Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning

Barile, Claudia; Casavola, Caterina; Pappalettera, Giovanni; Kannan, Vimalathithan Paramsamy

doi:10.1016/j.compstruct.2022.115629

Cited by 58 publications

(10 citation statements)

References 29 publications

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“…Smearing and plowing were responsible for the formation of a smoother surface, which prevented a sudden increase in the SWR of the composite. Under an applied load of 120 N, delamination and fiber breakage were observed, leading to an increased SWR, because the broken fibers no longer reinforced the matrix material 61 . Further investigation of the SEM images of the composite under an applied load of 140 N revealed plastic deformation that increased the SWR by increasing the surface roughness and contact area between the two surfaces, ultimately leading to more wear.…”

Section: Resultsmentioning

confidence: 99%

Friction and wear characteristics of Furcraea foetida fiber‐reinforced epoxy composites

Hussain,

Mohd Jamir,

Abdul Majid

et al. 2023

Polymer Composites

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In the current study, the friction and wear properties of Furcraea foetida fiber‐reinforced epoxy composites were investigated. This study evaluated the effect of Furcraea foetida fiber content on the tribological behavior of composites. A water‐retting method was employed to extract the fibers, and resin transfer molding was used to fabricate the composites. The frictional force, coefficient of friction (COF), and wear rate were measured under dry test conditions and various applied loads (80, 100, 120, and 140 N). Microstructural analysis was conducted using scanning electron microscopy to investigate the surface morphology and wear mechanisms of the composites. The results revealed that the composite with 50 vol% of fibers exhibits the lowest frictional force and COF in both normal and parallel orientations with overall average COF of 0.43 in normal orientation and 0.42 in parallel orientation. Moreover, the composite with 60 vol% demonstrates the best wear resistance with an overall average specific wear rate of 2.1982 × in normal orientation and 4.3478 × () in parallel orientation. Overall, this study provides valuable insights into the tribological behavior of Furcraea foetida fiber‐reinforced epoxy composites and identifies the optimal volume fractions for improved friction and wear performance.Highlights Higher vol% yields denser composites with minimized voids. Normal fiber orientation shows maximum SWR of 10.0614 × () . Optimal frictional force and COF in 50 vol% composite. Enhanced wear resistance observed in 60 vol% composite. Higher vol% increases interfacial bonding and wear resistance in composites.

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Section: Resultsmentioning

confidence: 99%

Friction and wear characteristics of Furcraea foetida fiber‐reinforced epoxy composites

Hussain,

Mohd Jamir,

Abdul Majid

et al. 2023

Polymer Composites

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“…Inspection methods, such as X-ray tomography [1][2][3] and ultrasonic scanning, [4][5][6] are very effective for detecting defects in composites but require removing the part from the structure and are not suited for real-time monitoring. Nondestructive methods, such as acoustic emissions 3,7 and ultrasonic-guided waves (Lamb waves), 8 offer the advantages of in situ and real-time monitoring of the structures. However, those techniques require additional devices (sensors) attached to the analyzed structure and generally detect damage only at their immediate vicinity, thus limiting the sensing area.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring of carbon nanotube-modified glass fiber-reinforced polymer composites by electrical resistance measurements and digital image correlation

Uribe-Riestra,

Ayuso-Faber,

Rivero-Ayala

et al. 2023

Structural Health Monitoring

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A method based on changes of electrical resistance was used to evaluate non-visible damage inflicted to multiscale hierarchical composites subjected to monotonic and cyclic bending loads. The composites comprise glass fiber weaves modified by carbon nanotubes in a vinyl ester matrix. Damage sensing is achieved by placing an array of electrodes close to the surfaces of four-point bending specimens and is correlated to strain fields measured by digital image correlation (DIC). The top (compressive) surface exhibited lower electrical resistance changes than the bottom (tensile) one. Spatial measurements of electrical resistance allowed identification of the most severely damaged zones, which coincided with those pinpointed by DIC. DIC also indicated an important presence of irreversible interlaminar shear strains accumulating close to the supports and/or loading introduction elements, which coincided with the location of delamination. The electrical technique allowed not only the detection of the onset of damage in the form of initial fiber breakage and matrix cracking, but also the detection of damage progression under cyclic loading and low-velocity impact.

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“…One of the greatest benefits of the parameter-based feature encoding is that it can significantly reduce the usage of storage and processing resources while maintaining the general features of the original signals. Threshold-based features are frequently used in the scenarios where the background noise is continuous and much weaker than the signals we are really interested in capturing [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor

Li,

Yang,

wang

et al. 2023

Meas. Sci. Technol.

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Fused filament fabrication (FFF) is one of the most popular techniques of additive manufacturing. However, product quality issues still limit the further application of FFF technology. Filament extrusion state has a great influence on the quality of FFF fabricated products, since both under-extrusion and over-extrusion can lead to the deterioration of product quality. Therefore, monitoring the filament extrusion states is vital and essential. This paper aims to monitor the filament extrusion state by acoustic emission (AE). To achieve this goal, experiments are conducted on a desktop FFF machine, where the states of under-extrusion and over-extrusion are induced by different extrusion speeds. Original AE signals are collected during the experiments. Confronted with the challenge posed by the susceptibility of AE signals to noise during the complex extrusion process and different conditions, one calculates the statistical distribution of the features defined on the raw AE signals, without the need for noise reduction steps. The k-nearest neighbor algorithm is then adopted to identify the different extrusion states, where the Bhattacharyya distance is employed to measure the distances or similarities of the calculated distributions. The findings demonstrate the successful identification of various extrusion states induced by different extrusion speeds through the presented method. The outcomes of this study pave the way for the development of an affordable in-situ FFF monitoring system with comprehensive capabilities.

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Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning

Cited by 58 publications

References 29 publications

Friction and wear characteristics of Furcraea foetida fiber‐reinforced epoxy composites

Friction and wear characteristics of Furcraea foetida fiber‐reinforced epoxy composites

Structural health monitoring of carbon nanotube-modified glass fiber-reinforced polymer composites by electrical resistance measurements and digital image correlation

Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor

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