A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates
Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require te...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/826 |
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| author | Muhammad Haris Yazdani Muhammad Muzammil Azad Salman Khalid Heung Soo Kim |
| author_facet | Muhammad Haris Yazdani Muhammad Muzammil Azad Salman Khalid Heung Soo Kim |
| author_sort | Muhammad Haris Yazdani |
| collection | DOAJ |
| description | Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures. |
| format | Article |
| id | doaj-art-21cdb4fdc48741549ca1add6ea02e643 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-21cdb4fdc48741549ca1add6ea02e6432025-08-20T03:12:35ZengMDPI AGSensors1424-82202025-01-0125382610.3390/s25030826A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite LaminatesMuhammad Haris Yazdani0Muhammad Muzammil Azad1Salman Khalid2Heung Soo Kim3Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaStructural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures.https://www.mdpi.com/1424-8220/25/3/826vibration signalsdelamination detectiondelamination identificationtransfer learningdeep learninghybrid model |
| spellingShingle | Muhammad Haris Yazdani Muhammad Muzammil Azad Salman Khalid Heung Soo Kim A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates Sensors vibration signals delamination detection delamination identification transfer learning deep learning hybrid model |
| title | A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates |
| title_full | A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates |
| title_fullStr | A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates |
| title_full_unstemmed | A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates |
| title_short | A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates |
| title_sort | hybrid deep transfer learning framework for delamination identification in composite laminates |
| topic | vibration signals delamination detection delamination identification transfer learning deep learning hybrid model |
| url | https://www.mdpi.com/1424-8220/25/3/826 |
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