Affiliation:
1. School of Mechanical Engineering Zhejiang Sci‐Tech University Zhejiang Hangzhou China
2. School of Additive Manufacturing Zhejiang Institute of Mechanical & Electrical Engineering Zhejiang Hangzhou China
Abstract
AbstractIt 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.