{"title":"Recognition of Reflector Type Using Neural Network Based on TOFD Echoes","authors":"E. G. Bazulin, L. V. Medvedev","doi":"10.1134/S1061830925600285","DOIUrl":null,"url":null,"abstract":"<p>In this paper we propose to automate the classification of reflector types by TOFD echoes using the ResNet-18 convolutional neural network. The main focus is on modeling and classification of reflectors such as cracks, pores, nonwelds, and void areas. Experiments included training the model on TOFD echoes calculated both in a numerical experiment and TOFD echoes measured during ultrasonic inspection. The results showed high classification accuracy: 96.2% in the numerical experiment, 97% on experimentally measured TOFD echoes with various types of reflectors. The study confirmed the possibility of using neural networks to determine the reflector type based on TOFD echo signals; this allows automating the process of nondestructive testing and reduce the influence of human factor. For further development of the method it is suggested to use segmentation models for processing images with several reflectors.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"61 6","pages":"603 - 609"},"PeriodicalIF":0.9000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830925600285","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper we propose to automate the classification of reflector types by TOFD echoes using the ResNet-18 convolutional neural network. The main focus is on modeling and classification of reflectors such as cracks, pores, nonwelds, and void areas. Experiments included training the model on TOFD echoes calculated both in a numerical experiment and TOFD echoes measured during ultrasonic inspection. The results showed high classification accuracy: 96.2% in the numerical experiment, 97% on experimentally measured TOFD echoes with various types of reflectors. The study confirmed the possibility of using neural networks to determine the reflector type based on TOFD echo signals; this allows automating the process of nondestructive testing and reduce the influence of human factor. For further development of the method it is suggested to use segmentation models for processing images with several reflectors.
期刊介绍：
Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
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