Laser-Based Characterization and Classification of Functional Alloy Materials (AlCuPbSiSnZn) Using Calibration-Free Laser-Induced Breakdown Spectroscopy and a Laser Ablation Time-of-Flight Mass Spectrometer for Electrotechnical Applications.

In this paper, we present the analysis of functional alloy samples containing metals aluminum (Al), copper (Cu), lead (Pb), silicon (Si), tin (Sn), and zinc (Zn) using a Q-switched Nd laser operating at a wavelength of 532 nm with a pulse duration of 5 ns. Nine pelletized alloy samples were prepared, each containing varying chemical concentrations (wt.%) of Al, Cu, Pb, Si, Sn, and Zn-elements commonly used in electrotechnical and thermal functional materials. The laser beam is focused on the target surface, and the resulting emission spectrum is captured within the temperature interval of 9.0×103 to 1.1×104 K using a set of compact Avantes spectrometers. Each spectrometer is equipped with a linear charged-coupled device (CCD) array set at a 2 μs gate delay for spectrum recording. The quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under the assumptions of optically thin plasma and self-absorption-free conditions, as well as local thermodynamic equilibrium (LTE). The net normalized integrated intensities of the selected emission lines were utilized for the analysis. The intensities were normalized by dividing the net integrated intensity of each line by that of the aluminum emission line (Al II) at 281.62 nm. The results obtained using CF-LIBS were compared with those from the laser ablation time-of-flight mass spectrometer (LA-TOF-MS), showing good agreement between the two techniques. Furthermore, a random forest technique (RFT) was employed using LIBS spectral data for sample classification. The RFT technique achieves the highest accuracy of ~98.89% using out-of-bag (OOB) estimation for grouping, while a 10-fold cross-validation technique, implemented for comparison, yields a mean accuracy of ~99.12%. The integrated use of LIBS, LA-TOF-MS, and machine learning (e.g., RFT) enables fast, preparation-free analysis and classification of functional metallic materials, highlighting the synergy between quantitative techniques and data-driven methods.