Investigation of damage accumulation in glass/carbon fiber hybrid composites through acoustic emission method

I. Emami Tabrizi, J. Seyyed Monfared Zanjani, M. Yildiz

Research output: Contribution to conferencePaperAcademicpeer-review

Abstract

In this study glass/carbon fiber hybrid laminates are produced with different stacking sequence. Tensile and flexural test specimens are prepared and acoustic emission (AE) is used for damage classification during mechanical tests. The calculation of the hybrid effect for flexural and tensile specimen shows the significant influence of stacking sequence on hybrid effect under flexural loads while it has very little effect on tensile properties. The damage evolution is monitored by AE signals and damage types are identified by clustering the AE hits based on their weighted peak frequency and partial power using K-means method. While tensile specimens revealed four clusters of AE hits, flexural samples showed three classes of damage. The missing cluster under flexural loads is related to identified to be fiber pull out, which is a rare failure type under flexural loading. The spectrograms obtained for AE hits showed that different sources of AE hits result in the distinct frequency-time structure of waveform. It is revealed that the distribution of energy over time and frequency domain is directly related to the type of material(s) involved during the development of the damage. Results of the frequency-time analysis showed that the peaks of energy released during acoustic emission bursts are related to the stiffness of the material being damaged.

Original languageEnglish
Publication statusPublished - 2019
Event22nd International Conference on Composite Materials, ICCM 2019 - Melbourne, Australia
Duration: 11 Aug 201916 Aug 2019
Conference number: 22

Conference

Conference22nd International Conference on Composite Materials, ICCM 2019
Abbreviated titleICCM 2019
CountryAustralia
CityMelbourne
Period11/08/1916/08/19

Keywords

  • Acoustic emission
  • Hybrid fiber laminates
  • K-means clustering
  • Spectrogram

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