Mathematical morphology applied to the study of dual phase steel formation

Dual Phase steel (DP steel) has shown high potential for automotive and other applications, due to its remarkable combined properties of high strength and good formability. The mechanical properties of the material are strictly related to the spatial distribution of the two steel phases, ferrite and martensite, and with their stochastic geometry. Unfortunately the experimental costs to obtain images of sections of steel samples are very high, so that one important industrial problem is to reduce the required number of 2D sections in order to either reconstruct the 3D geometry of the material, or to simulate realistic ones. In this work we will present a germ-grain statistical model which can be used for a best fitting of the main geometric characteristics of the martensite phase. The parameters of the model are estimated on the basis of morphological characteristics of the images of about 150 tomographic sections taken from a real sample. After optimization or tuning of the relevant parameters, the statistical model can then be used to identify the minimum number of sections of the sample which are needed to estimate the parameters in a reliable way.