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This research presents a deep learning framework utilizing convolutional neural networks (CNNs) to estimate fatigue crack lengths in metallic plates based on acoustic emission (AE) signals. The study employs a clustering system and transfer learning models, achieving an accuracy of approximately 99% in categorizing fracture lengths, significantly outperforming a custom CNN. The findings highlight the effectiveness of CNNs in structural health monitoring and the importance of real-time monitoring for aging e
- Author
- vahiddtudrive
- Language
- EN