Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates

Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. D...

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Main Authors: May, Z., Alam, M.K., Mahmud, M.S., Rahman, N.A.
Format: Article
Published: Public Library of Science 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096058745&doi=10.1371%2fjournal.pone.0242022&partnerID=40&md5=d74869d4002df302c3b87a9cba66b47e
http://eprints.utp.edu.my/29830/
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spelling my.utp.eprints.298302022-03-29T01:40:19Z Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates May, Z. Alam, M.K. Mahmud, M.S. Rahman, N.A. Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis. Copyright: © 2020 May et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public Library of Science 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096058745&doi=10.1371%2fjournal.pone.0242022&partnerID=40&md5=d74869d4002df302c3b87a9cba66b47e May, Z. and Alam, M.K. and Mahmud, M.S. and Rahman, N.A. (2020) Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates. PLoS ONE, 15 (11 Nov). http://eprints.utp.edu.my/29830/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis. Copyright: © 2020 May et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
format Article
author May, Z.
Alam, M.K.
Mahmud, M.S.
Rahman, N.A.
spellingShingle May, Z.
Alam, M.K.
Mahmud, M.S.
Rahman, N.A.
Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
author_facet May, Z.
Alam, M.K.
Mahmud, M.S.
Rahman, N.A.
author_sort May, Z.
title Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
title_short Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
title_full Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
title_fullStr Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
title_full_unstemmed Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
title_sort unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates
publisher Public Library of Science
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096058745&doi=10.1371%2fjournal.pone.0242022&partnerID=40&md5=d74869d4002df302c3b87a9cba66b47e
http://eprints.utp.edu.my/29830/
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