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Crack Size Determination Through Neural Network Using Back Scattered Ultrasonic Signal

저면산란 초음파 신호 및 신경회로망을 이용한 균열크기 결정

  • Published : 2000.01.01

Abstract

The role of quantitative nondestructive evaluation of defects is becoming more important to assure the reliability and the safety of structure, which can eventually be used for residual life evaluation of structure on the basis of fracture mechanics approach. Although ultrasonic technique is one of the most widely used techniques for application of practical field test among the various nondestructive evaluation technique, there are still some problems to be solved in effective extraction and classification of ultrasonic signal from their noisy ultrasonic waveforms. Therefore, crack size determination through a neural network based on the back-propagation algorithm using back-scattered ultrasonic signals is established in this study. For this purpose, aluminum plate containing vertical or inclined surface breaking crack with different crack length was used to receive the back-scattered ultrasonic signals by pulse echo method. Some features extracted from these signals and sizes of cracks were used to train neural network and the neural network's output of the crack size are compared with the true answer.

Keywords

Ultrasonic Wave;Nondestructive Test;Neural Network;Crack;Pulse Echo;Artificial Defect

References

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