Determination of austenitic steel alloys composition using laser-induced breakdown spectroscopy (LIBS) and machine learning algorithms

In this paper, the determination of composition of certified samples of austenitic steel alloys was done by combining laser-induced breakdown spectroscopy (LIBS) technique with machine learning algorithms. Isolation forest algorithm was applied to the MinMax scaled LIBS spectra in the spectral range form (200–500) nm to detect and eject possible outliers. Training dataset was then fitted with random forest regressor (RFR) and Gini importance criterion was used to identify the features that contribute the most to the final prediction. Optimal model parameters were found by using grid search cross-validation algorithm. This was followed by final RFR training. Results of RFR model were compared to the results obtained from linear regression with $$\mathcal {L}^{2}$$ norm and deep neural network (DNN) by means of $$R^{2}$$ metrics and root-mean-square error. DNN showed the best predictive power, whereas random forest had good prediction results in the case of Cr, Mn and Ni, but in the case of Mo, it showed limited performance