Intelligent defect detection in composite materials: the role of deep learning and machine learning fusion
Nondestructive Testing and Evaluation, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/10589759.2026.2713682
- Dergi Adı: Nondestructive Testing and Evaluation
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: composite materials, defect detection, machine learning algorithms, Non-destructive testing (NDT), transfer learning
- Sivas Cumhuriyet Üniversitesi Adresli: Evet
Özet
This study presents a novel integration of deep learning (DL) and machine learning (ML) for non-destructive testing (NDT) of composite materials, achieving superior defect detection performance. By combining pre-trained convolutional neural networks (CNNs), such as DenseNet169 and ResNet152, with ML algorithms like Multi-Layer Perceptron (MLP) and K-Nearest Neighbours (KNN), we leverage transfer learning and feature selection (Variance Threshold and XGBoost) to enhance classification accuracy. Experiments on the USimgAIST dataset (7,004 ultrasonic images) demonstrate that the DenseNet169 + MLP model achieved a state-of-the-art accuracy of 99.57%, Cohen’s Kappa of 0.9912, and only 2 false negatives against 4 false positives on the test set, minimising the risk of undetected structural defects. Feature selection significantly reduces computational complexity while maintaining high sensitivity (99.71%) and specificity (99.45%). These results highlight the potential of DL-ML fusion for automated quality control and structural integrity assessment in composite materials. Future work will focus on optimising real-time applications and extending the approach to diverse datasets.