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
收藏
页数：7
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