The aim of this research is to investigate the failure mechanisms of the filament-wound composite tubes under axial compressional loading by using an acoustic emission approach. First, the mechanical properties of ±45°C composite tubes were obtained experimentally. Then, failure due to the buckling phenomenon and crashworthiness characteristics were studied utilizing numerical simulation and experimental methods. Tubes were next simulated in ABAQUS software, and a continuum damage mechanics model was implemented in a progressive framework to assess the failure modes. From the macroscale view, results showed that the damage behavior of composite tubes turned out to be dominated by local buckling followed by a post-buckling field, which is generated by longitudinal cracks along the winding direction. On the micro-scale, the acoustic emission-based procedure based on the wavelet packet transform method was adopted. The hierarchical modeled assessment resulted in the identity of four clusters of AE signals. In GFRP tubes, the fiber breakage and fiber/matrix separation could mostly control the higher percentage of damage and cause to increase the energy absorption. Finally, by comparing the results obtained from micro and macro scales, the local buckling failure mode was attributed to the low content of fiber/matrix debonding in the structure.

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This study investigates the mechanical behavior and damage mechanisms of thin-walled glass/epoxy filament wound tubes under quasi-static lateral loads. The novelty is that the tubes are reinforced in critical areas using strip composite patches to provide a topology-optimized tube, and their damage mechanisms and mechanical performance are compared to that of un-reinforced (reference) tubes. To detect the types of damage mechanisms and their progression, the Acoustic Emission (AE) method is employed, accompanied by data clustering analysis. The loading conditions are simulated using the finite element method, and the results are validated through experimental testing. The findings confirm that the inclusion of reinforcing patches improves the stress distribution, leading to enhanced load carrying capacity, stiffness, and energy absorption. Compared to the reference tubes, the reinforced tubes exhibit a remarkable increase of 23.25% in the load carrying capacity, 33.46% in the tube’s stiffness, and 23.67% in energy absorption. The analysis of the AE results reveals that both the reference and reinforced tubes experience damage mechanisms such as matrix cracking, fiber-matrix debonding, delamination, and fiber fracture. However, after matrix cracking, delamination becomes dominant in the reinforced tubes, while fiber failure prevails in the reference tubes. Moreover, by combining the AE energy and mechanical energy using the Sentry function, it is observed that the reinforced tubes exhibit a lower rate of damage propagation, indicating superior resistance to damage propagation compared to the reference tubes.@en

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring technologies for composite tubes experiencing progressive damage were investigated. The challenges associated with quantitative failure monitoring were addressed, and the Genetic K-means algorithm, hierarchical clustering, and artificial neural network (ANN) methods were employed along with other three alternative methods. The impact characteristics and damage mechanisms of composite tubes under axial compressive load were assessed using Acoustic Emission (AE) monitoring and machine learning.Various failure modes such as matrix cracking, delamination, debonding, and fiber breakage were induced by layer bending. An increase in fibers/matrix separation and fiber breakage was observed with altered failure modes, while matrix cracking decreased Signal classification was achieved using hierarchical and K-means genetic clustering methods, providing insights into failure mode frequency ranges and corresponding amplitude ranges. The ANN model, trained with labeled data, demonstrated high accuracy in classifying data and identifying specific failure mechanisms. Comparative analysis revealed that the Random Forest model consistently outperformed the ANN and Support Vector Machine (SVM) models, exhibiting superior predictive accuracy and classification using ACC, MCC and F1-Score metrics. Moreover, our evaluation emphasized the Random Forest model's higher true positive rates and lower false positive rates. Overall, this study contributes to the understanding of model selection, performance assessment in machine learning, and failure detection in composite structures.

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By analyzing the failure mechanisms, crashworthiness characteristics of FW composite tubes subjected to two modes of progressive damage and catastrophic failure are investigated using acoustic emission technique and numerical method. The AE signals of ±45° composite tubes were classified using hierarchical and wavelet transform methods, and based on the realistic and three-dimensional geometrical architecture of tubular structures, the microstructural finite element model was developed using Catia and ABAQUS software. Then deformation patterns and the impression of each mechanism on the crashworthiness characteristics were assessed. Results indicated that fiber breakage and fiber/matrix debonding could likely control the higher percentage of damage. By changing the type of modes from progressive damage to catastrophic failure, the percentage of matrix cracking increases, the fiber/matrix separation decreases, and the failure behavior become dominated by local buckling. Comparing the FE simulation with experimental results, we found the proposed 3D model can reasonably predict the pre-crushing, post-crushing, and material densification.

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The focus of this study is to investigate the mechanical properties, of ±35° and ±55° filament wound (FW) composite tubes under axial compression loading using the acoustic emission technique. For this purpose, material failure, crashworthiness characteristics, and the effect of each mechanism on the energy absorption capacity were studied using numerical and experimental approaches. Also, to identify and estimate the contribution percentage of damage mechanisms as well as how the damage grows in the specimens, the analysis of acoustic emission signals recorded during loading was performed. Digital image correlation was additionally used to capture displacement/strain contour maps. Finally, to analyze the effect of the winding pattern in the experimental test, the tubes were simulated using finite element analysis (FEA). For modeling of damage mechanisms, a 3D continuum damage model was used. The results of signal processing showed that by increasing the weaving angle of fibers from ±35° to ±55°, the separation of fibers from the matrix decreases, and the percentage of matrix crushing and fiber failure increases. The assessment of damage percentages showed that the reason for the large drop in force at ±55° compared to ±35° is the increase in matrix crushing. Furthermore, the failure behavior of FW tubes appeared to be dominated by local buckling, and the FEA effectively predicted the linear behavior and maximum load value of the composite tubes.

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