Automated impact damage detection technique for composites based on thermographic image processing and machine learning classification

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dc.contributor.authorAlhammad, Muflih
dc.contributor.authorAvdelidis, Nicolas Peter
dc.contributor.authorIbarra-Castanedo, Clemente
dc.contributor.authorTorbali, Muhammet E.
dc.contributor.authorGenest, Marc
dc.contributor.authorZhang, Hai
dc.contributor.authorZolotas, Argyrios
dc.contributor.authorMaldgue, Xavier P. V.
dc.date.accessioned2023-01-04T12:33:56Z
dc.date.available2023-01-04T12:33:56Z
dc.date.issued2022-11-22
dc.description.abstractComposite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique.en_UK
dc.identifier.citationAlhammad M, Avdelidis NP, Ibarra-Castanedo C,et al., (2022) Automated impact damage detection technique for composites based on thermographic image processing and machine learning classification. Sensors, Volume 22, Issue 23, November 2022, Article number 9031en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s22239031
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18843
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomposite materialsen_UK
dc.subjectimpact damageen_UK
dc.subjectdamage diagnosisen_UK
dc.subjectinfrared thermographyen_UK
dc.subjectmachine learningen_UK
dc.subjectprincipal component thermographyen_UK
dc.subjectpulsed phase thermographyen_UK
dc.subjectthermographic imagesen_UK
dc.subjectsupport vector machineen_UK
dc.titleAutomated impact damage detection technique for composites based on thermographic image processing and machine learning classificationen_UK
dc.typeArticleen_UK

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