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Journal of Polymer & Composites Original Research Open Access

Enhanced Deep Learning Techniques for Defect Detection in Steel-Fiber-Reinforced Composites: A Comparative Study of KerasTuner, CNN–PSO, and CNN–GWO

  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 Department of Mechanical Engineering, KNIT, Sultanpur
  4. Ashwani Sharma Department of Mechanical Engineering, Amity University, Lucknow Campus

Abstract

The identification of microstructural defects in steel-fibre-reinforced composites is useful for the material integrity, mechanical reliability and longevity. Classic material inspection techniques face difficulties in recognizing the complex and visually similar defects because of heterogeneous material morphology, changing imaging conditions, and limits of manual feature extraction. To tackle those problems, this study presents a comparative deep-learning framework that integrates Convolutional Neural Networks (CNNs) with three hyperparameter optimization techniques namely KerasTuner, Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The framework focuses on automated recognition of composite defects through image pre-processing, hierarchical extraction of relevant features, and optimization-supported configuration. Three methods are evaluated in a common comparative framework to study learning behaviour, convergence characteristic, and classification performance. The experiment assesses both training and validation accuracy as well as their associated losses over 25 epochs. The graphical comparison shows that CNN–GWO achieved the highest training and validation accuracy followed by CNN–PSO and CNN–KerasTuner. Values derived from the given performance curves suggest that CNN–GWO achieves a validation accuracy of 99.5%, followed by CNN–PSO at 97.0% and CNN–KerasTuner at 94.5%. Moreover, CNN–GWO achieves high initial loss reduction and demonstrates good convergence properties, indicating its potential effectiveness in choosing discriminative CNN configurations for composite defect recognition. While these results demonstrate the advantages of CNN–GWO in the reported experimental setting, the statistical robustness and generalization of the results need to be evaluated with independent test sets and repeated experimental trials. The swarm-intelligence-assisted deep learning framework has the potential applications in automated material inspection. Moreover, it offers a comparative background for developing reliable defect classification systems for steel-fibre-reinforced composites. Steel fiber reinforced composites (SFRCs) are widely used in the construction industry to improve strength and durability. However, the detection of defect in SFRCs is performed manually which is time consuming and does not provide accurate results. There are various automated deep learning approaches which can detect the defect in SFRCs quickly and provide reliable result. This study aims to carry out the hyperparameter optimization of convolutional neural network (CNN) using KerasTuner and with the help of advanced metaheuristic algorithms such as particle swarm optimization (PSO) and grey wolf optimization (GWO). The authors will develop a model which will provide better defect detection accuracy than the existing model and will make the process more automated. Results will be presented in the latter sections to validate the effectiveness of the model.

Keywords

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