{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:45:16Z","timestamp":1780609516672,"version":"3.54.1"},"reference-count":18,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Institute of Civil Military Technology Cooperation","award":["21-SN-EC-03"],"award-info":[{"award-number":["21-SN-EC-03"]}]},{"name":"Defense Acquisition Program Administration and the Ministry of Trade, Industry, and Energy of the Korean government","award":["21-SN-EC-03"],"award-info":[{"award-number":["21-SN-EC-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23010083","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:26:25Z","timestamp":1671765985000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3384-6566","authenticated-orcid":false,"given":"Soojin","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Laser and Electron Beam Technologies, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changkyoo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Laser and Electron Beam Technologies, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea"},{"name":"Department of Materials Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.jmrt.2021.11.147","article-title":"Effect of laser surface cleaning of corroded 304L stainless steel on microstructure and mechanical properties","volume":"16","author":"Yoo","year":"2022","journal-title":"J. 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