FCGNet: A Deep Learning Methodology for Real Time Prediction of Fatigue Crack Growth Rate Using Acoustic Emission Signals
IEEE SENSORS JOURNAL（2025）
Key words
Fatigue,Steel,Stress,Welding,Real-time systems,Feature extraction,Monitoring,Marine vehicles,Metals,Load modeling,Acoustic emission (AE) sensor,convolutional neural network (CNN),fatigue crack growth rate (CGR),long short-term memory (LSTM),nondestructive evaluation,recurrent neural network
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