Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection

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dc.contributor.authorDeng, Kailun
dc.contributor.authorLiu, Haochen
dc.contributor.authorYang, Lichao
dc.contributor.authorAddepalli, Sri
dc.contributor.authorZhao, Yifan
dc.date.accessioned2023-02-02T18:38:51Z
dc.date.available2023-02-02T18:38:51Z
dc.date.issued2023-01-31
dc.description.abstractWith the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.en_UK
dc.identifier.citationDeng K, Liu H, Yang L, et al., (2023) Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection. Neural Computing and Applications, Volume 35, Issue 15, May 2023, pp.11207-11221en_UK
dc.identifier.issn0941-0643
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08293-7
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19123
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCFRPen_UK
dc.subjectbarely visible impact damageen_UK
dc.subjectNDTen_UK
dc.subjectdeep learningen_UK
dc.subjectdamage classificationen_UK
dc.titleClassification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspectionen_UK
dc.typeArticleen_UK

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