The various failure mechanisms in bidirectional glass/epoxy laminates loaded in tension are identified using acoustic emission (AE) analysis. AE data recorded during the tensile testing of a single layer specimen are used to identify matrix cracking and fiber failure, while delaminationsignals are characterized using a two-layer specimen with a pre-induced defect. Parametric studies usingAE count rate and cumulative counts allowed damage discrimination at different levels of loading and Fuzzy C-means clustering associated with principal component analysis were used to discriminate between failure mechanisms. The two above methods led to AE waveform selection: On selected waveforms, Fast Fourier Transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform allowed identifying frequency range and time history for failure modes, whilst noise content associated with the different failure modes was calculated and removed by discrete wavelet transform. Short Time FFT finally highlighted the possible failure mechanism associated with each signal. Copyright © 1996-2011 ASTM. All Rights Reserved.

A global method for the identification of failure modes in fiberglass using acoustic emission / V., Arumugam; C., Suresh Kumar; Santulli, Carlo; Sarasini, Fabrizio; A., Joseph Stanley. - In: JOURNAL OF TESTING AND EVALUATION. - ISSN 0090-3973. - STAMPA. - 39:5(2011), p. 103730. [10.1520/jte103730]

A global method for the identification of failure modes in fiberglass using acoustic emission

SANTULLI, CARLO;SARASINI, Fabrizio;
2011

Abstract

The various failure mechanisms in bidirectional glass/epoxy laminates loaded in tension are identified using acoustic emission (AE) analysis. AE data recorded during the tensile testing of a single layer specimen are used to identify matrix cracking and fiber failure, while delaminationsignals are characterized using a two-layer specimen with a pre-induced defect. Parametric studies usingAE count rate and cumulative counts allowed damage discrimination at different levels of loading and Fuzzy C-means clustering associated with principal component analysis were used to discriminate between failure mechanisms. The two above methods led to AE waveform selection: On selected waveforms, Fast Fourier Transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform allowed identifying frequency range and time history for failure modes, whilst noise content associated with the different failure modes was calculated and removed by discrete wavelet transform. Short Time FFT finally highlighted the possible failure mechanism associated with each signal. Copyright © 1996-2011 ASTM. All Rights Reserved.
2011
pattern recognition; acoustic emission; gfrp; failure modes; wavelet analysis
01 Pubblicazione su rivista::01a Articolo in rivista
A global method for the identification of failure modes in fiberglass using acoustic emission / V., Arumugam; C., Suresh Kumar; Santulli, Carlo; Sarasini, Fabrizio; A., Joseph Stanley. - In: JOURNAL OF TESTING AND EVALUATION. - ISSN 0090-3973. - STAMPA. - 39:5(2011), p. 103730. [10.1520/jte103730]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/415312
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