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Our publications in 2024

List of publications in 2024, in which our employees participated:

Barat V., Marchenkov A., Bardakov V., Arzumanyan D., Ushanov S., Karpova M., Lepsheev E., Elizarov S. Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method. Applied Sciences (Switzerland). 2024. Vol. 14, No. 22. P. 10546. DOI: 10.3390/app142210546. https://www.mdpi.com/2076-3417/14/22/10546 (full text). eLibrary ID: 79148054

Abstract The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases


Barat V.A., Ushanov S.V., Lepsheev E.A., Sviridov G.V., Lavrik N.V. Identification of diffusion interlayers of dissimilar welds under static tension by acoustic emission method. Edelweiss Applied Science and Technology. 2024. Vol. 8. No. 6. P. 1554-1565. DOI: 10.55214/25768484.v8i6.2273 (full text). https://learning-gate.com/index.php/2576-8484/article/view/2273 (full text). eLibrary ID: 74663953

Abstract The paper studies the possibility of carbide and decarburized ferrite interlayers detection formed during the production and operation of dissimilar welded joints of austenitic to pearlitic steels. Diffusion interlayers may be interpreted as welded joint microstructure defects, since their formation can cause premature failure of the product under high-temperature operation conditions. In addition, with a certain chemical and structural-phase composition of the welded metals, brittle interlayers formation and development of cracks in the weld zone may be occurred. To identify diffusion interlayers, the acoustic emission (AE) method is used in this research. Specimens cut from welded joints were tested by tension until rupture with simultaneous recording of AE signals. Based on the research results, particular AE data signatures corresponding to the specimens with diffusion interlayers were identified. The main feature indicating the presence of diffusion interlayers is an increase in the amplitudes of AE impulses and AE activity at a stress value of 300 MPa, corresponding to the ultimate strength of the ferrite phase