Numerical analysis of crack path effects on the vibration behaviour of aluminium alloy beams and its identification via artificial neural networks

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dc.contributor.authorDoğanay Katı, Hilal
dc.contributor.authorBuhari, Jamilu
dc.contributor.authorFrancese, Arturo
dc.contributor.authorHe, Feiyang
dc.contributor.authorKhan, Muhammad
dc.date.accessioned2025-02-19T15:41:57Z
dc.date.available2025-02-19T15:41:57Z
dc.date.freetoread2025-02-19
dc.date.issued2025-02-01
dc.date.pubOnline2025-01-30
dc.description.abstractUnderstanding and predicting the behaviour of fatigue cracks are essential for ensuring safety, optimising maintenance strategies, and extending the lifespan of critical components in industries such as aerospace, automotive, civil engineering and energy. Traditional methods using vibration-based dynamic responses have provided effective tools for crack detection but often fail to predict crack propagation paths accurately. This study focuses on identifying crack propagation paths in an aluminium alloy 2024-T42 cantilever beam using dynamic response through numerical simulations and artificial neural networks (ANNs). A unified damping ratio of the specimens was measured using an ICP® accelerometer vibration sensor for the numerical simulation. Through systematic investigation of 46 crack paths of varying depths and orientations, it was observed that the crack propagation path significantly influenced the beam’s natural frequencies and resonance amplitudes. The results indicated a decreasing frequency trend and an increasing amplitude trend as the propagation angle changed from vertical to inclined. A similar trend was observed when the crack path changed from a predominantly vertical orientation to a more complex path with varying angles. Using ANNs, a model was developed to predict natural frequencies and amplitudes from the given crack paths, achieving a high accuracy with a mean absolute percentage error of 1.564%.
dc.description.journalNameSensors
dc.identifier.citationDoğanay Katı H, Buhari J, Francese A, et al., (2025) Numerical analysis of crack path effects on the vibration behaviour of aluminium alloy beams and its identification via artificial neural networks. Sensors, Volume 25, Issue 3, February 2025, Article number 838
dc.identifier.eissn1424-8220
dc.identifier.elementsID563504
dc.identifier.issn1424-8220
dc.identifier.issueNo3
dc.identifier.paperNo838
dc.identifier.urihttps://doi.org/10.3390/s25030838
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23482
dc.identifier.volumeNo25
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/1424-8220/25/3/838
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4005 Civil Engineering
dc.subject40 Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleNumerical analysis of crack path effects on the vibration behaviour of aluminium alloy beams and its identification via artificial neural networks
dc.typeArticle
dcterms.dateAccepted2025-01-27

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