Defect severity classification of complex composites using CWT and CNN

Wilson, Lim and Mohd Khairuddin, Anis Salwa and Khairuddin, Uswah and Murat, Bibi Intan Suraya (2022) Defect severity classification of complex composites using CWT and CNN. Lecture Notes in Electrical Engineering, 834. 165 – 171. ISSN 1876-1100, DOI

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Composite structures are prone to internal defects such as delamination. Due to this, it is vital to recognize internal flaws in composite materials accurately because there is possibility that these internal defects can severely degrade the composite structure’s strength. This work aims to develop an intelligent complex composite defect severity classification which will contribute to efficient monitoring of composite structures during their service life. Firstly, the behavior of guided ultrasonic waves is processed and transformed into image database using continuous wavelet transform method. Then, a defect classification framework is proposed by using convolutional neural network to classify six types of defect sizes. A total of 798, 342, and 90 images are used for training, validation, and testing, respectively. The results present that the proposed system achieved approximately above 86 of precision and recall for all six defects classes. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Funders: None
Additional Information: Cited by: 0; Conference name: International Conference on Computational Intelligence in Machine Learning, ICCIML 2021; Conference date: 1 June 2021 through 2 June 2021; Conference code: 274369
Uncontrolled Keywords: Complex networks; Composite structures; Convolutional neural networks; Deep learning; Defects; Glass ceramics; Structure (composition); Complex composites; Composite defects; Composites material; Composites structures; Deep learning; Defect severity classification; Internal defects; Internal flaws; Signal classification; Wavelets transform; Wavelet transforms
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Nov 2023 13:26
Last Modified: 03 Dec 2023 01:57

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