Research on LIBS online monitoring criteria for aircraft skin laser paint removal based on OPLS-DA

Abstract

Online monitoring technology plays a pivotal role in advancing the utilization of laser paint removal in aircraft maintenance and automation. Through the utilization of a high-frequency infrared pulse laser paint removal laser-induced breakdown spectroscopy (LIBS) online monitoring platform, this research conducted data collection encompassing 60 sets of LIBS spectra during the paint removal process. Classification and identification models were established employing principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). These models served as the foundation for creating criteria and rules for the online LIBS monitoring of the controlled paint removal process for aircraft skin. In this research, 12 selected characteristic spectral lines were used to construct the OPLS-DA model, with a predictive root mean square error (RMSEP) of 0.2873. Both full spectrum and feature spectral line data achieved a predictive accuracy of 94.4%. The selection of feature spectral lines maintains predictive performance while significantly reducing the amount of input data. Consequently, this research offers a methodological reference for further advancements in online monitoring technology for laser paint removal in aircraft skin.