Acoustic Emission Behavior during Fatigue Crack of API5LX70 Gas Pipeline Steel

Yusof, M. F. M.; Nizwan, C. K. E.; Jamaludin, Nordin; Abdullah, Shahrum

doi:10.4028/www.scientific.net/amm.80-81.148

Cited by 6 publications

(4 citation statements)

References 8 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results also showed that moving RMS for the crack-growth-related signal was higher than that for the crack surfaces rubbing/contact related signal, which suggested that the crack opening/closing phenomenon can be differentiated from crack growth using AE moving RMS. Yusof et al [ 46 ] investigated AE characteristics during fatigue of an API 5L X70 gas pipeline steel and found that AE signals related to crack closure had higher count values than those related to crack growth. However, AE RMS from crack propagation was higher than that from crack closure.…”

Section: Resultsmentioning

confidence: 99%

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods

Shi,

Yao,

et al. 2024

Sensors

View full text Add to dashboard Cite

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

show abstract

Section: Resultsmentioning

confidence: 99%

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods

Shi,

Yao,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Fracture tests were carried out to detect AE signals associated with crack initiations [27,28,30] and crack growth in metals [29,[31][32][33]. .…”

Section: Ae Monitoring Of Fracturementioning

confidence: 99%

Acoustic Emission Monitoring of Fracture Tests

Chuluunbat¹,

Kostryzhev²

2018

Advanced Engineering Testing

View full text Add to dashboard Cite

Identification of defect(s) where crack initiation is possible and prediction of crack propa gation are of great importance in materials engineering. The current chapter analyzes the application of the acoustic emission (AE) technique for fracture monitoring in the line pipe steel during single-edge-notched tension (SENT) test and Charpy V-notch (CVN) impact test. It was found that the AE activity starts before the yield point, due to the stress concentration at the crack tip, and increases suddenly before the peak load is reached, due to the fracture initiation. Toward the end of the test, the AE hit density increases again, following intensive crack propagation. The AE analysis conducted in the present chapter showed a strong evidence of AE hit density increasing before the peak load is reached, the moment corresponding to fracture initiation.

show abstract

“…Chuluunbat at al [5] investigated the effect of loading conditions on fracture behaviour of aluminium alloy using AE monitoring during tensile testing. Yusof at al [6] studied AE activity of X70 pipeline steel subjected to fatigue. The crack initiation was found to increase the AE count value at positive peak stress.…”

Section: Introductionmentioning

confidence: 99%

Investigation of X80 Line Pipe Steel Fracture during Tensile Testing Using Acoustic Emission Monitoring

Chuluunbat¹,

Kostryzhev

Marenych

2019

KEM

View full text Add to dashboard Cite

The acoustic emission (AE) monitoring technique is widely used in mechanical and materials research for detection of plastic deformation, fracture initiation and crack growth. However, the quantitative dependences of the AE signal parameters on material fracture parametersare not completely understood. This paper presents recent research results on AE monitoring of the fracture behavior of X80 line pipe steel, a critically important material for the oil and gas transportation industry.Fracture of this steel was studied using tensile testing of small scale specimens coupled with AE monitoring and high speed video camera. The dependence of fracture behavior and AE parameters on loading conditions (strain rate and presence or absence of a notch) was investigated. The AE parameters were analyzed using the “Average Hit” features and “Wave Form and Power Spectrum” methodologies. The fracture surface was characterized using scanning electron microscopy and a dependence of the AE parameters on the average void size has been obtained.

show abstract

Acoustic Emission Behavior during Fatigue Crack of API5LX70 Gas Pipeline Steel

Cited by 6 publications

References 8 publications

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods

Acoustic Emission Monitoring of Fracture Tests

Investigation of X80 Line Pipe Steel Fracture during Tensile Testing Using Acoustic Emission Monitoring

Contact Info

Product

Resources

About