Method to improve the classification accuracy by in situ laser cleaning of painted metal scraps during laser-induced breakdown spectroscopy based sorting

Abstract Scrap metals are typically covered with surface contaminants, such as paint, dust, and rust, which can significantly affect the emission spectrum during laser-induced breakdown spectroscopy (LIBS) based sorting. In this study, the effects of paint layers on metal surfaces during LIBS classification were investigated. LIBS spectra were collected from metal surfaces painted with black and white paints by ablation with a nanosecond pulsed laser (wavelength = 1064 nm, pulse width = 7 ns). For the black-painted samples, the LIBS spectra showed a broad background emission, emission lines unrelated to the target metals, large shot-to-shot variation, and a relatively low signal intensity of the target metal, causing poor classification accuracy even at high shot numbers. Cleaning the black paint layer by ablating over a wide area prior to LIBS analysis resulted in high classification accuracy with fewer shot numbers. A method to determine the number of cleaning shots necessary to obtain high classification accuracy and high throughput is proposed on the basis of the change in LIBS signal intensity during cleaning shots. For the white-painted samples, the paint peeled off the metal surface after the first shot, and strong LIBS signals were measured after the following shot, which were attributed to the nanoparticles generated by the ablation of the paint, allowing an accurate classification after only two shots. The results demonstrate that different approaches must be employed depending on the paint color to achieve high classification accuracy with fewer shot numbers.