Enhanced Classification of Ultra-Thin Austenitic Steel Foils via Feature-Optimized LIBS and Plasma Temperature Integration

Reliable classification of ultra-thin austenitic stainless steel foils (e.g., 301, 304, and 316L) is critical for high-end manufacturing in electronics, aerospace, and new energy. Currently, the grading technologies for stainless steel are not only time-consuming but also fails to the requirement of in-situ industrial processes. Laser-induced breakdown spectroscopy (LIBS) is an on-line detection method and possesses broad industrial applications. However, the inherent similarity in elemental compositions and the instability of laser-induced plasmas in thin-gauge materials pose significant challenges for conventional LIBS classification. This study presents a robust classification based on LIBS coupled with optimized feature extraction and generalized spectral integration. We evaluated twelve hybrid models by combining three feature selection methods: Analysis of Variance (ANOVA), ReliefF, and Linear Discriminant Analysis (LDA) with four machine learning algorithms (ANN, KNN, XGBoost, and Logistic Regression). Results indicate that the LDA-LR configuration achieves the highest classification efficiency, reaching an accuracy of 95.7% with 58 optimized features. Furthermore, the classification accuracy was further improved to 96.2% by incorporating plasma temperature (Te) as a generalized physical feature and filtering invalid data. This work demonstrates that integrating physical plasma parameters with data-driven models significantly improves the discriminative power of LIBS for complex industrial ultra-thin alloys.