Acoustic emission analysis during fatigue crack propagation by Bayesian statistical modeling

Rail networks are gradually getting busier with rolling stock traveling at higher speeds and carrying heavier axle loads than ever before. In order to consider the further application of acoustic emission (AE) for railway damage monitoring, the fatigue and AE characteristics during the fatigue crack growth (FCG) process in a rail steel were investigated in this study. For better recognition and prediction fatigue damage of rail steel, the relationship between the micromechanism and the characteristics of AE signals were studied. The FCG experiments were carried out, and the AE signals emitted from FCG process were studied by AE parameters and Fast Fourier Transformed (FFT) spectra analysis. The AE results were also compared with the fracture surfaces. The results show that the AE technique is capable of acquiring and identifying brittle fracture events during FCG of rail steel.

In engineering structures that are subject to cyclic loading, monitoring and assessing fatigue crack growth (FCG) plays a crucial role in ensuring reliability. In this study, the acoustic emission (AE) technique was used to monitor the FCG behavior of 2.25Cr1Mo0.25V steel in real-time. Specifically, an AE multi-parameter analysis was conducted to qualitatively assess the crack growth condition and quantitatively correlate the crack growth rate with AE. Various AE parameters were extracted from AE signals, and the performances of different AE parameters were analyzed and discussed. The results demonstrated that four stages of FCG, which correspond to macrocrack initiation, stable crack growth with low crack growth rate, stable crack growth with high crack growth rate, and unstable crack growth, are distinctly identified by several AE time domain parameters. The sudden and continuous occurrence of many AE signals with high count (>100) and high energy (>40 mV·ms) can provide early and effective warning signs for accelerated crack growth before final failure occurs. Moreover, linear correlations between crack growth rate and different AE parameters are established for quantifying crack growth. Based on the AE multi-parameter analysis, it was found that the count, energy, and kurtosis are superior AE parameters for both qualitatively and quantitatively characterizing the FCG in 2.25Cr1Mo0.25V steel. Results from this research provide an AE strategy based on multi-parameter analysis for effective monitoring and assessment of FCG in engineering materials.

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

Characterization of piezoelectric wafer active sensor for acoustic emission sensing

Study of fatigue crack growth in RAFM steel using acoustic emission technique

Predicting fatigue crack growth rates and thresholds at low temperatures

Fatigue behavior of notched steel beams reinforced with bonded CFRP plates: Determination of prestressing level for crack arrest

Influence of micro structure on acoustic emission behavior during stage 2 fatigue crack growth in solution annealed, thermally aged and weld specimens of AISI type 316 stainless steel

The crack growth characteristics of a 4-ply, unidirectional, titanium matrix composite, SCS-6/Ti-6Al-2Sn-4Zr-2Mo, subjected to thermomechanical fatigue were investigated. A linear summation model was developed to predict the isothermal and thermomechanical fatigue (TMF) crack growth rates of the composite. The linear summation approach assumes the total fatigue crack growth rate is a combination of a cycle-dependent and a time-dependent component. To assist the modeling effort, a series of isothermal, in-phase, and out-of-phase crack growth tests were conducted. The test temperatures ranged from 150‡C to 538‡C and the fastest thermal frequency was 0.0083 Hz. With the exception of the 150‡C isothermal test, the model was able to correlate all the baseline fatigue crack growth test data between δK of 50 to 90 MPa √m. In addition, the model was able to predict the fatigue crack growth rate of a proof test which involved a continual change in temperature range and load range to produce a constant crack growth rate. The proof test began under isothermal conditions at the maximum temperature and ended under in-phase TMF conditions.

In the use of metals, due to industrial advances and the application of more dynamic loads, it is necessary to pay more attention to the fatigue issue. Structural health monitoring (SHM) system is a method of evaluating and monitoring structural health. It has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. Non-destructive inspection methods are used to condition and health monitoring structures at the time of production and even during the service life of parts. Among non-destructive methods, the acoustic emission method has become a standard and reliable method in recent years. The stimulated internal energy of the structure is received in this acoustic emission technique as health monitoring features. A dominating attribute of the acoustic emission technique is its application ability in its loading condition. Therefore, it provides instant damage information within a short period of time. Thus, acoustic emission monitoring tests are often performed in the operating conditions of the structure. Acoustic emissions can inform us of the changes that occur before the final failure and prevent much financial and human damage. In this project, the characteristics of acoustic emission in the fatigue crack growth of aluminum alloy 2025 for online structural monitoring have been investigated and determined. Acoustic emission tests have been performed in two parts: bending fatigue test with the aim of initiation of fatigue cracks in aluminum alloy 2025 specimens and following tensile tests with the aim of growth of fatigue cracks. The acoustic emission signals and parameters sent by the acoustic emission sensor during both tests were received and recorded by the acoustic emission software. According to the received acoustic emission information, various diagrams are plotted. Analyzing the results from online acoustic emission monitoring showed the acoustic emission method can be considered a suitable and reliable technique for detecting crack initiation and crack growth in aluminum alloy 2025.

A unified description of mechanical and actuation fatigue crack growth in shape memory alloys

In this study, the fatigue crack growth (FCG) characteristics of the short surface crack was studied at various stress ratios, R, using the fine grained cemented carbide. The FCG law for the material was studied using two parameters, the maximum stress intensity factor Kmax and the stress intensity factor range ΔK. It was found that the relationship between the rate of FCG, da/dN and ΔK depends on the R value. In the da/dN vs. Kmax relation, at the region of high crack growth rate, the FCG rate was nearly constant by the value of Kmax without depending on R. However, in the low FCG rate region, the FCG rate tended to accelerate as R became lower. Therefore, it was difficult to specify da/dN as a single value at different R ratios using either ΔK or Kmax from the low speed region to the high-speed region. For that reason, the FCG behaviour of the material was studied based on the crack growth law, da/dN = AKmaxmΔKn. The values of m and n were obtained as 6.6 and 1.4, respectively. Therefore, it was found that the FCG rate of the cemented carbide was strongly dependent on Kmax rather than ΔK, like the FCG characteristics of ceramics.

To the instability of acoustic emission (AE) signal of rock fracture, the method about feature extraction and comprehensive recognition of those was came up with combining AE parameters, EMD and BP neural network. Through the acoustic emission experiment of different brittle rock under uniaxial compression, stress-strain curve and AE data were obtained; time-frequency characteristics of AE signal of rock samples were compared. Feature vectors, like AE parameters, and EMD energy entropy, was synthesized to BP neural network to distinguish different AE signal. The results show that evolution characteristic with stress or time of AE parameters of different rock which was under uniaxial compression exist similarities and differences. EMD and Welch spectrum can reflect the difference among spectrum, energy distribution of AE signal of different rock very well. With various characteristics of different rock acoustic emission, the neural network has good recognition effect.

In the use of metals, due to industrial advances and the application of more dynamic loads, it is necessary to pay more attention to the fatigue issue. Non-destructive inspection methods are used to condition and health monitoring of structures at the time of production and even during the service life of parts. Among non-destructive methods, the acoustic emission method has become a standard and reliable method in recent years. In this project, the characteristics of acoustic emission in the fatigue crack growth of aluminum alloy 2025 for online structural monitoring have been investigated and determined. Acoustic emission tests have been performed in two parts: bending fatigue test with the aim of initiation of fatigue cracks in aluminum alloy 2025 specimens and following tensile tests with the aim of growth of fatigue cracks. The acoustic emission signals and parameters sent by the acoustic emission sensor during both tests were received and recorded by the acoustic emission software. According to the received acoustic emission information, various diagrams are plotted. Analyzing the results from online acoustic emission monitoring showed, the acoustic emission method can be considered as a suitable and reliable technique for detecting crack initiation and crack growth in aluminum alloy 2025.

Modeling of I + II mixed mode crack initiation and growth from the notch