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Abstarct <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>

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Recognition of Reflector Type Using Neural Network Based on TOFD Echoes

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • E. G. Bazulin,
  • L. V. Medvedev

摘要

Abstarct

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.