Automatic classification of true and false laser-induced damage in large aperture optics

Fupeng Wei; Fengdong Chen; Bingguo Liu; Zhitao Peng; Jun Tang; Qihua Zhu; Dongxia Hu; Yong Xiang; Nan Liu; Zhihong Sun; Guodong Liu

doi:10.1117/1.OE.57.5.053112

24 May 2018 Automatic classification of true and false laser-induced damage in large aperture optics

Fupeng Wei, Fengdong Chen, Bingguo Liu, Zhitao Peng, Jun Tang, Qihua Zhu, Dongxia Hu, Yong Xiang, Nan Liu, Zhihong Sun, Guodong Liu

Author Affiliations +

Optical Engineering, Vol. 57, Issue 5, 053112 (May 2018). https://doi.org/10.1117/1.OE.57.5.053112

Abstract

An automatic classification method based on machine learning is proposed to distinguish between true and false laser-induced damage in large aperture optics. First, far-field light intensity distributions are calculated via numerical calculations based on both the finite-difference time-domain and the Fourier optical angle spectrum theory for Maxwell’s equations. The feature vectors are presented to describe the possible damage sites, which include true and false damage sites. Finally, a kernel-based extreme learning machine is used for automatic recognition of the true sites and false sites. The method studied in this paper achieves good recognition of false damage, which includes a variety of types, especially attachment-type false damage, which has rarely been studied before.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Fupeng Wei, Fengdong Chen, Bingguo Liu, Zhitao Peng, Jun Tang, Qihua Zhu, Dongxia Hu, Yong Xiang, Nan Liu, Zhihong Sun, and Guodong Liu "Automatic classification of true and false laser-induced damage in large aperture optics," Optical Engineering 57(5), 053112 (24 May 2018). https://doi.org/10.1117/1.OE.57.5.053112

Received: 4 November 2017; Accepted: 26 April 2018; Published: 24 May 2018

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