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

Dia, Abdou; Dieng, Lamine; Gaillet, Laurent; Gning, Papa-Birame

doi:10.1016/j.heliyon.2019.e01414

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…Evaluation of the residual tensile strength following the impact of glass/epoxy laminates has been done by AE [33,34] and a damage threshold level was established to identify when mechanical properties of the material started to decreasing. There are also some studies on damage characterization of hybrid composite materials using the AE technique [35][36][37], showing the potential of this technique for hybrid configurations. Suresh Kumar et al [38] used AE amplitude, duration, counts, energy, rise angle (RA) and peak frequency for characterizing failure modes in hybrid composite laminates.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission based investigation on the effect of temperature and hybridization on drop weight impact and post-impact residual strength of hemp and basalt fibres reinforced polymer composite laminates

Kumar

Fotouhi

Saeedifar

et al. 2019

Composites Part B: Engineering

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This paper investigates the effect of temperature and hybridization on the impact damage evolution and post-impact residual strength of hemp/epoxy, basalt/epoxy and their hybrid laminates, using mechanical and acoustic emission (AE) based analysis. To start with, the specimens were impacted by a drop weight impact tower machine at two temperatures of 30˚C and 65˚C and then they were subjected to a three-point bending test for the assessment of their residual strength, while online AE signals were recorded during the test. The mechanical behavior of the laminates was evaluated through measurement of the impact force and absorbed energy. AE response of the slope of cumulative rise angle (RA) was used for identification of the severity of the impact-induced damage in the laminates. In addition, the sentry function was computed on the basis of the correlation between the mechanical strain energy stored in the materials and the acoustic energy propagates by fracture events, enabled evaluation of the amount of impact-induced damage. These results showed the hybridized laminates having a better resistance to impact damage at the elevated temperature (65 o C) compared with the nonhybridized laminates, whereas, in the case of the ambient temperature (30 o C), basalt/epoxy laminates had a higher impact damage resistance than other configurations. This study reveals

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Section: Introductionmentioning

confidence: 99%

Acoustic emission based investigation on the effect of temperature and hybridization on drop weight impact and post-impact residual strength of hemp and basalt fibres reinforced polymer composite laminates

Kumar

Fotouhi

Saeedifar

et al. 2019

Composites Part B: Engineering

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“…Yuan and Wang 293 Yuan and Wang 293 Bending fatigue damage Djabali et al 294 Lopato and Chady 295 Djabali et al 294 Djabali et al 294 Tensile-tensile fatigue Dia et al 296 Aparna et al 199 Wagner et al 297 Reid et al 298…”

Section: Fibre Content and Orientationmentioning

confidence: 99%

Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review

Wang

Zhong

Lee

et al. 2020

Advances in Mechanical Engineering

297

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Composite materials/structures are advancing in product efficiency, cost-effectiveness and the development of superior specific properties. There are increasing demands in their applications to load-carrying structures in aerospace, wind turbines, transportation, and medical equipment, etc. Thus robust and reliable non-destructive testing (NDT) of composites is essential to reduce safety concerns and maintenance costs. There have been various NDT methods built upon different principles for quality assurance during the whole lifecycle of a composite product. This paper reviews the most established NDT techniques for detection and evaluation of defects/damage evolution in composites. These include acoustic emission, ultrasonic testing, infrared thermography, terahertz testing, shearography, digital image correlation, as well as X-ray and neutron imaging. For each NDT technique, we cover a brief historical background, principles, standard practices, equipment and facilities used for composite research. We also compare and discuss their benefits and limitations, and further summarise their capabilities and applications to composite structures. Each NDT technique has its own potential and rarely achieves a full-scale diagnosis of structural integrity. Future development of NDT techniques for composites will be directed towards intelligent and automated inspection systems with high accuracy and efficient data processing capabilities.

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“…Andrejiova et al (2019) proposed a classification of damage from impact loadings on rubber-textile conveyor belts that occurs due to the impact of transported material. Dia et al (2019) proposed a combination of principal components analysis (PCA), k-means analysis, and CART for identifying damage to hybrid laminate aluminum/glass under quasi-static Kim et al, 2017 Subparts evaluated using failure tree analysis Detection Tsai et al, 2005 Using the Weibull equation for detecting fatigue damage Classification Andrejiova et al, 2019 Damage classification of rubber-textile conveyor belts using CART Failure analysis and fatigue loading. Similar studies have been carried out by Kim et al (2017) for CFRP materials.…”

Section: Tree-based Modelsmentioning

confidence: 99%

Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

Shamsirband¹,

Khansari

2021

J. Zhejiang Univ. Sci. A

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A loss of integrity and the effects of damage on mechanical attributes result in macro/micro-mechanical failure, especially in composite structures. As a progressive degradation of material continuity, predictions for any aspects of the initiation and propagation of damage need to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides material design, structural integrity and health need to be monitored carefully. Among the most powerful methods for the detection of damage are machine learning (ML) and deep learning (DL). In this paper, we review state-of-the-art ML methods and their applications in detecting and predicting material damage, concentrating on composite materials. The more influential ML methods are identified based on their performance, and research gaps and future trends are discussed. Based on our findings, DL followed by ensemble-based techniques has the highest application and robustness in the field of damage diagnosis.

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

Cited by 18 publications

References 21 publications

Acoustic emission based investigation on the effect of temperature and hybridization on drop weight impact and post-impact residual strength of hemp and basalt fibres reinforced polymer composite laminates

Acoustic emission based investigation on the effect of temperature and hybridization on drop weight impact and post-impact residual strength of hemp and basalt fibres reinforced polymer composite laminates

Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review

Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

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