An automated method of quantifying ferrite microstructures using electron backscatter diffraction (EBSD) data

The identification and quantification of the different ferrite microconstituents in steels has long been a major challenge for metallurgists. Manual point counting from images obtained by optical and scanning electron microscopy (SEM) is commonly used for this purpose. While classification systems exist, the complexity of steel microstructures means that identifying and quantifying these phases is still a great challenge. Moreover, point counting is extremely tedious, time consuming, and subject to operator bias. This paper presents a new automated identification and quantification technique for the characterisation of complex ferrite microstructures by electron backscatter diffraction (EBSD). This technique takes advantage of the fact that different classes of ferrite exhibit preferential grain boundary misorientations, aspect ratios and mean misorientation, all of which can be detected using current EBSD software. These characteristics are set as criteria for identification and linked to grain size to determine the area fractions. The results of this method were evaluated by comparing the new automated technique with point counting results. The technique could easily be applied to a range of other steel microstructures. •New automated method to identify and quantify ferrite microconstituents in HSLA steels is presented.•Unique characteristics of the ferrite microconstituents are investigated using EBSD.•Characteristics of ferrite microconstituents are exploited to identify the type of ferrite grains within the steel's microstructures.•The identified ferrite grains are linked to their associated grain's size for area fraction calculations.