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Journal of Polymer & Composites Original Research Open Access

A Hybrid Artificial Intelligence Approach for Defect Detection in Fiber-Reinforced Composite Steels

  1. Dibya Tripathi Department of Mechanical Engineering, Amity University, Lucknow, Campus
  2. Kheelraj Pandey Department of Mechanical Engineering, Amity University, Lucknow, Campus
  3. Akhilesh Kumar Chauhan Kamla Nehru Institute of Technology (KNIT), Sultanpur
  4. Ashwani Sharma Department of Mechanical Engineering, Amity University, Lucknow, Campus

Abstract

Defect detection in fiber-reinforced composite materials is important for maintaining product quality and ensuring structural reliability. However, manual inspection can be time-consuming and may depend on the experience of the inspector. This study presents a hybrid Convolutional Neural Network–Grey Wolf Optimization (CNN–GWO) approach for automated detection and classification of defects in fiber-reinforced composite materials. The proposed approach combines CNN-based feature learning with GWO-based hyperparameter optimization to improve the selection of the network configuration. The image dataset was divided into training, validation, and testing sets in the ratio of 70:15:15, with augmentation applied to the training data. GWO was used to minimize validation loss and identify an effective CNN configuration. The optimized model consisted of 128 filters in the first convolutional layer with a kernel size of 5, 192 filters in the second convolutional layer, and 256 dense units. The model achieved a reported training accuracy of 99.23%, validation accuracy of 99.82%, precision of 99.80%, recall of 99.51%, F1-score of 99.77%, and AUC of 0.999. The validation loss was approximately 0.02, compared with 0.1119 for the KerasTuner-based model. These results show that GWO can effectively support CNN hyperparameter selection and improve the reported classification performance. The proposed framework provides a promising basis for automated composite-defect inspection and its potential future application in industrial inspection systems.

Keywords

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