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2 articles for “KerasTuner”
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Enhanced Deep Learning Techniques for Defect Detection in Steel-Fiber-Reinforced Composites: A Comparative Study of KerasTuner, CNN–PSO, and CNN–GWO
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
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A Hybrid Artificial Intelligence Approach for Defect Detection in Fiber-Reinforced Composite Steels
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 …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 · pp. 285–310 Read article