Classification of coal deposited epoxy micro-nanocomposites by adopting machine learning techniques to LIBS analysis

Epoxy micro-nanocomposite specimens incorporated with 66 wt% of silica micro fillers and 0.7 wt% of ion trapping particles as nano fillers, are coated with four different variants of coal. The conductivity of the coal deposited samples is observed to be in direct correlation with the percentage carbon content present in the coal samples. The epoxy micro-nanocomposite specimens coated with different variants of coals were successfully classified by using Laser induced breakdown spectroscopy (LIBS) assisted by various machine learning techniques. It is noticed that the classification through Logistic regression method (LRM) has reflected a higher training as well as testing accuracy of 100% and 98%, respectively when compared to other machine learning methods.

Automated summary

The conductivity of epoxy micro-nanocomposite specimens coated with different variants of coals is directly correlated with the carbon content of the coal samples. Using LIBS and machine learning techniques, the specimens were successfully classified, with Logistic regression method showing the highest training and testing accuracy of 100% and 98% respectively compared to other methods.