菲索干涉仪
干涉测量
激光器
材料科学
光学
超声波传感器
人工神经网络
光纤
灵敏度(控制系统)
声学
计算机科学
物理
人工智能
天文干涉仪
电子工程
工程类
作者
JinHyok Choe,Song Jon,WonSok Ryang,YongMi Yun,Juhyok So
标识
DOI:10.1016/j.optlastec.2022.107857
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.
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