Real-Time Diagnosis of Crack Initiation in Aluminum 2024 Sharp Notch Samples Using Acoustic Emission Processing

Abstract

Abstract This study presents a real-time framework for diagnosing crack initiation in Aluminum 2024-T3 sharp notch samples by integrating acoustic emission (AE) processing with supervised machine learning. To overcome the technical limitations of traditional ex-situ analysis, a multi-threaded, concurrent processing architecture was developed in the C# programming language, enabling simultaneous data collection, feature extraction, and classification. A novel "initiation ratio" metric was implemented to identify the onset of cracking by quantifying the proportion of fracture-specific waveforms relative to other damage-related signals. Comparative analysis between contact-based piezoelectric (PZT) sensors and a non-contact laser vibrometer demonstrated that PZT sensors achieved 100% accuracy in detecting crack initiation under both monotonic and fatigue loading. While the laser vibrometer showed a 96.8% accuracy during monotonic tests, its performance in fatigue was limited by sensitivity to global movement and background noise. The concurrent framework reduced total processing time by up to 78%, providing a robust and efficient methodology for early-stage structural health monitoring in real-world applications.