Acoustic Emission during Plastic Deformation of Pb–Sn
			Alloys

Salita, D. S.; Polyakov, V. V.

doi:10.1134/s1029959920060156

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…To identify the moment of disorder in the flow of AE parameters caused by an AE source occurrence of different nature, the calculation of the two-sample Kolmogorov-Smirnov criterion [19] was applied based on a comparison of two empirical distribution functions: ,…”

Section: Empirical Distribution Functionsmentioning

confidence: 99%

“…To determine the similarity degree of the empirical distribution functions of AE parameters, the probabilistic criterion Kα(λ) was evaluated, the values of which were selected from reference data [19]. When the empirical distribution functions of the AE parameters were similar, the criterion parameter Kα(λ) was close to zero.…”

Section: Empirical Distribution Functionsmentioning

confidence: 99%

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Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

Marchenkov¹,

Vasiliev²,

Chernov³

et al. 2023

Preprint

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This research is aimed to develop the method for damage degree evaluation in metals and alloys during their deformation and destruction based on acoustic emission (AE) technique. Experimental studies were carried out on tensile specimens of high-strength steel with simultaneous registration of AE signals in tested material. It was shown that the time-dependent AE primary parameters, such as the AE signal maximum amplitudes and AE activity, do not have a clear relationship with the material damage degree. This fact makes it difficult to use standard methods for assessing the damage degree of acoustic signal sources, including defects. It was proposed to use the Kolmogorov-Smirnov criterion to differ the processes of metal plastic deformation and crack propagation during the rupture. The possibility of estimating the material damage degree by high-energy AE hits share was shown. Besides, a particular expression was obtained for evaluating the damage degree for the material under study.

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Section: Empirical Distribution Functionsmentioning

confidence: 99%

Section: Empirical Distribution Functionsmentioning

confidence: 99%

Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

Marchenkov¹,

Vasiliev²,

Chernov³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…To decrease the intensity of adhesive wear of Al-based alloys, they are alloyed with soft metals (Sn or Pb), which do not dissolve in the aluminum matrix. Inclusions of these metals can serve as a solid lubricant for the friction surface [ 2 , 3 , 4 , 5 , 6 , 7 ]. With an increase in their content, the pressure of severe seizure of aluminum with steel increases, and the friction coefficient (µ) decreases to about 0.2 for the AO20-1 alloy [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Contribution of Tin to the Strain Hardening of Self-Lubricating Sintered Al-30Sn Alloy and Its Wear Resistance under Dry Friction

2023

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Aluminum alloys, which have been widely used in various manufacturing industries as an upper layer of bearing inserts, are alloyed with Sn to decrease the intensity of adhesive wear. A relationship between the mechanical properties, wear resistance, and structure of sintered Al-30Sn alloy containing a large amount of the soft phase was studied in this work. The above-mentioned characteristics were determined by testing the investigated material under compression and wear under dry friction in the pin-on-disk geometry at a sliding speed of 0.6 m/s and pressures of 1–5 MPa. The studied alloy was prepared by sintering of compacts consisting of a mixture of commercial powders in a vacuum furnace at a temperature of 600 °C for an hour. Then, the sintered Al-30Sn samples were subjected to processing by equal channel angular pressing (ECAP) with routes A and C. It has been established that the hardening value of the alloy subjected to ECAP virtually does not depend on the Sn content, but it depends on the number of passes and the processing route. The maximum increase in the strength of the alloy was found after the first and second passes. At the fixed Sn content, its effect on the wear resistance of the alloy does not depend on the strain hardening value of the aluminum matrix.

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“…Acoustic emission (AE) is a non-destructive measurement method, which has high sensitivity, and thus, it is widely used for different scientific and engineering purposes [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The AE signals are fingerprints of intermittent microscopic processes accompanied by sudden local changes leading to the emission of acoustic signals [ 4 , 11 , 12 , 13 , 14 , 15 , 16 ]. The above citations also illustrate that the evaluation of the measured signals is continuously developing, since the information provided by AE or by nonlinear ultrasonic methods [ 16 ] is rich in different microscopic aspects of the investigated crackling noise (avalanches of AE signals).…”

Section: Introductionmentioning

confidence: 99%

“…The method used in this article can be applied for the partition and classification of different local stress relaxation mechanisms producing AE signals, since they should have different waveforms and PSD functions, in general. Thus, in situ AE monitoring with adaptive sequential k-means (ASK) algorithm [ 6 ] helps to separate different local stress relaxation mechanisms during plastic deformations and other structural changes (e.g., during martensitic transformations) as it is illustrated in references [ 6 , 7 , 8 , 13 , 14 , 15 , 17 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Clustering Characterization of Acoustic Emission Signals Belonging to Twinning and Dislocation Slip during Plastic Deformation of Polycrystalline Sn

et al. 2022

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Results of acoustic emission (AE) measurements, carried out during plastic deformation of polycrystalline Sn samples, are analyzed by the adaptive sequential k-means method. The acoustic avalanches, originating from different sources, are separated on the basis of their spectral properties, that is, sorted into clusters, presented both on the so-called feature space (energy-median frequency plot) and on the power spectral density (PSD) curves. We found that one cluster in every measurement belongs to background vibrations, while the remaining ones are clearly attributed to twinning as well as dislocation slips at −30 °C and 25 °C, respectively. Interestingly, fingerprints of the well-known “ringing” of AE signals are present in different weights on the PSD curves. The energy and size distributions of the avalanches, corresponding to twinning and dislocation slips, show a bit different power-law exponents from those obtained earlier by fitting all AE signals without cluster separation. The maximum-likelihood estimation of the avalanche energy (ε) and size (τ) exponents provide ε=1.57±0.05 (at −30 °C) and ε=1.35±0.1 (at 25 °C), as well as τ=1.92±0.05 (at −30 °C) and τ= 1.55±0.1 (at 25 °C). The clustering analysis provides not only a manner to eliminate the background noise, but the characteristic avalanche shapes are also different for the two mechanisms, as it is visible on the PSD curves. Thus, we have illustrated that this clustering analysis is very useful in discriminating between different AE sources and can provide more realistic estimates, for example, for the characteristic exponents as compared to the classical hit-based approach where the exponents reflect an average value, containing hits from the low-frequency mechanical vibrations of the test machine, too.

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Acoustic Emission during Plastic Deformation of Pb–Sn Alloys

Cited by 6 publications

References 16 publications

Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

Contribution of Tin to the Strain Hardening of Self-Lubricating Sintered Al-30Sn Alloy and Its Wear Resistance under Dry Friction

Clustering Characterization of Acoustic Emission Signals Belonging to Twinning and Dislocation Slip during Plastic Deformation of Polycrystalline Sn

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