Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials.

Damage in composite laminates evolves through complex interactions of different failure modes, influenced by load type, environment, and initial damage, such as from transverse impact. This paper investigates damage growth in cross-ply polymeric matrix laminates under tensile load, focusing on three primary failure modes: transverse matrix cracks, delaminations, and fiber breaks in the primary loadbearing 0-degree laminae. Acoustic emission (AE) techniques can monitor and quantify damage in real time, provided the signals from these failure modes can be distinguished. However, directly observing crack growth and related AE signals is challenging, making numerical simulations a useful alternative. AE signals generated by the three failure modes were simulated using modified step impulses of appropriate durations based on incremental crack growth. Linear elastic finite element analysis (FEA) was applied to model the AE signal propagating as Lamb waves. Experimental attenuation data were used to modify the simulated AE waveforms by designing arbitrary magnitude response filters. The propagating waves can be detected as surface displacements or surface strains depending upon the type of sensor employed. This paper presents the signals corresponding to surface strains measured by surface-bonded piezoelectric sensors. Fiber break events showed higher-order Lamb wave modes with frequencies over 2 MHz, while matrix cracks primarily exhibited the fundamental S₀ and A₀ modes with frequencies ranging up to 650 kHz, with delaminations having a dominant A₀ mode and frequency content less than 250 kHz. The amplitude and frequency content of signals from these failure modes are seen to change significantly with source–sensor distance, hence requiring an array of dense sensors to acquire the signals effectively. Furthermore, the reasonable correlation between the simulated waveforms and experimental acoustic emission signals obtained during quasi-static tensile test highlights the effectiveness of FEA in accurately modeling these failure modes in composite materials.

Composite structural members experience extensive and complex damage that accumulate in a relatively steady pace as the structure is quasi-statically loaded. This damage progression which starts as matrix cracks, delaminations, and random fiber breaks, turns unstable when groups of adjacent fibers, ranging from four to ten fibers fail together, after about 85% of ultimate strength, as reported in the literature. Identifying this critical damage that precedes the final fracture has been difficult even in laboratory specimens. There is little consensus on successful use of AE signals to differentiate failure modes. The inability of AE patterns to identify failure modes is likely caused by the limited frequency bandwidth of available AE sensors, and the high attenuation seen in AE signals particularly in the frequency range likely to be associated with fiber fractures. As a part of this study new acoustic emission sensors capable of measuring frequencies to 2 MHz were developed. In addition, composite specimens were instrumented with sufficient number of sensors to capture high frequency signals before they are attenuated. Unidirectional, cross-ply, and quasi-isotropic carbon-epoxy composite tensile specimens were monitored while they were statically loaded to failure. Distinctly different signals corresponding to the three failure modes could be observed. High frequency acoustic emission signals with frequencies well in excess of 1MHz, mostly seen in the last 20% of the loading cycle. Signals with frequencies in the range of 300 kHz to 700 kHz and duration of the order of 50 microseconds, were observed in cross ply and quasi-isotropic specimens, and are believed to be from matrix cracks. Fewer events with frequencies below 300 kHz and duration that exceeded about 200 microseconds are believed to be from delaminations. An important observation in this study is the appearance of groups of near identical waveforms, which are believed to be from clusters of adjacent fiber breaks, appearing in the last 15% of the loading cycle. The size of the individual groups of such AE signals and their number of groups increase as the final failure is approached. Hence, by monitoring such groups of AE waveforms, it may be possible to recognize the point at which the damage growth is turning unstable and it may still be possible to avert catastrophic failure.

This paper examines pulse thermographic nondestructive evaluation of flat bottom holes of isotropic materials. Different combinations of defect diameters and depths are considered. Thermographic Signal Reconstruction (TSR) method is used to analyze these results. In addition, a new normalization procedure is used to remove the dependence of thermographic results on the material properties and instrumentation settings during these experiments. Hence the normalized results depend only on the geometry of the specimen and the defects. These thermographic NDE procedures were also simulated using finite element technique for a variety of defect configurations. The data obtained from numerical simulations were also processed using the normalization scheme. Excellent agreement was seen between the results obtained from experiments and numerical simulations. Therefore, the scheme is extended to introduce a correlation technique by which numerical simulations are used to quantify the defect parameters.