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This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework's effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications. [ABSTRACT FROM AUTHOR]

Assessing the damage evolution in carbon-fiber-reinforced polymer (CFRP) composites is an intricate task due to their complex mechanical responses. The acoustic emission technique (AE) is a non-destructive evaluation tool that is based on the recording of sound waves generated inside the material as a consequence of the presence of active defects. Proper analysis of the recorded waves can be used for monitoring the damage evolution in many materials, including composites. The acoustic track associated with the entire loading history of the sample or the structures is usually followed by using some descriptors, such as the amplitude of the sound waves and the number of counts. In this study, the acoustic emission in CFRP single-lap shear joints was monitored by using a multiparameter approach based on the contemporary analysis of multiple features, such as the absolute signal level (ASL), initiation frequency, and reverberation frequency, to understand whether a proper combination of them can be adopted for a more robust description of the damage propagation in CFRP structures. For selecting the best features, principal component analysis (PCA) was used. The selected features were classified into different clusters using fuzzy c-means (FCM) data clustering for analyzing the damage modes. [ABSTRACT FROM AUTHOR]

Two different configurations of adhesive-bonded carbon fiber-reinforced plastic (CFRP) specimens, joggled lap-joint specimens and single lap-joint specimens, are mechanically tested. The mechanical tests show that the joggled lap specimens have lower strength than the single lap specimens. The damage modes in both the specimens are analysed by the Acoustic Emission descriptors recorded during the mechanical tests. The acoustic data as cumulative counts and cumulative energy show the critical points of failure in both the specimen groups under loading. Moreover, they also show that the damage modes in both the specimens are dissimilar. Finally, the data provided by acoustic emission descriptors are verified by fractographic analysis on the failed surface. [ABSTRACT FROM AUTHOR]

Abstract The acousto-ultrasonic approach is experimented on CFRP laminates. The test was organized in two modes: before the impact event and after the impact event; along longitudinal and transverse directions. Finally, the Compression after impact (CAI) test is performed on the impacted specimens to characterize its residual compressive strength. The AE descriptors loss along the longitudinal and transverse directions of the specimen are utilized to characterize the interlaminar strength of the specimen. With the help of Continuous Wavelet Transform (CWT) analysis, a hypothesis is created to discriminate the loss variation of AE descriptors to characterize the interlaminar strength of the material and the presence of damage or flaw in the material. The hypothesis holds true that the direction along which the relative loss of AE descriptors is low, has the lowest interlaminar strength. The mechanical results support the hypothesis created. With the proper channelling of acoustic signals and discrimination of data, the acousto-ultrasonic approach can be extended to characterize the interlaminar strength of the mechanical structures. [ABSTRACT FROM AUTHOR]

In the typical structures of carbon-fibre-reinforced plastics (CFRP), multiple layers are stacked together with a particular sequence in order to give specific mechanical characteristics. Layers are organized with different angles, different sequences and different technological processes to obtain innovative materials. This paper introduces a new parameter, able to improve mechanical properties of composites. It analyses an unconventional fibres orientation combined with the through-the-thickness stitching on the in-plane mechanical properties of CFRP. Conventional carbon fibres orientation is generally referred to a Cartesian coordinate system, in which fibres are arranging in bundles along different angle orientations respect to the zero lamina. In this paper a polar coordinate system of continue carbon tow is introduced to create specimens as well as complex geometry components in an easy way. Five different configurations are studied: two are classified as traditional composites (unstitched and Cartesian orientation of woven carbon cloth), three are defined as innovative composites (stitched laminates and continue carbon tow organized in polar or Cartesian system). The application of this technique aims to introduce some advantages in the material's manufacturing process and if compared with the conventional strategy seems to be very promising since it appears to be very efficient in stopping delamination phenomenon. [ABSTRACT FROM AUTHOR]

In this research work, a deep Convolutional Neural Network (CNN) was trained for image-based Acoustic Emission (AE) waveform classification. AE waveforms from different damage modes of Carbon Fibre Reinforced Polymer (CFRP) composites were used to train the CNN for online damage monitoring. Spectrograms of AE Waveforms from four different damage modes, matrix cracking, delamination, debonding, and fibre breakage, were obtained in their Mel scale and used as the training data and test data for the CNN. The overall prediction accuracy of the CNN is 97.9%, while the fibre breakage and delamination events were able to be predicted with 100% accuracy. Then this pre-trained CNN is used for online damage monitoring of mode I delamination test of CFRP specimens. AE waveforms generated during the mode I test are classified using the trained CNN and the results are analysed in terms of the classified AE descriptors. The classified AE descriptors proved to identify the occurrences of different damage modes, thereby validating the damage classification accuracy of the CNN. [ABSTRACT FROM AUTHOR]

In the last decades, the increasing use of laminate materials, such as carbon fibre reinforced plastics, in several engineering applications has pushed researchers to deeply investigate their mechanical behavior, especially in consideration of the delamination process, which could affect their performance. The need for improving the capability of the current instruments in predicting some collapse or strength reduction due to hidden damages leads to the necessity to combine numerical models with experimental campaigns. The validation of the numerical models could give useful information about the mechanical response of the materials, providing predictive data about their lifetime. The purpose of the delamination tests is to collect reliable results by monitoring the delamination growth of the simulated in situ cracking and use them to validate the numerical models. In this work, an experimental campaign was carried out on high performance composite laminates with respect to the delamination mode I; subsequently, a numerical model representative of the experimental setup was built. The ANSYS Workbench Suite was used to simulate the delamination phenomena and modeFRONTIER was applied for the numerical/experimental calibration of the constitutive relationship on the basis of the delamination process, whose mechanism was implemented by means of the cohesive zone material (CZM) model. [ABSTRACT FROM AUTHOR]

The present work describes the mechanical characterization combined with the thermal degradation kinetics of Carbon Fiber Reinforced Polymers (CFRP). The thermal degradation kinetics of CFRP have never been studied in the past. In that regard, the present work focuses on studying the thermal degradation kinetics of CFRP tested mechanically at different environmental conditions. Tensile tests were performed on the specimens with different lay-ups at room temperature, elevated temperature (71 °C), and cryogenic conditions (−54 °C), and the same specimens were used for thermal degradation kinetic studies. Mechanical tests show different responses respect to the different environmental conditions and different fibers orientation. On the other hand, the thermogravimetric results, mass loss, and derivative mass loss, show no significant difference in the degradation of CFRP tested at different temperatures. However, the thermal degradation kinetics shows more insight into the degradation pattern of the materials. The activation energy of degradation shows that the degradation of materials subjected to elevated conditions increases rapidly in the later stages of degradation, suggesting the formation of high char yield. The varying activation energy has been related to different degradation mechanisms. Lastly, the morphology of the materials was studied under SEM to understand the structural change in the material after tested in different weather conditions. [ABSTRACT FROM AUTHOR]