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Data-Driven Approaches for Characterization of Delamination Damage in Composite Materials | IEEE Journals & Magazine | IEEE Xplore

Data-Driven Approaches for Characterization of Delamination Damage in Composite Materials


Abstract:

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 ...Show More

Abstract:

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.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 68, Issue: 3, March 2021)
Page(s): 2532 - 2542
Date of Publication: 20 February 2020

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