PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
This work addresses metal recycling efficiency by improving alloy classification, but it is incremental as it compares existing detectors and methods without introducing major innovations.
The paper tackled the problem of real-time differentiation between aluminium and copper alloys for metal recycling by using PGNAA spectral data and machine learning, achieving best results with Maximum Likelihood Classifier and Conditional Variational Autoencoder, with CeBr3 excelling in short measurement times and HPGe in longer durations.
In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is utilized for classification. The study compares data from two detectors, cerium bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy resolution and sensitivity. We test various data generation, preprocessing, and classification methods, with Maximum Likelihood Classifier (MLC) and Conditional Variational Autoencoder (CVAE) yielding the best results. The study also highlights the impact of different detector types on classification accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe performing better in longer durations. The findings suggest the importance of selecting the appropriate detector and methodology based on specific application requirements.