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Reflector type recognition using neural network based on TOFD echoes
- Authors: Bazulin E.G.1, Medvedev L.V.1
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Affiliations:
- ECHO+ Research and Production Center LLC
- Issue: No 6 (2025)
- Pages: 3-10
- Section: Acoustic methods
- URL: https://rjdentistry.com/0130-3082/article/view/686466
- DOI: https://doi.org/10.31857/S0130308225060013
- ID: 686466
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Abstract
In this paper we propose to automate the classification of reflector types by TOFD-echoes using ResNet-18 convolutional neural network. The main focus is on modeling and classification of reflectors such as cracks, pores, non-welds 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 different types of reflectors. The study confirmed the possibility of using neural networks to determine the reflector type from TOFD-echo signals, which allows to automate 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.
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About the authors
E. G. Bazulin
ECHO+ Research and Production Center LLC
Author for correspondence.
Email: bazulin@echoplus.ru
Russian Federation, 123458 Moscow, Tvardovskogo str., 8, Technopark «Strogino»
L. V. Medvedev
ECHO+ Research and Production Center LLC
Email: bazulin@echoplus.ru
Russian Federation, 123458 Moscow, Tvardovskogo str., 8, Technopark «Strogino»
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