Estimation of the aging grade of T91 steel by laser-induced breakdown spectroscopy coupled with support vector machines

T91 steel is a representative martensitic heat-resistant steel widely used in high temperature compression components of industrial equipment. During the service period, the operation safety and the service life of the equipment will be affected by the change of structure and mechanical properties of the steel components, which is called material aging. In order to develop a rapid in-situ aging estimation technology of high temperature compression components surface, laser-induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) was employed in this paper. The spectral characteristics of 10 T91 steel specimens with different aging grades were analyzed. Line intensities and the line intensity ratios (ionic/atomic and alloying element/matrix element) that indicate the change of metallographic structure were used to establish SVM models, and the results using different variable sets were compared. The model was optimized by comparing different pulse number for practical effectiveness, and the robustness of the model was investigated in dealing with the inhomogeneity of steel composition. The study results show that the estimation model obtained the best performance using line intensities and line intensity ratios averaged from 31st–60th laser pulses as input variables. The estimation accuracy of validation set was greatly improved from 75.8% to 95.3%. In addition, the model showed the outstanding capacity for handling the fluctuations of spectral signals between measuring-points (spots), which indicated that the aging estimation based on a few measuring-points is feasible. The studies presented here demonstrate that the LIBS coupled with SVM is a new useful technique for the aging estimation of steel, and would be well-suited for fast safety assessment in industrial field. [Display omitted] •Using LIBS coupled with SVM for aging estimation of T91 steel.•Established SVM aging estimation model based on spectral characteristics of samples.•Evaluated the capability of model for handling the issues in practical measurement.