Abstract
Using the advanced acoustic emission (AE) technique, we address the problem of early identification of crack initiation and growth in ductile structural steels under cyclic loading. The notched 9MnSi5 steel specimens with weld joints were fatigue tested at room and lower temperatures with concurrent AE measurements. Detection of AE in ductile materials where fatigue crack initiation and propagation is mediated by local dislocation behavior ahead of the notch or crack tip is challenging because of an extremely low amplitude of the AE signal. With account of this issue, two new practically oriented criteria for recognition of different stages of fatigue are proposed on the basis of AE data: (1) a power spectrum-based criterion and (2) a pattern recognition-based criterion utilizing modern clustering algorithms. The applicability of both criteria is verified using obtained AE data. A good correspondence between AE outcomes and experimental observations of the fatigue behavior was obtained and is discussed.
Highlights
The critical state of a cyclically loaded material is often associated with accumulated cracks. integrity remains, the catastrophic growth of a main crack is expected with further loading.This state characterizes the material that has accumulated a large number of microcracks tending to merge with high probability, forming a main crack
According to fracture mechanics, such a crack is unstable and can grow spontaneously to final failure. This scenario of gradual damage accumulation followed by a rapid main crack propagation to fracture is common for many structural steels and industrial facilities such as pressure vessels, pipelines, and tanks operating under cyclic load, vibration, or both
The most widely used application of acoustic emission (AE) non-destructive testing nowadays might be to find the active defects in the pressure vessel equipment [4,5,6,8], which is biaxially loaded by cyclically changing internal operating pressure
Summary
The critical state of a cyclically loaded material is often associated with accumulated cracks. The AE peak amplitude is often comparable or lower than the background noise level This substantially complicates the use of standard criteria, which have been developed for the threshold AE hit detection schemes [4]. The latter apparently do not work when the AE amplitude is low In such cases the conventional AE approaches towards AE source discrimination during industrial equipment testing and monitoring fail to detect early stages of fracture in ductile steels. This fact has prompted many researchers to development of advanced data mining methodologies such as unsupervised pattern recognition and signal clustering for the classification of AE data. The present work is aimed at extending efforts in this direction and developing robust AE criteria suitable for the effective identification of the transition of ductile structural steels to the critical condition corresponding to approaching failure under fatigue loading
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