convolutional neural network

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• Published Open access Original Research 23 Oct 2026

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

  By Dibya Tripathi, Kheelraj Pandey, Akhilesh Kumar Chauhan, Ashwani Sharma

  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, …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 · pp. 377–403 Read article →