Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-10-26 DOI:10.1155/2024/5740931
Mingxuan Huang, Zhonghai Xu, Dianyu Chen, Chaocan Cai, Weilong Yin, Rongguo Wang, Xiaodong He
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Abstract

In this study, a novel delamination detection method for composite materials is proposed through the innovative use of You Only Look Once v8 (YOLOv8), vibration analysis, and 2D continuous wavelet transform techniques. The method detects the location and size of damage more accurately than existing methods and avoids manual intervention in the detection process. Damage detection performed on the simulation dataset shows that the method is able to accurately identify the delamination location with IoU = 0.9906 and an average accuracy of 91.32%. The proposed method is then compared with the widely used YOLOv5 model, and the superior performance of the YOLOv8 model is verified, with a 37.93% improvement in training speed and 0.81% improvement in detection accuracy. In addition, an experimental dataset of four composite laminates with delamination damage is constructed. By using transfer learning, the performance of the pretrained network achieves a good precision up to 1. The method proposed in this study expands the range of tasks that can be accomplished by mode shape analysis and is very effective in real experiments.

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利用模形处理方法和 YOLOv8 检测复合材料层压板中的分层现象
本研究通过创新性地使用 You Only Look Once v8 (YOLOv8)、振动分析和二维连续小波变换技术,提出了一种新型复合材料分层检测方法。与现有方法相比,该方法能更准确地检测出损坏的位置和大小,并避免了检测过程中的人工干预。在模拟数据集上进行的损伤检测表明,该方法能够准确识别分层位置,IoU = 0.9906,平均准确率为 91.32%。然后,将所提出的方法与广泛使用的 YOLOv5 模型进行了比较,结果验证了 YOLOv8 模型的优越性能,其训练速度提高了 37.93%,检测准确率提高了 0.81%。此外,还构建了四个复合材料层压板分层损伤实验数据集。本研究提出的方法拓展了模态振型分析的任务范围,在实际实验中非常有效。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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