Detection and classification of SMC composites based on LIBS

The electric energy meter box is an important protective unit of the power grid system, which is usually made of sheet molding compound (SMC) composites. Its quality and operational reliability have a significant impact on the safety of the power grid. However, there is currently a lack of rapid and effective detection methods for the shell material of the electric energy meter box. This paper proposes a rapid detection method for the SMCs material of the electric energy meter box based on laser-induced breakdown spectroscopy technology and develops an optimization method for the laser test parameters. For different formulations of the materials, two classification methods based on support vector machine (SVM) and self-organizing map (SOM) neural network are established. The classification accuracy of SVM reaches 99.9%, and the classification accuracies of SOM neural network are all over 90%. This method achieves rapid testing and classification of SMC composites used in energy meter boxes, providing new theoretical and practical approaches to enhance the safety and stable operation of the power grid.