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Conventional inspection and maintenance can be expensive and time-consuming, motivating automated structural health monitoring using guided Lamb waves. This master’s thesis combines finite element simulations of guided Lamb wave propagation with machine learning for delamination detection and localisation in composite plates. A one-dimensional CNN is trained and evaluated using simulation-generated datasets across multiple plate sizes, analyzing the effects of noise level and training dataset size, and reporting classification accuracy and localisation error metrics under controlled signal-to-noise conditions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-for-lamb-wave-based-delamination-detection-in-composite-structures-masters-thesis/290887/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-for-lamb-wave-based-delamination-detection-in-composite-structures-masters-thesis/290887.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the thesis address?","Question",{"text":112,"@type":113},"The thesis targets automated delamination detection and localisation in composite structures using guided Lamb waves, aiming to reduce the cost and time of conventional inspection and maintenance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the machine learning model built and trained?",{"text":117,"@type":113},"Finite element simulations generate datasets for undamaged and delaminated composite plates, and a one-dimensional convolutional neural network (CNN) is trained and evaluated using these simulation results.",{"name":119,"@type":110,"acceptedAnswer":120},"What factors are studied for detection and localisation performance?",{"text":121,"@type":113},"The study considers multiple plate sizes and examines how noise level and the size of the training dataset affect classification accuracy for detection and mean squared error for localisation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290887,1791265502,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Machine learning for Lamb wavebased delamination detection in composite structures  \nMaster’s thesis in Complex Adaptive Systems and Applied Mechanics  \nGUSTAV GIEBAT & JOEL JANSON  \nDEPARTMENT OF INDUSTRIAL AND MATERIALS SCIENCE  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2026  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2026  \nMachine learning for Lamb wave-based delamination detection in composite structures  \nGUSTAV GIEBAT  \nJOEL JANSON  \nDepartment of Industrial and Materials Science Division of Computational Mechanics and Materials Engineering Chalmers University of Technology Gothenburg, Sweden 2026  \nMachine learning for Lamb wave-based delamination detection in composite structures  \nGUSTAV GIEBAT, JOEL JANSON  \n© GUSTAV GIEBAT, JOEL JANSON, 2026 .  \nSupervisor: Ruoshan Luo, Saab Surveillance  \nExaminer: Johanna Xu, Department of Industrial and Materials Science  \nMaster’s Thesis 2026  \nDepartment of Industrial and Materials Science  \nDivision of Computational Mechanics and Materials Engineering Chalmers University of Technology  \nSE-412 96 Gothenburg Telephone +46 31 772 1000  \nCover: Finite element simulation of Lamb wave propagation in a composite plate containing a delamination. The out-of-plane displacement field is shown with a deformation scale factor of 5 × 106 .  \nTypeset in LATEX  \nGothenburg, Sweden 2026  \nMachine learning for Lamb wave-based delamination detection in composite structures  \nGUSTAV GIEBAT, JOEL JANSON Department of Industrial and Materials Science Chalmers University of Technology  \nAbstract  \nComposite materials are increasingly used in the aerospace industry due to their high strength-to-weight ratio and favourable mechanical properties. However, composite structures are susceptible to complex failure mechanisms such as delamination, while conventional inspection and maintenance procedures are often expensive and time consuming. Structural health monitoring based on guided Lamb waves has therefore emerged as a promising approach for automated damage detection. This thesis investigates a methodology for delamination detection and localisation in composite plates by combining finite element simulations of guided Lamb waves with machine learning. Finite element simulations were performed to model wave propagation in undamaged and delaminated plates, generating a dataset used to train and evaluate a one-dimensional convolutional neural network (CNN) . Three plate sizes were considered, and the influence of noise level and training dataset size was examined. For delamination detection, the proposed CNN achieved classification accuracies of (96 .4% ± 7 .0%) ,(99 .4% ± 0 .5%), and (78 .0% ± 23 .0%) on the 100 mm, 250 mm, and 500 mm plates, respectively, at a signal-to-noise ratio of 20 dB. For localisation, the corresponding mean squared errors were (0 .00191 ± 0 .00092) , (0 .00232 ± 0 .00043) , and (0 .01068±0 .00240) . The results demonstrate that finite element simulations can be used to generate datasets suitable for machine learning-based structural health monitoring, and that the proposed CNN can accurately detect and localise delaminations under favourable noise and training data conditions.  \nKeywords: Lamb Waves, Structural Health Monitoring, Composites, Delamination, Damage Detection, Damage Localisation, Machine Learning, Convolutional Neural Networks, Finite Element Method.  \nAcknowledgements  \nWe would like to express our sincere gratitude to Saab for providing us with the opportunity to carry out this exciting thesis project and for creating an inspiring and supportive working environment.  \nWe are especially grateful to Ruoshan Luo, our industrial supervisor, for valuable guidance, technical expertise, and continuous support throughout the project. Her insights and feedback have been invaluable in shaping the direction of this work.  \nWe would also like to thank Johanna Xu, examiner at Chalmers University of Technology, for ensuring the academic quality of the","cbCaimCfEU7UkRu8","https://ap.wps.com/l/cbCaimCfEU7UkRu8","pdf",4528623,71,"English","# Abstract\n# Acknowledgements\n# List of Acronyms\n# 1 Introduction\n## 1.1 Background\n## 1.2 Aim\n## 1.3 Research questions\n## 1.4 Scope and limitations\n## 1.5 Thesis outline\n# 2 Theory\n## 2.1 Composites\n## 2.1.1 Damage modes\n## 2.2 Lamb waves\n## 2.2.1 Wave modes\n## 2.3 FE modelling criteria for wave propagation\n## 2.3.1 Mesh criteria\n## 2.3.2 Time step criteria\n## 2.4 Machine learning","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets automated delamination detection and localisation in composite structures using guided Lamb waves, aiming to reduce the cost and time of conventional inspection and maintenance.\"},{\"question\":\"How is the machine learning model built and trained?\",\"answer\":\"Finite element simulations generate datasets for undamaged and delaminated composite plates, and a one-dimensional convolutional neural network (CNN) is trained and evaluated using these simulation results.\"},{\"question\":\"What factors are studied for detection and localisation performance?\",\"answer\":\"The study considers multiple plate sizes and examines how noise level and the size of the training dataset affect classification accuracy for detection and mean squared error for localisation.\"}]","Machine learning for Lamb wave-based delamination detection in composite structures - Master’s thesis | PDF",1789642889,179]