Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data

On-line monitoring and quantification of fatigue cracks are essential for ensuring the reliability of engineering structures. The acoustic emission (AE) technique is one of the structural health monitoring (SHM) techniques and is capable of detecting the growth of defects in real time. In this study, the identification and prediction of fatigue crack growth (FCG) of 316LN stainless steel under different stress ratios were investigated by the AE technique. The purpose of this study is to extract multiple AE parameters from AE waves and relate AE signals with FCG under different load ratios for qualitatively identifying the fatigue damage and quantitatively predicting the crack size. The results show that three damage stages associated with crack initiation, stable crack growth, and rapid crack growth are accurately identified by using multiple AE parameters during FCG under various load ratios. The quantitative relationships between Fatigue crack growth rates (FCGRs) and AE parameters are linear in log-log scale and independent of the stress ratio. Moreover, AE energy is found to be the most superior parameter for qualitatively identifying the fatigue damage and quantitatively relating FCGR to AE data. Finally, a combined qualitative and quantitative approach for FCG assessment based on AE monitoring is proposed. The criterion for diagnosing the critical damage state and estimating the fatigue crack size is also clarified. Results from this study will provide a strategy for effectively assessing FCG with AE data and guide complementary studies for damage identification with AE monitoring.

Automated summary

This study investigates the identification and prediction of fatigue crack growth (FCG) in 316LN stainless steel under different stress ratios using the acoustic emission (AE) technique. Multiple AE parameters are extracted from AE waves and used to qualitatively identify fatigue damage and quantitatively predict crack size. The results show that the AE technique accurately identifies the three damage stages associated with crack initiation, stable crack growth, and rapid crack growth. The quantitative relationships between fatigue crack growth rates (FCGRs) and AE parameters are linear in log-log scale and independent of the stress ratio. AE energy is found to be the most superior parameter for qualitatively identifying fatigue damage and relating FCGR to AE data.