Acoustic emission monitoring of damage progression in Glass/Epoxy composites during static and fatigue tensile tests

Roundi, Walid; Mahi, Abderrahim El; Gharad, Abdellah El; Rebière, Jean-Luc

doi:10.1016/j.apacoust.2017.11.017

Cited by 109 publications

(55 citation statements)

References 24 publications

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“…The calculated energy per count is clustered using k-means++ algorithm. For considering the optimum number of clusters in which the data can be classified, Davies-Bouldin Index (DBI) is used [26,27]. The DBI index for cluster 'k' ranging from 1 to 6 is calculated in MATLAB ® .…”

Section: Data Clustering Using K-means++ Pattern Recognitionmentioning

confidence: 99%

Damage Propagation Analysis in the Single Lap Shear and Single Lap Shear-Riveted CFRP Joints by Acoustic Emission and Pattern Recognition Approach

et al. 2020

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An innovative way of using the Acoustic Emission (AE) technique is introduced in this research work. The ratio of recorded acoustic energy and the counts recorded for each acoustic event were used for characterizing Carbon Fiber Reinforced Plastic (CFRP) laminates adhesively bonded with and without mechanical fasteners. The cumulative counts and cumulative energy of the recorded acoustic events were used for identifying the critical points of failure under loading of these hybrid joint specimens. The peak amplitude distribution was used for identifying the different damage modes such as delamination, matrix cracking and fiber breakage, albeit, ineffectively. The new parameter energy per count was introduced in this work, which can successfully identify the different damage modes under loading. To differentiate the damage modes using the energy per count, they were clustered using k-means++ pattern recognition technique. The method introduced in this work can estimate the damage modes of the CFRP specimens.

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Section: Data Clustering Using K-means++ Pattern Recognitionmentioning

confidence: 99%

Damage Propagation Analysis in the Single Lap Shear and Single Lap Shear-Riveted CFRP Joints by Acoustic Emission and Pattern Recognition Approach

et al. 2020

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“…Furthermore, several studies on the AE data generated from mechanical testing have been reported. Various researchers studied the correlation of AE features on composite specimen deformation by tensile testing [9][10][11][12][13]. The most of researchers highlight the investigation of damage progression in the composite is still a challenging task, especially when multiaxial stress state occurs.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…high mechanical and insulation properties of FRP layers and ductility with thermal and electrical conduction of the metal layers. Hence, several studies [2, 3, 4, 5, 6] have highlighted the benefits of their use, such as high resistance to crack propagation, impact loading and corrosion, that increases the fatigue endurance and durability of FML. Thus given their mechanical functions and their exposure to environmental conditions, understanding their long-term behavior is a major challenge for their use in structures.…”

Section: Introductionmentioning

confidence: 99%

Damage detection of a hybrid composite laminate aluminum/glass under quasi-static and fatigue loadings by acoustic emission technique

Dia¹,

Dieng²,

Gaillet³

et al. 2019

Heliyon

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This paper deals with the characterization by Acoustic Emission technique of damages occurring in a hybrid laminate aluminium/glass during quasi-static and fatigue tests. Indeed, hybrid laminates materials metal/composites are more and more considered in structure, automotive and aerospace designs because of their good mechanical performances and lightness. To understand their damages characteristics, several types of laminates (fiber orientations, number of folds, presence or not of an aluminium sheet) have been tested. The acoustic emission analysis has been realized using statistical multi-parameters methods of data clustering: combination of Principal Components Analysis (PCA) and k-means methods in unsupervised analysis and Classification and Regression Trees (CART) in supervised analysis. Using these methods, it was possible to identify damages occurring during both quasi-static and fatigue tests. Acoustic emission parameters such as counts to peak (PCNTS), amplitude, duration, counts and frequency come out as the most relevant to classify the damage mechanisms; and with the energy parameter, friction mechanisms that can occur during fatigue tests have been sorted.

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Acoustic emission monitoring of damage progression in Glass/Epoxy composites during static and fatigue tensile tests

Cited by 109 publications

References 24 publications

Damage Propagation Analysis in the Single Lap Shear and Single Lap Shear-Riveted CFRP Joints by Acoustic Emission and Pattern Recognition Approach

Damage Propagation Analysis in the Single Lap Shear and Single Lap Shear-Riveted CFRP Joints by Acoustic Emission and Pattern Recognition Approach

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

Damage detection of a hybrid composite laminate aluminum/glass under quasi-static and fatigue loadings by acoustic emission technique

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