Research of laser ultrasonic defect statistics recognition technology based on radial basis function neural network

被引：7

作者：

Choe, JinHyok ^{[1
]}

Jon, Song ^{[2
]}

Ryang, WonSok ^{[2
]}

Yun, YongMi ^{[3
]}

So, Juhyok ^{[3
]}

机构：

[1] KimChaek Univ Technol, Dept Phys, Pyongyang 950003, North Korea

[2] Univ Sci, Dept Phys, Pyongyang 950003, North Korea

[3] Univ Sci, Dept Chem, Pyongyang 950003, North Korea

来源：

OPTICS AND LASER TECHNOLOGY | 2022年 / 150卷

关键词：

Surface defect; Optical fiber Fizeau interferometer; RBF neural network; SAW; SURFACE CRACK DEPTH; QUANTITATIVE-EVALUATION;

D O I：

10.1016/j.optlastec.2022.107857

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this study, we detected the depth of surface defect at a statistic on rate of 95.8% or more by using the Radial Basis Function Neural Network, a Q-switched and pulsed Nd:YAG laser and optical fiber Fizeau interferometer. In the experiment, Nd:YAG pulsed laser was used for generating ultrasound in a sample, while an optical fiber Fizeau interferometer was used to detect the ultrasound generated by a laser line source. Our results showed that this system has a high sensitivity as well as a high spatial resolution, what is more important is that this system is applicable to practice field.

引用

页数：7

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