Efficient sorting of resin-based CFRP composites is critical for optimizing composite recycling streams. In this work, a methodology integrating Laser-Induced Breakdown Spectroscopy (LIBS) with Machine Learning (ML)-enhanced classification models to achieve accurate material discrimination is presented. LIBS is employed to identify the chemical composition of individual compounds, producing spectrograms that are subsequently processed to group chemically similar materials based on Epoxy resin (Bisphenol-A). The grouped datasets that contain 4000 peaks and 665 features were sampled to standardize feature dimensionality and cleaned to remove noise. A statistical analysis is then conducted to select the most informative features, followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, classification is performed using a Support Vector Classification (SVC) model, fine-tuned to the processed data to maximize accuracy. With a 5-fold cross validation (CV), the average nested accuracy score is 0.8317 ± 0.0212. This integrated approach demonstrates the potential for advancing automated sorting technologies in composite recycling applications.
Kanakis et al. (Sat,) studied this question.