Damage Classification of Sandwich Composites Using Acoustic Emission Technique and k-means Genetic Algorithm

Pashmforoush, Farzad; Khamedi, Ramin; Fotouhi, Mohamad; Hajikhani, Milad; Ahmadi, Mehdi

doi:10.1007/s10921-014-0243-y

Cited by 71 publications

(30 citation statements)

References 24 publications

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“…Therefore, a commercial mechanical pencil with an in-house machined guide-ring was used to generate simulated AE sources by breaking a 0.5 mm diameter and 2-3 mm length 2H pencil lead, as recommended by ASTM standards (E976-99) [17]. A Hsu-Nielsen source provides a signal with a broad frequency spectrum, with significant content in the bandwidths previously demonstrated to be associated with adhesive failure [3,6,11]. This, combined with its good repeatability, makes it an appropriate choice of source for these experiments.…”

Section: Acoustic Emission Instrumentationmentioning

confidence: 99%

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Analysis of Acoustic Emission Propagation in Metal-to-Metal Adhesively Bonded Joints

2018

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Acoustic emission (AE) monitoring shows promise as one of the most effective methods for condition monitoring of adhesively-bonded joints. Previous research has demonstrated its ability to detect, locate and classify adhesive joint failure, though in these studies little attention appears to have been paid to the differences in AE wave propagation through the bonded and un-bonded sections of the specimens tested, or to the effects of the wave modes excited or the propagation distances. This paper details an experimental study conducted on large aluminium sheet specimens to identify the effects of the presence of an adhesive layer on AE wave propagation. Three specimens are considered; a single aluminium sheet, two aluminium sheets placed together without adhesive, and an adhesively-bonded specimen. A pencil lead break (PLB) is used as a simulated AE source, and is applied to the three specimens at varying propagation distances and orientations. The acquired signals are processed using wavelet-transforms to explore time-frequency features, and compared with modified group-velocity curves based on the Rayleigh-Lamb equations to allow identification of wave-modes and edge-reflections. The effects of propagation distance and source orientation are investigated while comparison is made between the three specimens. It is concluded that while the wave propagation modes can be approximated as being constant throughout all three specimens, there is a significant change in the received waveforms due to the attenuation of high-frequency components exhibited by the bonded specimen. These findings may be utilised to provide a deeper understanding of acquired AE data, improving the current abilities to identify, locate and characterise damage mechanisms occurring within adhesive joints, ultimately improving safety in the use of adhesive bonding for critical applications.

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Section: Acoustic Emission Instrumentationmentioning

confidence: 99%

“…It is capable of detecting multiple damage mechanisms (e.g. adhesive failure/interfacial debonding [3,6,9], cohesive failure/adhesive cracking [9,10], adherend matrix cracking [11], fibre breakage [3,11], sandwich core failure [11]) and is not limited by a minimum detectable defect size.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Acoustic Emission Propagation in Metal-to-Metal Adhesively Bonded Joints

2018

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“…The proposed approach successfully classified the AE signal associated with matrix cracking and interfacial decohesion. Pashmforoush et al [93] combined k-means algorithm with genetic algorithm for the clustering of various damage mechanisms in a mode I delamination test of a sandwich composite. Figure 7 shows the employed procedure for unsupervised damage classification.…”

Section: K-means and K-nearest Neighborsmentioning

confidence: 99%

“…The main purpose of using more than one learning algorithms could be one of the following: enhance the Fig. 7 A concise explanation of the damage classification procedure [93] discriminative capabilities of the damage features, extract discriminative features, reduce the dimensions of the discriminative features, identify the hidden pattern in the discriminate feature space, or to select the most appropriate discriminate features. The word ensemble classifiers have been used in the literature for a combination of classifier whose final predictive decision is a combination (typically by weighted or unweighted voting) of all the classifiers.…”

Section: Miscellaneousmentioning

confidence: 99%

Damage assessment of smart composite structures via machine learning: a review

et al. 2019

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Composite materials are heterogeneous in nature and suffer from complex non-linear modes of failure, such as delamination, matrix crack, fiber-breakage, and voids, among others. The early detection of damage in composite structures, such as airplanes, is imperative to avoid catastrophic failure and tragic consequences. This paper reports on the use of machine learning techniques for the damage assessment (i.e., detection, quantification, and localization) of smart composite structures. The success of the machine learning paradigm for damage assessment depends on the representational capability of the discriminative features for the problems of interest. However, from a practical standpoint, it is not possible to define a global or superset of discriminative features that could discriminate between damaged and undamaged states of the structures, and simultaneously make a distinction between various modes of failures. In addition, one machine learning algorithm may show optimum performance for the discriminative features of a particular problem but fails for others. This article focuses on a review of discriminative features and the corresponding machine learning algorithms (both supervised and unsupervised), for various types of damage in smart composite structures.

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“…[21][22][23]. Recently, AE has been utilized as an applicable technique to detect in-situ information from the damages that occur in laminated composites [5,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission-Based Methodology to Evaluate Delamination Crack Growth Under Quasi-static and Fatigue Loading Conditions

et al. 2017

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The aim of this study was to investigate the applicability of acoustic emission (AE) technique to evaluate delamination crack in glass/epoxy composite laminates under quasi-static and fatigue loading. To this aim, double cantilever beam specimens were subjected to mode I quasistatic and fatigue loading conditions and the generated AE signals were recorded during the tests. By analyzing the mechanical and AE results, an analytical correlation between the AE energy with the released strain energy and the crack growth was established. It was found that there is a 3rd degree polynomial correlation between the crack growth and the cumulative AE energy. Using this correlation the delamination crack growth was predicted under both the static and fatigue loading conditions. The predicted crack growth values was were in a good agreement with the visually recorded data during the tests. The results indicated that the proposed AE-based method has good applicability to evaluate the delamination crack growth under quasi-static and fatigue loading conditions, especially when the crack is embedded within the structure and could not be seen visually. B Milad Saeedifar

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Damage Classification of Sandwich Composites Using Acoustic Emission Technique and k-means Genetic Algorithm

Cited by 71 publications

References 24 publications

Analysis of Acoustic Emission Propagation in Metal-to-Metal Adhesively Bonded Joints

Analysis of Acoustic Emission Propagation in Metal-to-Metal Adhesively Bonded Joints

Damage assessment of smart composite structures via machine learning: a review

Acoustic Emission-Based Methodology to Evaluate Delamination Crack Growth Under Quasi-static and Fatigue Loading Conditions

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