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Composite materials have been widely used in the aerospace industry and are critical for safe operations. However, the delamination, caused by cyclic loads and corrosive service environment, poses a serious threat to the structural integrity of composite laminates. The acoustic emission technique has been adopted to assess the structural integrity by characterizing damage location, type, and size. This article proposes and compares data-driven prognostic methods to quantify the delamination area efficiently and accurately. To address the problem of insufficient inspection data, the prediction model adopts the path length across the delamination area as the target value. The delamination area can then be estimated with the predicted path length based on formulated geometric relationships. This solution will augment the model training datasets, and consequently, avoid the overfitting problem during the training process. Experimental results on composite coupons demonstrate that the proposed ensemble learning-based model outperforms other state-of-the-art methods in terms of prediction accuracy and efficiency. [ABSTRACT FROM AUTHOR]

The load-bearing capacity of damaged composite materials can be improved using the double adhesive repair technique. This study uses acoustic emission (AE) as the primary detection method to examine the damage evolution of modified double-bonded repair specimens and discover the most effective configuration for bi-adhesive repair. The introduction of machine learning and signal feature extraction in the analysis of huge acoustic emission parameters has significantly improved the accuracy and efficiency of damage classification. Following the preliminary division of the clustering results of the unsupervised algorithm, the corresponding relationship between various clustering types and damage types is confirmed using the energy characteristics of wavelet packet decomposition. Parameter and waveform analysis are extensively utilized in studying the AE damage of composite materials and have complementary advantages. Thus, a supervised classifier is established by combining characteristic parameters with time-frequency domain analysis of the signal waveform. Furthermore, machine learning is used to process the large signal set and analyze the debonding damage behavior of the patch. In the end, it is shown that the specimens with the modified adhesive in the middle of the repair area displayed superior repair results. Hence, when combined with machine learning, the signal classification of the damage evolution behavior of repaired specimens is realized with high accuracy. [ABSTRACT FROM AUTHOR]