Abstract

Laser-induced breakdown spectroscopy (LIBS) is expected to be used for real-time monitoring and closed-loop control of laser-based layered controlled paint removal (LLCPR) from aircraft skin. However, the LIBS spectrum must be rapidly and accurately analyzed, and the monitoring criteria should be established based on machine learning algorithms. Hence, this study develops a self-built LIBS monitoring platform for the paint removal process utilizing a high-frequency (kilohertz-level) nanosecond infrared pulsed laser and collects the LIBS spectrum during the laser removal process of the top coating (TC), primer (PR), and aluminum substrate (AS). After subtracting the spectrum’s continuous background and screening the key features, we construct a classification model of three types of spectra (TC, PR, and AS) based on a random forest algorithm, and the real-time monitoring criterion based on the classification model and multiple LIBS spectra was established and verified experimentally. The results show that the classification accuracy is 98.89%, the time-consuming classification is about 0.03 ms per spectrum, and the monitoring results of the paint removal process are consistent with the macroscopic observation and microscopic profile analysis results of the samples. Overall, this research provides core technical support for the real-time monitoring and closed-loop control of LLCPR from aircraft skin.

© 2023 Optica Publishing Group

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