Cluster analysis of acoustic emission signals for 2D and 3D woven glass/epoxy composites
Source abstract
Abstract
Abstract The goal of this work is to identify the best strategy for clustering of AE events, originated from damage initiation and development of 2D and 3D glass/epoxy woven composites loaded in tension. Two AE features – peak amplitude and peak frequency – were selected as the best cluster-definition features from nine AE parameters by (a) Laplacian score and correlation analysis, (b) principal component analysis and k-means++ algorithm and (c) repeatability and similarity analysis of the clusters in AE registration of different specimens. Peak amplitude and peak frequency represent adequately and in a reproducible way the AE events clustering for both 2D and 3D woven glass/epoxy composites, resulting in the clusters of similar shape. Cluster bounds are identified for different reinforcement type and different loading directions. The cluster identification creates a framework for analysis of a link between damage mode and AE parameters of the corresponding AE event.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Mechanical Behavior of Composites · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (31)
Show source-provided references
1. Waveform based clustering and classification of AE transients in composite laminates using principal component analysis10.1016/S0963-8695(02)00004-X ↗
2. Failure mechanisms analysis of 2D carbon/carbon using acoustic emission monitoring10.1016/S0963-8695(98)00002-4 ↗
3. Analysis of damage mechanisms and associated acoustic emission in two SiCf/[Si–B–C] composites exhibiting different tensile behaviours. Part II: unsupervised acoustic emission data clustering10.1016/j.compscitech.2007.12.002 ↗
4. On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses10.1016/j.ymssp.2010.11.014 ↗
5. Pattern recognition approach to identify natural clusters of acoustic emission signals10.1016/j.patrec.2011.09.018 ↗
6. Quantification of failure mechanisms in mode-I loading of fiber reinforced plastics utilizing acoustic emission analysis10.1016/j.compscitech.2011.10.013 ↗
7. Identification of failure modes in GFRP using PVDF sensors: ANN approach10.1016/j.compstruct.2003.10.019 ↗
8. Reliability of damage mechanism localisation by acoustic emission on glass/epoxy composite material plate10.1016/j.compstruct.2011.11.037 ↗
9. Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers10.1016/j.ndteint.2003.09.010 ↗
10. Use of acoustic emission to identify damage modes in glass fibre reinforced polyester10.1016/S0266-3538(02)00087-8 ↗
11. Integration of the Kohonen’s self-organising map and k-means algorithm for the segmentation of the AE data collected during tensile tests on cross-ply composites10.1016/j.ndteint.2004.09.006 ↗
12. Health monitoring of FRP using acoustic emission and artificial neural networks10.1016/j.compstruc.2007.02.015 ↗
13. Effect of fiber orientation in unidirectional glass epoxy laminate using acoustic emission monitoring
14. Damage characterization of polymer-based composite materials: multivariable analysis and wavelet transform for clustering acoustic emission data10.1016/j.ymssp.2007.11.029 ↗
15. Acoustic emission-based damage classification of glass/polyester composites using harmony search k-means algorithm10.1177/0731684412442257 ↗
16. A global method for the identification of failure modes in fiberglass using acoustic emission10.1520/JTE103730 ↗
17. On the use of acoustic emission to investigate damage mechanisms in glass-fibre-reinforced polypropylene10.1016/0266-3538(94)90171-6 ↗
18. Longitudinal splitting and fiber breakage characterization in graphite epoxy using acoustic emission data
19. Suzuki M, Nakanishi H, Iwamoto M, Jinen E. Application of static fracture mechanisms to fatigue fracture behavior of class A-SMC composite. In: Japan-US conference on composite materials, 4th, Washington, DC; 1989. p. 297–306.
20. Fatigue behavior of non-crimp 3D orthogonal weave and multi-layer plain weave E-glass reinforced composites10.1016/j.compscitech.2010.08.002 ↗
21. A comparative study of tensile properties of non-crimp 3D orthogonal weave and multi-layer plain weave E-glass composites. Part 1: materials, methods and principal results10.1016/j.compositesa.2009.03.012 ↗
22. A comparative study of tensile properties of non-crimp 3D orthogonal weave and multi-layer plain weave E-glass composites. Part 2: comprehensive experimental results10.1016/j.compositesa.2009.04.032 ↗
23. Gramellini · 2009
24. Feature selection for clustering: a review
25. Laplacian score for feature selection
26. Cai · 2010
27. Jolliffe · 2005
28. Arthur D, Vassilvitskii S. K-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms: Society for Industrial and Applied Mathematics; 2007. p. 1027–35.
29. Performance evaluation of some clustering algorithms and validity indices10.1109/TPAMI.2002.1114856 ↗
30. Classification of surface EMG signals using optimal wavelet packet method based on Davies–Bouldin criterion10.1007/s11517-006-0100-y ↗
31. Study of fracture mechanism of class A-SMC based on acoustic emission method10.2472/jsms.36.229 ↗