Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process

Soojin Choi; Changkyoo Park

doi:10.3390/s23010083

Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process

Sensors (Basel). 2022 Dec 22;23(1):83. doi: 10.3390/s23010083.

Authors

Soojin Choi¹, Changkyoo Park^{1

2}

Affiliations

¹ Department of Laser and Electron Beam Technologies, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea.
² Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Abstract

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process.

Keywords: convolution neural network; laser cleaning; laser-induced breakdown spectroscopy; monitoring; paint removal.

Grants and funding

21-SN-EC-03/the Institute of Civil Military Technology Cooperation, funded by the Defense Acquisition Program Administration and the Ministry of Trade, Industry, and Energy of the Korean government