Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Oh, Hyun-Taik; Won, Jong-Ick; Woo, Sung-Choong; Kim, Tae−Won

doi:10.3390/ma13225207

Cited by 11 publications

(7 citation statements)

References 46 publications

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“…The density matrix cracking was predicted to be higher at interface bonding between carbon fibre and matrix than PALF and matrix. When the applied impact energy is beyond a certain level, the matrix cracking reaches the maximum level, exceeding energy causing the second mode of internal damage mechanism, delamination [ 71 ]. Delamination is a fracture that occurs between plies with various fibre orientations in the matrix-rich region [ 72 ].…”

Section: Resultsmentioning

confidence: 99%

Effects of Ply Orientations and Stacking Sequences on Impact Response of Pineapple Leaf Fibre (PALF)/Carbon Hybrid Laminate Composites

et al. 2022

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This study investigated the impact response behaviours of pineapple leaf fibre (PALF)/carbon hybrid laminate composites for different ply orientations and stacking sequences. The laminates were manufactured using a vacuum infusion approach with various stacking sequences and ply orientations classified as symmetric quasi-isotropic, angle-ply symmetric, and cross-ply symmetric. The laminates were analysed using an IMATEK IM10 drop weight impact tester with an increment of 5 J until the samples were perforated. This investigation reveals that the overall impact properties of PALF and carbon as reinforcements were improved by a beneficial hybridised effect. The laminates with an exterior carbon layer can withstand high impact energy levels up to 27.5 J. The laminate with different stacking sequences had a lower energy transfer rate and ruptured at higher impact energy. The laminates with ply orientations of [0°/90°] and [±45°]8 exhibited 10% to 30% better energy absorption than those with ply orientations of [±45°2, 0°/90°2]s and [0°/90°2, ±45°2]s due to energy being readily transferred within the same linear ply orientation. Through visual inspection, delamination was observed to occur at the interfaces of different stacking sequences and ply orientations.

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Section: Resultsmentioning

confidence: 99%

Effects of Ply Orientations and Stacking Sequences on Impact Response of Pineapple Leaf Fibre (PALF)/Carbon Hybrid Laminate Composites

et al. 2022

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“…As the impact penetrates more, the strain, bending and shear stress increases to a point where fibres break. [37][38][39][40] The force-time curves of S 4 and S 5 in comparison to S 1 are shown in Figure 6(c) and (d). The graph in Figure 6(a) and (b) shows a decreasing trend of impact force for 45°a ngle ply laminates, whereas 45°multiaxis woven plies shows increasing trend in impact force.…”

Section: Force Time Historymentioning

confidence: 99%

Novel method of multiaxis weaving and impact analysis of the ensuing composites

Shimpi,

Sridharan,

Monastyreckis

et al. 2024

Journal of Composite Materials

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The study aims to develop a novel method of weaving multiaxis preform on conventional weaving machines and analyse impact tolerance of the ensuing composites along with development of finite element analysis model. An extra set of 200 tex carbon fibres, referred as bias fibres, were integrated into carbon fibre plain woven fabric during the weaving stage on a conventional closed reed weaving machine. This was achieved by partial carbon fibre weft insertion at regular intervals from both edges of the fabric. The multiaxis preform was used as ply on top of plain woven carbon fabric plies, each of 160 g per square meter aerial density. The carbon fibre composites were manufactured by infusing bisphenol F epoxy resin as a matrix and 4 plies of carbon fabric by hand layup method. The composites were subjected to low-velocity impact loading at 0.4 m height of 5.12 kg impactor, and force-time history was recorded by the piezoelectric transducer. A comparison of impact loading results showed that the multiaxis woven composites increase impact strength by 18.3% due to the reinforcement of bias fibre. Based on the test results, a finite element analysis model was developed in Ansys v.19 to simulate the stress distribution during impact loading of the multiaxis woven composites.

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“…To investigate the damage situation of structures, these characteristic parameters can be used individually or in combination [18]. When certain acoustic emission characteristic parameters are combined, damage pattern recognition can be achieved, thereby identifying the acoustic emission features of specific damage patterns [19][20][21][22]. Therefore, the application of acoustic emission technology in the field of structural health monitoring (SHM) is gaining increasing attention [23].…”

Section: Introductionmentioning

confidence: 99%

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

Cui,

Zhang,

et al. 2024

JMSE

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Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

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Determination of Impact Damage in CFRP via PVDF Signal Analysis with Support Vector Machine

Cited by 11 publications

References 46 publications

Effects of Ply Orientations and Stacking Sequences on Impact Response of Pineapple Leaf Fibre (PALF)/Carbon Hybrid Laminate Composites

Effects of Ply Orientations and Stacking Sequences on Impact Response of Pineapple Leaf Fibre (PALF)/Carbon Hybrid Laminate Composites

Novel method of multiaxis weaving and impact analysis of the ensuing composites

An Improved Identification Method of Pipeline Leak Using Acoustic Emission Signal

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