Prediction of delamination growth in carbon/epoxy composites using a novel acoustic emission-based approach

Mohammadi, Reza; Saeedifar, Milad; Hosseini‐Toudeshky, Hossein; Najafabadi, Mehdi Ahmadi; Fotouhi, Mohamad

doi:10.1177/0731684415583166

Cited by 45 publications

(19 citation statements)

References 20 publications

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“…Figure 5 also shows the errors bar of the results. This confirms that large deformations occurred for FDM specimens, as expected, especially if created at high temperature; in this case, correction of G I is mandatory for comparison with CFRP specimens [ 32 , 33 ]. Indeed, the most significant reduction in G I toughness was observed for the group manufactured at 240 °C.…”

Section: Resultssupporting

confidence: 79%

“…This predictive capability is more evident if the extrusion temperature is higher; samples in group C display the greatest increase in slope, providing confirmation of higher acoustic activity compared to the other groups. This advantage has also been noted for several other materials [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ], and is very important for FDM parts, given that, in most cases, delamination occurs internally and is not visible.…”

Section: Resultsmentioning

confidence: 61%

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Acoustic Emissions in 3D Printed Parts under Mode I Delamination Test

2018

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This paper applies an innovative approach based on the acoustic emission technique to monitor the delamination process of 3D parts. Fused deposition modelling (FDM) is currently one of the most widespread techniques for additive manufacturing of a solid object from a computer model. Fundamentally, this process is based on a layer-by-layer deposition of a fused filament. The FDM technique has evolved to the point where it can now be proposed, not only as a prototyping technique, but also as one applicable to direct manufacturing. Nonetheless, a deeper comprehension of mechanical behavior and its dependence on process parameters must include the determination of material properties as a function of the service load. In this work, the effects of extrusion temperature on inter-layer cohesion are studied using a method employing a double cantilever beam (DCB). The ASTM D5528 standard was used to determine the delamination energy, GI. In addition, the acoustic emission technique was employed to follow the delamination process during testing. Finally, a Charge-Coupled Device (CCD) camera and a calibrated grid was employed to evaluate crack propagation during testing.

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Section: Resultssupporting

confidence: 79%

Section: Resultsmentioning

confidence: 61%

Acoustic Emissions in 3D Printed Parts under Mode I Delamination Test

2018

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“…Non-Destructive Evaluation (NDE) techniques are capable tools for examining the integrity of laminated composite structures [10][11][12]. Among the NDE techniques, Acoustic Emission (AE) is widely utilized for health monitoring of the composite structures [13][14][15][16][17][18]. AE is defined as the propagation of a transient elastic wave within the material caused by a sudden release of strain energy which can be due to the occurrence of a damage [19].…”

Section: Introductionmentioning

confidence: 99%

Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

Saeedifar

Najafabadi

Zarouchas

et al. 2018

Composites Part B: Engineering

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147

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This study focuses on the clustering of the indentation-induced interlaminar and intralaminar damages in carbon/epoxy laminated composites using Acoustic Emission (AE) technique. Two quasi-isotropic specimens with layups of [60/0/-60] 4S (is named dispersed specimen) and [60 4 /0 4 /-60 4 ] S (is named blocked specimen) were fabricated and subjected to a quasi-static indentation loading. The mechanical data, digital camera and ultrasonic C-scan images of the damaged specimens showed different damage evolution behaviors for the blocked and dispersed specimens. Then, the AE signals of the specimens were clustered for tracking the evolution behavior of different damage mechanisms. In order to select a reliable clustering method, the performance of six different clustering methods consisting of k-Means, Genetic k-Means, Fuzzy C-Means, Self-Organizing Map (SOM), Gaussian Mixture Model (GMM), and hierarchical model were compared. The results illustrated that hierarchical model has the best performance in clustering of AE signals. Finally, the evolution behavior of each damage mechanism was investigated by the clustered AE signals with hierarchical model. The results of this study show that using AE technique with an appropriate clustering method such as hierarchical model could be an applicable tool for structural health monitoring of composite structures.

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“…Damage and failure characterization by the combination of AE data and mechanical data. In this section, the sentry function f(x) introduced by [21], has been utilized to validate our proposed clustering technique and increase the sensitivity of the damage and failure regions classification. The sentry function is defined as the logarithm of mechanical energy or stress E m (X i ) to the AE energy or cumulative Abs energy E AE ðX 0 i Þ.…”

Section: Plos Onementioning

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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Prediction of delamination growth in carbon/epoxy composites using a novel acoustic emission-based approach

Cited by 45 publications

References 20 publications

Acoustic Emissions in 3D Printed Parts under Mode I Delamination Test

Acoustic Emissions in 3D Printed Parts under Mode I Delamination Test

Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

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

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