Акустико-эмиссионная диагностика латентных дефектов в многослойных печатных платах радиоэлектронных устройств
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Acoustic emission diagnostics of hidden defects of multilayer printed circuit boards in electronic devices

idUvaysov S.U. idChernoverskaya V.V. Nguyen H.D.   Lu N.T.   The H.V.  

UDC 621.396.69
DOI: 10.26102/2310-6018/2024.44.1.004

  • Abstract
  • List of references
  • About authors

The article presents the results of the acoustic emission method application (AE) and machine learning algorithms in the problem of diagnosing defects in the stratification of a multilayer printed circuit board structure (MPB). A combination of physical and computational experiments is used to solve the problem. To conduct full-scale tests, the study uses a vibration stand to generate a load on the test object and receive acoustic emission signals. The computational experiment is carried out using mathematical modeling in a specialized ABAQUS environment. In order to obtain the best solution to the problem, an optimization problem is solved during the experiment to determine the frequency of the harmonic signal generated by the vibration stand with a view to receiving the maximum response of the MPB under review and unambiguous identification of the bundle defect. When conducting the numerical experiments, the effects and reactions (AE signals) of MPB were modeled at different frequencies of input vibration signals ranging from 100 to 2000 Hz. Full-scale experiments were conducted in the laboratory of control and testing of radioelectronic devices at the Department of KPRES of RTU MIREA. The results of the study have shown that the vibration frequency most effective for detecting a delamination defect equals 1500 Hz (a defect of almost rectangular shape with a size of 30×37 mm). Subsequently, this was confirmed by correlation analysis, which made it possible to identify the maximum differences between the acoustic emission signals of a suitable MPB sample and a sample with a delamination defect for the input vibration of a given frequency. The second part of the study deals with processing of the physical and computational experiment results, establishing the degree of adequacy of the obtained mathematical models to real samples of MPB and the processes occurring in them, as well as the use of machine learning algorithms for more reliable diagnosis of MPB defects. In the presented study, the random forest and the support vector machine learning (SVM) methods were employed as machine learning algorithms. Based on the results of their execution, the accuracy of the two algorithms was evaluated.

1. Smirnov A.O., Farafonov V.A., Kartoshkin A.D. Acoustic emission in materials: wave generation mechanisms, application and technological features of non-destructive testing. Vestnik nauki. 2023;5(4):828–833. (In Russ.).

2. Marcas, J., Smith, A., Jones, R. Application of vibration table and acoustic emission to detect delamination defects in printed circuit boards. Journal of Engineering Acoustics. 2016;48(2):117–129.

3. Hu X., Yue Y., Cai C., Qi Z.M. Temperature-robust optical microphone with a compact grating interferometric module. Applied Optics. 2023;62(23):6072–6080 DOI: 10.1364/AO.489968.

4. Teng S., Chen X., Chen G., Cheng L., Bassir D. Structural damage detection based on convolutional neural networks and population of bridges. Measurement. 2022;202:17–47. DOI: 10.1016/j.measurement.2022.111747.

5. Slesarev D.A. Processing and analysis of signals in non-destructive testing. Moscow, MEI; 2013. 99 p. (In Russ.).

6. Gallyamov I.I. Physical basis of nondestructive testing and flaw detection. Ufa, UGNTU; 2015. 101 p. (In Russ.).

7. Nushtaev D.V., Tropkin S.N. Abaqus: a textbook for beginners. Moscow, OOO TESIS; 2010. 43 p. (In Russ.).

8. Fam Le Kuok Khan'. Diagnostics of radioelectronic devices in impact testing. Candidate of Engineering Sciences Thesis. Moscow, RTU MIREA; 2021. 157 p. (In Russ.).

9. Zolochevskii A.A., Bekker A.A. Introduction to ABAQUS. A textbook. Kharkov, TOV “Bіznes Іnvestor Grup”; 2011. 49 p. (In Russ.).

10. Manilyk T., Il'in K. Practical application of ABAQUS in engineering problems. Version 6.5. Moscow, MFTI, OOO TESIS; 2006. 67 p. (In Russ.).

11. Uvaysov S.U., Chernoverskaya V.V., Nguyen Hong Duc, Lu Ngoc Tien. The use of an artificial neural network in the task of ultrasonic diagnostics of printed circuit boards of electronic devices. Modeling, Optimization and Information Technology. 2023;11(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1338. DOI: 10.26102/2310-6018/2023.41.2.020 (In Russ.).

12. Ermakov S.M. The Monte Carlo method in computational mathematics: an introductory course. Moscow, BINOM. Laboratoriya znanii; 2018. 192 p. (In Russ.).

13. Uvaysov S.U. Chernoverskaya V.V. Dang N.V. Tuan N.V. The use of an artificial neural network in thermal diagnostics of the printed node of the on-board take-off control device of an aircraft. Modeling, Optimization and Information Technology. 2022;10(3). URL: https://moitvivt.ru/ru/journal/pdf?id=1218. DOI: 10.26102/2310-6018/2022.38.3.012 (In Russ.).

14. Rostovtsev V.S. Artificial neural networks: a textbook. Kirov, Izd-vo VyatGU; 2014. 208 p. (In Russ.).

Uvaysov Saygid Uvaysovich
Doctor of Engineering Sciences Professor

Scopus | ORCID |

MIREA - Russian Technological University

Moscow, the Russian Federation

Chernoverskaya Victoria Vladimirovna
Candidate of Engineering Sciences Associate Professor

ORCID |

MIREA - Russian Technological University

Moscow, the Russian Federation

Nguyen Hong Duc

MIREA - Russian Technological University

Moscow, the Russian Federation

Lu Ngoc Tien

MIREA - Russian Technological University

Moscow, the Russian Federation

The Hai Vo

MIREA - Russian Technological University

Moscow, the Russian Federation

Keywords: acoustic emission, multilayer printed circuit board, hidden defects, structure stratification, modeling, physical experiment, machine learning algorithm, support vector machine method, random forest method, non-destructive testing

For citation: Uvaysov S.U. Chernoverskaya V.V. Nguyen H.D. Lu N.T. The H.V. Acoustic emission diagnostics of hidden defects of multilayer printed circuit boards in electronic devices. Modeling, Optimization and Information Technology. 2024;12(1). Available from: https://moitvivt.ru/ru/journal/pdf?id=1493 DOI: 10.26102/2310-6018/2024.44.1.004 (In Russ).

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Full text in PDF

Received 20.12.2023

Revised 22.01.2024

Published 29.01.2024