The Use of the Acoustic Emission Method to Identify Crack Growth in 40CrMo Steel

Abstract

The article presents the application of the acoustic emission (AE) technique for detecting crack initiation and examining the crack growth process in steel used in engineering structures. The tests were carried out on 40CrMo steel specimens with a single edge notch in bending (SENB). In the tests crack opening displacement, force parameter, and potential drop signal were measured. The fracture mechanism under loading was classified as brittle. Accurate AE investigations of the cracking process and SEM observations of the fracture surfaces helped to determine that the cracking process is a more complex phenomenon than the commonly understood brittle fracture. The AE signals showed that the frequency range in the initial stage of crack development and in the further crack growth stages vary. Based on the analysis of parameters and frequencies of AE signals, it was found that the process of apparently brittle fracture begins and ends according to the mechanisms characteristic of ductile crack growth. The work focuses on the comparison of selected parameters of AE signals recorded in the pre-initiation phase and during the growth of brittle fracture cracking.

Application of the Acoustic Emission Method and Kolmogorov-Sinai Metric Entropy in Determining the Yield Point in Aluminium Alloy

“…Like the majority of materials, metals emit elastic (acoustic) waves when under a load. These waves are emitted due to a large number of auxiliary phenomena, which is why they can be tested applying the Acoustic Emission (AE) method [1]. These phenomena include plastic deformations, i.e., dislocation slide or crystal twinning; the formation and growth of cracks and the destructive process; phase transitions [2,3].…”

Using AE Signals to Investigate the Fracture Process in an Al–Ti Laminate

“…Fracture surfaces of the samples were observed with a scanning electron microscope (SEM), and the recorded AE signals were analysed using a non-hierarchical method for AE signal clustering based on k -means [ 30 , 31 ] and analysis using waveform time domain, waveform time domain (autocorrelation), fast Fourier transform (FFT Real) and waveform continuous wavelet based on the Morlet wavelet [ 32 ] in order to explain the peculiarities of the P – COD curves at different temperatures and identify the mechanisms generating the AE signals.…”

“…That is why using AE in the study made it possible to identify certain peculiarities of the fracture process in the tested layered composite. Also, the AE signals were clustered depending on the processes that generated them using the iterative k -means method, which clusters AE parameters in a Euclidean space [ 30 , 31 ], and analysed them using waveform time domain, waveform time domain (autocorrelation), fast Fourier transform (FFT Real) and waveform continuous wavelet based on the Morlet wavelet [ 32 ].…”

Investigation of the Fracture Process of Explosively Welded AA2519–AA1050–Ti6Al4V Layered Material

“…A comparison of AE signals with the shape of P-COD loading diagrams shows the differences between the cracking process for specimens made of Al-Ti laminate at different test temperatures [49]. At ambient temperature (293 K), the growth of a delamination interlayer fracture was initiated and continued along with the development of the main subcritical fracture in the base layers.…”