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

材料科学 复合材料 复合数 超声波传感器 相控阵 鉴定(生物学) 碳纤维 纤维 碳纤维复合材料 声学 工程类 电气工程 物理 植物 天线(收音机) 生物
作者
Ziang Jing,Gaoshen Cai,Yu Xiang,Bingxu Wang
出处
期刊:Polymer Composites [Wiley]
卷期号:46 (1): 902-913 被引量:2
标识
DOI:10.1002/pc.29033
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
粗犷的沛容完成签到,获得积分0
刚刚
刚刚
南波波发布了新的文献求助30
刚刚
1秒前
1秒前
kelly发布了新的文献求助10
2秒前
何以解忧完成签到,获得积分10
2秒前
2秒前
2秒前
动听友卉完成签到,获得积分10
3秒前
文静千凡发布了新的文献求助10
3秒前
汉堡包应助嘟嘟采纳,获得10
3秒前
蔡莹发布了新的文献求助10
3秒前
域尔完成签到,获得积分10
3秒前
hello_25baby完成签到,获得积分10
4秒前
lh发布了新的文献求助10
4秒前
5秒前
顾矜应助可靠的不评采纳,获得10
5秒前
nini发布了新的文献求助10
5秒前
大力的又菡完成签到,获得积分10
5秒前
淀粉肠发布了新的文献求助10
6秒前
6秒前
单薄雪巧发布了新的文献求助10
6秒前
6秒前
安静的皮皮虾完成签到,获得积分10
7秒前
7秒前
7秒前
毛子涵发布了新的文献求助10
7秒前
太阳照常升起完成签到,获得积分10
7秒前
zwyoo发布了新的文献求助10
7秒前
8秒前
李文静完成签到,获得积分10
8秒前
8秒前
体贴半仙完成签到,获得积分10
9秒前
杨苗苗发布了新的文献求助10
9秒前
共享精神应助鲤鱼小熊猫采纳,获得10
9秒前
Lucas应助夏秋采纳,获得10
9秒前
我是老大应助孙煜采纳,获得10
10秒前
Xiaoban完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
No Good Deed Goes Unpunished 1100
Bioseparations Science and Engineering Third Edition 1000
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 1000
BRITTLE FRACTURE IN WELDED SHIPS 1000
Entre Praga y Madrid: los contactos checoslovaco-españoles (1948-1977) 1000
Polymorphism and polytypism in crystals 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6107051
求助须知:如何正确求助?哪些是违规求助? 7936107
关于积分的说明 16445537
捐赠科研通 5233924
什么是DOI,文献DOI怎么找? 2796904
邀请新用户注册赠送积分活动 1778990
关于科研通互助平台的介绍 1651703