Detectability of Damages in Carbon Fiber Reinforced Plastics using Acoustic Emission

This contribution examines the usefulness of Acoustic Emissions (AE) as a non-destructive testing (NDT) method for detecting and distinguishing damages in carbon fiber reinforced polymer (CFRP) structures. Despite the widespread use of CFRP materials in various industries due to their favorable strength-to-weight ratio, the susceptibility to concealed internal damages necessitates advanced inspection techniques. Acoustic Emission, describing the use of ultrasonic waves emitted during deformation or damage events, is a proven and promising solution for real-time and reliable damage assessment. The study focuses on comparing two approaches: 1) a one-class Support Vector Machine (SVM) for initial damage detection, followed by detailed damage classification, and 2) a direct classification approach using five classes (four representing the material specific damage types and one for background noise). Both approaches undergo a systematic evaluation under diverse loading conditions to assess their reliability. A comprehensive experimental setup subjects CFRP specimens to controlled loading conditions, inducing various damage types and severities. Signal analysis reveals characteristic patterns associated with different damage modes, including matrix cracking, fiber breakage, debonding, and delamination. The investigation considers the influence of loading conditions on the detection and classification results to examine the robustness of the approach. The comparison between methodologies involves established metrics and analyses the posterior probability of the trained models, considering the impact of loading conditions on performance. The experimental results show AE’s effectiveness in detecting and classifying damages in CFRP structures, offering insights into technique sensitivity and specificity for different damage types. These findings contribute new knowledge to the NDT field, presenting a promising path for the advancement of CFRP structural health monitoring and maintenance practices in engineering applications. The study’s nuanced understanding of the strengths and limitations of the two classification approaches, considering loading conditions, contributes to the optimization of NDT strategies for diverse operation scenarios.