Machine learning-assisted laser-induced breakdown spectroscopy for estimating substrate surface temperatures

Abstract

Laser-Induced Breakdown Spectroscopy (LIBS) has been widely used across industries, medical applications, and environmental monitoring for elemental identification and concentration analysis due to its high accuracy, speed, and efficiency. Beyond elemental identification and concentration analysis, many studies suggest that LIBS signal intensities are influenced by sample surface temperatures, presenting an opportunity for temperature monitoring in processes such as three-dimensional additive manufacturing. In such applications, accurately detecting local temperatures at printing spots of interest is critical, specifically in ceramic printing, where phase transitions require temperatures exceeding one thousand degrees Celsius. Due to the dynamic nature of plasma emissions and experimental variability, there are few reports on the use of LIBS for monitoring sample surface temperatures. The direct use of absolute LIBS intensities is challenging for this purpose. Instead, this study explored the use of intensity ratios for surface temperature estimation. A series of LIBS spectra over wavelengths from 430.96 to 438.99 nm were collected from zirconium carbide (ZrC) at temperatures ranging from 350 to 600 °C. Intensity ratios, including atomic-to-atomic, ionization-to-ionization, and atomic-to-ionization line ratios, were evaluated. These ratios demonstrated significant exponential correlations with surface temperatures. Among the regression models, the highest R-squared (R²) value of 0.976 was observed for the intensity ratio of Zr II 435.974 nm to Zr I 434.789 nm. Additionally, machine learning algorithms were applied for full LIBS spectrum analysis, enabling comprehensive classification and prediction of sample surface temperatures without relying solely on a single intensity ratio. This strategy has demonstrated the potential of machine learning-assisted LIBS for real-time detection of sample surface temperatures in complex and dynamic environments.