{"title":"Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8","authors":"Mingxuan Huang, Zhonghai Xu, Dianyu Chen, Chaocan Cai, Weilong Yin, Rongguo Wang, Xiaodong He","doi":"10.1155/2024/5740931","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5740931","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/5740931","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
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.
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.