Defect severity classification of complex composites using CWT and CNN
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...
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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 出版事項: |
Springer Science
2022
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| 主題: | |
| オンライン･アクセス: | http://eprints.um.edu.my/43264/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126384070&doi=10.1007%2f978-981-16-8484-5_14&partnerID=40&md5=fee6e4800325d12237fd9c2a6319746b |
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| 要約: | 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. |
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