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...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Conference or Workshop Item |
Published: |
2022
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/98985/ http://dx.doi.org/10.1007/978-981-16-8484-5_14 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.98985 |
---|---|
record_format |
eprints |
spelling |
my.utm.989852023-02-22T03:11:53Z http://eprints.utm.my/id/eprint/98985/ Defect severity classification of complex composites using CWT and CNN Lim, Wilson Mohd. Khairuddin, Anis Salwa Khairuddin, Uswah Murat, Bibi Intan Suraya TJ Mechanical engineering and machinery 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. 2022-07 Conference or Workshop Item PeerReviewed Lim, Wilson and Mohd. Khairuddin, Anis Salwa and Khairuddin, Uswah and Murat, Bibi Intan Suraya (2022) Defect severity classification of complex composites using CWT and CNN. In: International Conference on Computational Intelligence in Machine Learning, ICCIML 2021, 1 June 2021 - 2 June 2021, Virtual, Online. http://dx.doi.org/10.1007/978-981-16-8484-5_14 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
TJ Mechanical engineering and machinery |
spellingShingle |
TJ Mechanical engineering and machinery Lim, Wilson Mohd. Khairuddin, Anis Salwa Khairuddin, Uswah Murat, Bibi Intan Suraya Defect severity classification of complex composites using CWT and CNN |
description |
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. |
format |
Conference or Workshop Item |
author |
Lim, Wilson Mohd. Khairuddin, Anis Salwa Khairuddin, Uswah Murat, Bibi Intan Suraya |
author_facet |
Lim, Wilson Mohd. Khairuddin, Anis Salwa Khairuddin, Uswah Murat, Bibi Intan Suraya |
author_sort |
Lim, Wilson |
title |
Defect severity classification of complex composites using CWT and CNN |
title_short |
Defect severity classification of complex composites using CWT and CNN |
title_full |
Defect severity classification of complex composites using CWT and CNN |
title_fullStr |
Defect severity classification of complex composites using CWT and CNN |
title_full_unstemmed |
Defect severity classification of complex composites using CWT and CNN |
title_sort |
defect severity classification of complex composites using cwt and cnn |
publishDate |
2022 |
url |
http://eprints.utm.my/id/eprint/98985/ http://dx.doi.org/10.1007/978-981-16-8484-5_14 |
_version_ |
1758578047089377280 |
score |
13.160551 |