UDC 004.032.26 DOI: 10.31548/machinery/1.2024.43 Application of the regression neural network for the analysis of the results of ultrasonic testing Abstract. Conducting a study on this topic becomes relevant due to the great importance of the safety of critical infrastructure facilities and the presence of operational defects in equipment elements and pipelines, which poses serious threats, including the possibility of equipment destruction and negative environmental impact. The purpose of this work is to study the possibility of using the diffraction-time technique of ultrasonic non-destructive testing together with a deep convolutional neural network to accurately determine the numerical value of the height of an operational crack. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method, and others. The study found that an automated approach to measuring crack height, based on diffraction signals and the use of neural networks, significantly improved the quality and accuracy of non-destructive testing. Ultrasonic testing is one of the most common inspection methods for detecting service cracks and is considered to be the most effective. It allows for reliable detection of defects and determination of their size without destroying the product. The results of the study emphasize the high potential and efficiency of the method in analysing the data obtained and provide confirmation of its applicability for determining the condition of objects during ultrasonic inspection. The paper emphasizes that these technologies are particularly important and effective. It is noted that their widespread use Suggested Citation: Andriievskyi, I., Spivak, S., Gogota, O., & Yermolenko, R. (2024). Application of the regression neural network for the analysis of the results of ultrasonic testing. Machinery & Energetics, 15(1), 43-55. doi: 10.31548/machinery/1.2024.43. *Corresponding author Ivan Andriievskyi Student Taras Shevchenko National University of Kyiv 01033, 60 Volodymyrska Str., Kyiv, Ukraine https://orcid.org/0009-0003-5186-735X Sofiia Spivak Student Taras Shevchenko National University of Kyiv 01033, 60 Volodymyrska Str., Kyiv, Ukraine https://orcid.org/0009-0004-8535-1818 Olga Gogota PhD in Physical and Mathematical Sciences, Junior Researcher Taras Shevchenko National University of Kyiv 01033, 60 Volodymyrska Str., Kyiv, Ukraine https://orcid.org/0000-0003-4108-7256 Ruslan Yermolenko * PhD in Physical and Mathematical Sciences, Associate Professor Taras Shevchenko National University of Kyiv 01033, 60 Volodymyrska Str., Kyiv, Ukraine https://orcid.org/0009-0000-0854-4668 Article’s History: Received: 03.11.2023; Revised: 05.02.2024; Accepted: 28.02.2024. Machinery & Energetics Vol. 15, No. 1. 2024 Journal homepage: https://technicalscience.com.ua/en Copyright © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/)