Rapid Identification of Homogeneous Alloys Based on Laser-Induced Breakdown Spectroscopy Combined with Machine-Learning Algorithms
In this study,laser-induced breakdown spectroscopy(LIBS)combined with machine learning algorithms was employed to identify the grades of nine homogeneous,national,standard alloy-steel samples.The original LIBS spectra of the alloy steels were processed using a statistically sensitive nonlinear iterative peak-clipping(SNIP)algorithm for continuous background subtraction.Principal component analysis(PCA)was used to reduce the dimensionality of spectral data and eliminate redundant information.The first 10 principal components constitute 94.3%of the total variance.The LIBS spectral data of the nine homogeneous alloy steels were partitioned into a 7∶3 ratio to create training and testing datasets.Based on the first 10 principal components obtained from PCA,PCA-support vector machine(SVM),PCA-decision tree,PCA-K nearest neighbor(KNN),and PCA-linear discriminant analysis(LDA)models were established for alloy-steel identification.The average accuracies of the four models for the training set are 99.06%,97.47%,90.47%,and 100%for the SVM,decision tree,KNN,and LDA,respectively,whereas those for the testing set are 96.29%,79.63%,67.04%,and 100%,respectively.The PCA-LDA model achieves a 100%identification rate for homogeneous alloy-steel grades.This study provides method and reference for the rapid identification of homogeneous alloy-steel grades using laser-induced breakdown spectroscopy.
laser-induced breakdown spectroscopypeak-clipping algorithmmachine learninghomogeneous metal identification
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NSTL
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