工程技术-材料科学:复合
CiteScore
7.50
自引率
32.70%
发文量
673
审稿时长
3.1 months
期刊介绍: Polymer Composites is the engineering and scientific journal serving the fields of reinforced plastics and polymer composites including research, production, processing, and applications. PC brings you the details of developments in this rapidly expanding area of technology long before they are commercial realities.
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