Minimization and Quantification of Error Associated with the K Gradient and the Interval of Crack Length Measurement in Fatigue Crack Growth Testing

1993 ◽  
Vol 21 (2) ◽  
pp. 107 ◽  
Author(s):  
DR Petersen ◽  
M Oore
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Length ◽  
Crack Growth ◽  
Length Measurement ◽  
Fatigue Crack Growth Testing ◽  
Crack Length Measurement

scholarly journals Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4221
Author(s):  
Roshan Joseph ◽  
Hanfei Mei ◽  
Asaad Migot ◽  
Victor Giurgiutiu
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Length ◽  
Crack Growth ◽  
Health Monitoring ◽  
Acoustic Waves ◽  
High Frequency ◽  
Fem Analysis ◽  
Signal Frequency ◽  
Propagation Pattern

Acoustic waves are widely used in structural health monitoring (SHM) for detecting fatigue cracking. The strain energy released when a fatigue crack advances has the effect of exciting acoustic waves, which travel through the structures and are picked up by the sensors. Piezoelectric wafer active sensors (PWAS) can effectively sense acoustic waves due to fatigue-crack growth. Conventional acoustic-wave passive SHM, which relies on counting the number of acoustic events, cannot precisely estimate the crack length. In the present research, a novel method for estimating the crack length was proposed based on the high-frequency resonances excited in the crack by the energy released when a crack advances. In this method, a PWAS sensor was used to sense the acoustic wave signal and predict the length of the crack that generated the acoustic event. First, FEM analysis was undertaken of acoustic waves generated due to a fatigue-crack growth event on an aluminum-2024 plate. The FEM analysis was used to predict the wave propagation pattern and the acoustic signal received by the PWAS mounted at a distance of 25 mm from the crack. The analysis was carried out for crack lengths of 4 and 8 mm. The presence of the crack produced scattering of the waves generated at the crack tip; this phenomenon was observable in the wave propagation pattern and in the acoustic signals recorded at the PWAS. A study of the signal frequency spectrum revealed peaks and valleys in the spectrum that changed in frequency and amplitude as the crack length was changed from 4 to 8 mm. The number of peaks and valleys was observed to increase as the crack length increased. We suggest this peak–valley pattern in the signal frequency spectrum can be used to determine the crack length from the acoustic signal alone. An experimental investigation was performed to record the acoustic signals in crack lengths of 4 and 8 mm, and the results were found to match well with the FEM predictions.

scholarly journals Fatigue crack growth testing using varying R-ratios

2010 ◽  
Vol 2 (1) ◽  
pp. 155-161 ◽  
Author(s):  
Magnus Hörnqvist ◽  
Thomas Hansson ◽  
Olle Clevfors
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Fatigue Crack Growth Testing

Fatigue Crack Length Measurement Method with an Ion Sputtered Film

2008 ◽  
pp. 437-438 ◽  
Author(s):  
Gang Deng ◽  
Koutarou Nasu ◽  
Tilahun Daniel Redda ◽  
Tsutomu Nakanishi
Keyword(s):  
Fatigue Crack ◽  
Crack Length ◽  
Measurement Method ◽  
Length Measurement ◽  
Crack Length Measurement

A research on fatigue crack growth monitoring based on multi-sensor and data fusion

2019 ◽  
pp. 147592171986572
Author(s):  
Chang Qi ◽  
Yang Weixi ◽  
Liu Jun ◽  
Gao Heming ◽  
Meng Yao
Keyword(s):  
Finite Element ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Data Fusion ◽  
Crack Propagation ◽  
Finite Element Model ◽  
Crack Length ◽  
Crack Growth ◽  
Lamb Wave ◽  
Element Model

Fatigue crack propagation is one of the main problems in structural health monitoring. For the safety and operability of the metal structure, it is necessary to monitor the fatigue crack growth process of the structure in real time. In order to more accurately monitor the expansion of fatigue cracks, two kinds of sensors are used in this article: strain gauges and piezoelectric transducers. A model-based inverse finite element model algorithm is proposed to perform pattern recognition of fatigue crack length, and the fatigue crack monitoring experiment is carried out to verify the algorithm. The strain spectra of the specimen under cyclic load in the simulation and experimental crack propagation are obtained, respectively. The active lamb wave technique is also used to monitor the crack propagation. The relationship between the crack length and the lamb wave characteristic parameter is established. In order to improve the recognition accuracy of the crack propagation mode, the random forest and inverse finite element model algorithms are used to identify the crack length, and the Dempster–Shafer evidence theory is used as data fusion to integrate the conclusion of the two algorithms to make a more accountable and correct judge of the crack length. An experiment has been conducted to demonstrate the effectiveness of the method.

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