Damage clasification in composite materials using neural networks

dc.creatorTais, Carlos Esteban
dc.creatorFontana, Juan Manuel
dc.creatorMolisani, Leonardo
dc.creatorO'Brien, Ronald
dc.creatorBallesteros Iglesias, Maria Yolanda
dc.creatordel Real, Juan Carlos
dc.date.accessioned2025-06-26T16:02:16Z
dc.date.issued2024-11-04
dc.description.abstractComposite materials are widely employed in critical industrial applications, where their use has surged due to their numerous advantages over traditional materials. However, these benefits can be compromised if adequate quality control techniques are not implemented, particularly for detecting structural damage. Acoustic emission is a nondestructive technique commonly used for damage detection. By leveraging artificial intelligence tools to efficiently process emitted signals, the detection and classification process can be automated. This study utilizes sound pressure levels to diagnose failures in fiberglass-reinforced (GFRP) epoxy composite beams. A pattern recognition system based on Artificial Neural Network (ANN) algorithms is employed for diagnosis. To ensure data variability, the classifier was trained and validated using preprocessed acoustic signals from multiple healthy and damaged beams in various locations. Testing was conducted using test results from specimens not used for training and validation, ensuring the ANN's robustness. The results demonstrate a high fault detection percentage, confirming the reliability of the ANN.
dc.description.affiliationTais, Carlos Esteban. Universidad Tecnológica Nacional. Facultad Regional Villa María. Ingeniería Mecánica. Argentina.
dc.description.affiliationFontana, Juan Manuel. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Argentina.
dc.description.affiliationMolisani, Leonardo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Argentina.
dc.description.affiliationO'Brien, Ronald. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Argentina.
dc.description.affiliationBallesteros Iglesias, Maria Yolanda. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. España.
dc.description.affiliationdel Real, Juan Carlos. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. España.
dc.formatpdf
dc.identifier.urihttps://ieeexplore.ieee.org/document/10735804
dc.identifier.urihttps://hdl.handle.net/20.500.12272/13321
dc.language.isoes
dc.publisherIEEE
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Argentinaen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.rights.useCC-BY-NC-ND
dc.subjectDamage detection
dc.subjectSound Pressure Level
dc.subjectNeural Networks
dc.titleDamage clasification in composite materials using neural networks
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versionacceptedVersion

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