Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

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dc.contributor.authorDeng, Kailun
dc.contributor.authorLiu, Haochen
dc.contributor.authorCao, Jun
dc.contributor.authorYang, Lichao
dc.contributor.authorDu, Weixiang
dc.contributor.authorXu, Yigeng
dc.contributor.authorZhao, Yifan
dc.contributor.funderThis work was partially supported by the Royal Academy of Engineering Industrial Fellowship [#grant IF2223B-110], and partially supported by the Science and Technology Department of Gansu Province Science and Technology Project Funding, 22YF7GA072.
dc.date.accessioned2024-03-18T13:40:12Z
dc.date.available2024-03-18T13:40:12Z
dc.date.issued2024-03-14
dc.description.abstractMost existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.en_UK
dc.identifier.citationDeng K, Liu H, Cao J, et al., (2024) Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials. Composite Structures, Volume 337, June 2024, Article number 118030en_UK
dc.identifier.issn0263-8223
dc.identifier.urihttps://doi.org/10.1016/j.compstruct.2024.118030
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21022
dc.language.isoen_UKen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine Learningen_UK
dc.subjectAttention mechanismen_UK
dc.subjectBarely visible impact damage (BVID)en_UK
dc.subjectCarbon fibre reinforced polymer (CFRP)en_UK
dc.subjectPulsed thermography (PT)en_UK
dc.titleAttention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materialsen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-03-11

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