Propagation of acoustic emission signals in continuous conjugated media under real-time loading was explored. The results of explored plastic deformation polymer coatings on a metal base using the acoustic emission method with synchronization of deformations and the moments of occurrence of acoustic emission signals are presented. Using the principal component method, the acoustic emission spectra, which make it possible to trace the evolution of deformation transformation processes, were analyzed. Presented the results of theoretical and experimental studies on the separate propagation of acoustic emission vibrations in a polymer coating, a metal base, and their joint combination in the form of multilayer structures. Boundary problems of propagation of acoustic emission signals in the conjugation of continuous media are considered from the standpoint of an elastic continuum and wave representations. The main variables are the force that initiates the appearance of acoustic emission signals and the displacement that determines the propagation of elastic waves. Based on the local rearrangement of the internal structure of conjugated media under conditions of development of deformation processes in the material, the verification of the main theoretical models of energy spectrum acoustic signals in continuous media at the micro-, meso-, and macro-levels was carried out. In this work, we present experimental data on a set of basic acoustic emission characteristics for four-point bending. It is shown that the principal components method reduces the dimension of data while maintaining the least amount of new information. Using the method of principal components to determine the stages of plastic deformation of polymer coatings on a metal base using the acoustic emission method. With the digitalization of acoustic emission signals and noise filtering, new possibilities for isolating a weak signal at the noise level appear even when its amplitude is significantly lower than the noise level. The study results can be used to predict the degree of destruction of two-layer materials under loading.

Department of Energetics Electrical Engineering and Physics Kherson National Technical University 73008 Kherson Ukraine

Department of Material Science Technical University of Liberec 461 17 Liberec Czech Republic

Department of Transport Technology and Mechanical Engineering Kherson Marine Academy 73000 Kherson Ukraine

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Louda P., Sharko A., Stepanchikov D. An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses. Materials. 2021;14:2090. doi: 10.3390/ma14092090. PubMed DOI PMC

Louda P., Marasanov V., Sharko A., Stepanchikov D., Sharko A. The Theory of Similarity and Analysis of Dimensions for Determining the State of Operation of Structures under Difficult Loading Conditions. Materials. 2022;15:1191. doi: 10.3390/ma15031191. PubMed DOI PMC

Cho H., Shoji N., Ito H. Acoustic emission generation behavior in A7075–T651 and A6061–T6 aluminum alloys with and without cathodic hydrogen charging under cyclic loading. J. Nondestr. Eval. 2018;37:83. doi: 10.1007/s10921-018-0536-7. DOI

Lavanya S., Mahadevan S., Mukhopadhyay C.K., Kumar S.A. Acoustic Emission during Press-Brake Bending of SS 304L Sheets and its Correlation with Residual Stress Distribution after Bending. J. Mater. Eng. Perform. 2022;31:1550–1561. doi: 10.1007/s11665-021-06250-w. DOI

Nedoseka A.Y., Nedoseka S.A., Markasheva L.I., Kushnareva U.S. On the recognition of changes in the structure of materials during destruction according to acoustic emission data. Tech. Diagn. Non-Destr. Test. 2016;4:9–13.

Abdulaziz A.H., Elsabbagh A., Hedaya M., McCrory J., Holford K., Abdulaziz A.H., McCrory J., Holford K.M., Elsabbagh A., Hedaya M. Experimental three-point bending test of glass fibre aluminium honeycomb sandwich panel with acoustic emission damage assessment. Insight Non-Destr. Test. Cond. Monit. 2021;63:727–733. doi: 10.1784/insi.2021.63.12.727. DOI

Sharko M., Gusarina N., Petrushenko N. Advances in Intelligent Systems and Computing. Springer; Berlin/Heidelberg, Germany: 2019. Information-entropy model of making management decisions in the economic development of the enterprises; pp. 304–314. Lecture Notes in Computational Intelligence and Decision Making. DOI

Chai M., Zhang Z., Duan Q. A new qualitative acoustic emission parameter based on Shannon’s entropy for damage monitoring. Mech. Syst. Signal Process. 2018;100:617–629. doi: 10.1016/j.ymssp.2017.08.007. DOI

Sharko M., Gonchar O., Tkach M., Polishchuk A., Vasylenko N., Mosin M., Petrushenko N. Intellectual Information Technologies of the Resources Management in Conditions of Unstable External Environment. Book Chapter. Lect. Notes Data Eng. Commun. Technol. 2021;1158:519–533. doi: 10.1007/978-3-030-82014-5. DOI

Marasanov V., Rudakova H., Stepanchikov D., Sharko O., Sharko A., Kiryushatova T. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies. Volume 77. Springer; Cham, Switzerland: 2021. Analysis of Digital Processing of the Acoustic Emission Diagnostics Informative Parameters Under Deformation Impact Conditions; pp. 230–251. DOI

Marasanov V., Sharko A., Stepanchikov D. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing. Volume 1020. Springer; Cham, Switzerland: 2019. Model of the Operator Dynamic Process of Acoustic Emission Occurrence While of Materials Deforming; pp. 48–64. DOI

Buketov A.V., Brailo M.V., Yakushchenko S.V., Sapronov O.O., Smetankin S.O. The formulation of epoxy-polyester matrix with improved physical and mechanical properties for restoration of means of sea and river transport. J. Mar. Eng. Technol. 2020;19:109–114. doi: 10.1080/20464177.2018.1530171. DOI

Muir C., Swaminathan B., Almansour A.S., Sevener K., Smith C., Presby M., Kiser J.D., Pollock T.M., Daly S. Damage mechanism identification in composites via machine learning and acoustic emission. NPJ Comput. Mater. 2021;7:95. doi: 10.1038/s41524-021-00565-x. DOI

Huang J., Zhang Z., Han C., Yang G. Identification of deformation stage and crack initiation in TC11 alloys using acoustic emission. Appl. Sci. 2020;10:3674. doi: 10.3390/app10113674. DOI

Tian Y., Yu R., Zhang Y., Zhao X. Application of Acoustic Emission Characteristics in Damage Identification and Quantitative Evaluation of Limeston. Gongcheng Kexue Yu Jishu/Adv. Eng. Sci. 2020;52:115–122.

Hase A. Early detection and identification of fatigue damage in thrust ball bearings by an acoustic emission technique. Lubricants. 2020;8:37. doi: 10.3390/lubricants8030037. DOI

Zou S., Yan F., Yang G., Sun W. The identification of the deformation stage of a metal specimen based on acoustic emission data analysis. Sensors. 2017;17:789. doi: 10.3390/s17040789. PubMed DOI PMC

Marasanov V., Stepanchikov D., Sharko A., Sharko O. Technology for determining the residual life of metal structures under conditions of combined loading according to acoustic emission measurements. Commun. Comput. Inf. Sci. 2020;1158:202–217.

Bryansky A.A., Bashkov O.V., Bashkov I.O., Solovev D.B. PCM Bearing Capacity Prediction Criteria Development Based on Registered AE Parameters. IOP Conf. Ser. Earth Environ. Sci. 2020;459:062105. doi: 10.1088/1755-1315/459/6/062105. DOI

Sapronov O.O., Buketov A.V., Maruschak P.O., Panin S.V., Brailo M.V., Yakushchenko S.V., Sapronova A.V., Leshchenko O.V., Menou A. Research of crack initiation and propagation under loading for providing impact resilience of protective coating. Funct. Mater. 2019;26:114–120. doi: 10.15407/fm26.01.114. DOI

Dmitriev A.A., Polyakov V.V., Lependin A.A. Investigation of plastic deformation of an aluminum alloy using wavelet transforms of acoustic emission signals. Lett. Mater. 2014;8:33–36. doi: 10.22226/2410-3535-2018-1-33-36. DOI

Mieza J.I., Oliveto M.E., López Pumarega M.I., Armeite M., Ruzzante J.E., Piotrkowski R. Identification of AE bursts by classification of physical and statistical parameters. AIP Conf. Proc. 2005;760:1174–1181.

Dmitriev A.A., Polyakov V.V., Ruder D.D. Application of the principal component method to the study of acoustic emission signals in aluminum alloys. News Altai State Univ. Phys. 2018;1:19–23.

Salita D.S., Polyakov V.V. Application of main component methods for stading acoustic emission at plastic deformation of leads alloys. News Altai State Univ. Phys. 2018;4:26–30.

Karimi N.Z., Minak G., Kianfar P. Analysis of damage mechanisms in drilling of composite materials by acoustic emission. Compos. Struct. 2015;131:107–114. doi: 10.1016/j.compstruct.2015.04.025. DOI

Lu D., Yu W. Correlation analysis between acoustic emission signal parameters and fracture stress of wool fiber. Text. Res. J. 2019;89:4568–4580. doi: 10.1177/0040517519838057. DOI

Al-Jumaili S.K., Eaton M.J., Holford K.M., McCrory J.P., Pullin R. Damage characterisation in composite materials under buckling test using Acoustic Emission waveform clustering technique; Proceedings of the 2014 NDT 2014—53rd Annual Conference of the British Institute of Non-Destructive Testing; Manchester, UK. 9–11 September 2014.

Zhou W., Zhao W.-Z., Zhang Y.-N., Ding Z.-J. Cluster analysis of acoustic emission signals and deformation measurement for delaminated glass fiber epoxy composites. Compos. Struct. 2018;195:349–358. doi: 10.1016/j.compstruct.2018.04.081. DOI

Roundi W., El Mahi A., El Gharad A., Rebiere J.-L. Acoustic emission monitoring of damage progression in Glass/Epoxy composites during static and fatigue tensile tests. Appl. Acoust. 2018;132:124–134. doi: 10.1016/j.apacoust.2017.11.017. DOI

Marasanov V. Operator of the Dynamic Process of the Appearance of Acoustic Emission Signals During Deforming the Structure of Materials. In: Marasanov V.V., Stepanchikov D.M., Sharko A.V., Sharko A.A., editors. IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO) (Kyiv, 22–24 April 2020) Igor Sikorsky Kyiv Polytechnic Institute; Kyiv, Ukraine: 2020. pp. 646–650. DOI

Zhao G., Zhang L., Tang C., Hao W., Luo Y. Clustering of AE signals collected during torsional tests of 3D braiding composite shafts using PCA and FCM. Compos. Part B Eng. 2019;161:547–554. doi: 10.1016/j.compositesb.2018.12.145. DOI

Zhang Y., Zhou B., Yu F., Chen C. Cluster analysis of acoustic emission signals and infrared thermography for defect evolution analysis of glass/epoxy composites. Infrared Phys. Technol. 2021;112:103581. doi: 10.1016/j.infrared.2020.103581. DOI

Behnia A., Chai H.K., GhasemiGol M., Sepehrinezhad A., Mousa A.A. Advanced damage detection technique by integration of unsupervised clustering into acoustic emission. Eng. Fract. Mech. 2019;210:212–227. doi: 10.1016/j.engfracmech.2018.07.005. DOI

Li D., Tan M., Zhang S., Ou J. Stress corrosion damage evolution analysis and mechanism identification for prestressed steel strands using acoustic emission technique. Struct. Control. Health Monit. 2018;25:e2189. doi: 10.1002/stc.2189. DOI

Marasanov V., Stepanchikov D., Sharko A., Sharko A. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. Advances in Intelligent Systems and Computing. Volume 1246. Springer; Cham, Switzerland: 2021. Technique of System Operator Determination Based on Acoustic Emission Method. DOI

Diagnostic of Machine of Using Performance Parameter. ISO; Genève, Switzerland: 2002.

Standard Guide for Acoustic Emission System Performance Verification. ASTM; West Conshohocken, PA, USA: 2004.

Marasanov V., Sharko A. Energy spectrum of acoustic emission signals of nanoscale objects. J. Nano Electron. Phys. 2017;9:0212(1)–0212(4).

Marasanov V., Sharko A. Energy Spectrum of Acoustic Emission Signals in Complex Media. J. Nano Electron. Phys. 2017;9:04024-1–04024-5. doi: 10.21272/jnep.9(4).04024. DOI

Kunin I.A. Theory of elastic media with microstructure. Nonlocal theory of elasticity. M Nauka. 1982:424.

Marasanov V.V., Sharko A.V., Sharko A.A. Boundary problems of determining the energy spectrum of acoustic emission signals in conjugated continuous media. Cybern. Syst. Anal. 2019;55:170–179. doi: 10.1007/s10559-019-00195-8. DOI

Marasanov V., Sharko O., Sharko A. Energy Spectrum of Acoustic Emission Signals in Coupled Continuous Media. J. Nano Electron. Phys. 2019;11:03028-1–03028-7. doi: 10.21272/jnep.11(3).03028. DOI

Barsukov A.I., Glazkova M.Y., Ryazhskikh V.I., Sumera S.S. Commutativity of spectral divisors of quadratic pencils—Bulletin of the South Ural State University Series. Math. Mech. Phys. 2019;11:5–11.

Makarov P.V., Solonenko O.P., Bondar M.P., Romanova V.A., Cherepanov O.I., Balakhonov R.R., Grishkov V.N., Lyudkov A.I., Evtushenko E.P. Modeling of deformation processes at the mesolevel in materials with different types of gradient coatings. Phys. Mesomech. 2003;6:47–61.

Makarov P.V. The approach of physical mesomechanics to modeling the processes of deformation and fracture. Phys. Mesomech. 1998;1:61–81.

Panin S.V. Regularities of plastic deformation and destruction at the mesolevel of materials with coatings and surface hardening. Phys. Mesomech. 2004;7:109–113.

Ivanova V.S., Oksogonov A.A. On the connection between the stages of plastic deformation processes and the fractal structure corresponding to a change in the scale level of deformation. Phys. Mesomech. 2008;9:17–27.

Li J.B., Saidi L., Harrath S., Bechhoefer E., Benbouzid M. Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Appl. Acoust. 2018;132:167–181.

Chai M., Hou X., Zhang Z., Duan Q. Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data. Int. J. Fatigue. 2022;160:106860. doi: 10.1016/j.ijfatigue.2022.106860. DOI

Ambrosioa D., Desseina G., Wagnera V., Yahiaouia M., Parisa J.-Y., Fazzinia M., Cahucb O. On the potential applications of acoustic emission in friction stir welding. J. Manuf. Processes. 2022;75:461–475. doi: 10.1016/j.jmapro.2022.01.012. DOI