2023 Volume 109 Issue 7 Pages 623-637
The distribution of alloying elements was investigated to clarify latent features of the microstructure formation in TRIP-aided Multi-phase steels by using an unsupervised machine learning technique. The cold-rolled specimens with a composition of Fe-0.20mass%C-1.5mass%Si-1.5mass%Mn were heat-treated under various conditions. The FE-EPMA (field emission electron probe microanalysis) method was used to accurately measure the distribution of C, Si and Mn. The two-phase microstructure of ferrite and austenite was formed during intercritical annealing at 800 °C for 120 s. C and Mn were distributed in the austenite region while Si was distributed in the ferrite region. Part of the austenite transformed to bainite by subsequent heat-treatment of isothermal holding at 400 °C for 1200 s. The C concentration in the austenite region increased due to bainite formation. The microstructure formation factor was estimated by primary component analysis of big data on the elemental distribution. The cumulative contribution ratio of the second principal component was 0.85. Therefore, most of the information was contained in the first and second principal components. In the first principal component, the coefficients for the standardized C and Mn concentrations possessed positive values, while that for the standardized Si concentration possessed negative value. The tendency of each coefficient corresponds to the C, Si and Mn distribution caused by intercritical annealing. Moreover, the absolute value of the coefficient for the standardized C concentration was significantly larger than those of the other coefficients in the second principal component. The component represents the C enrichment of austenite due to bainite transformation.
