Multiphase Identification Through Automatic Classification from Large-Scale Nanoindentation Mapping Compared to an EBSD-Machine Learning Approach - Carl Slater; Bharath Bandi; Pedram Dastur; Claire Davis (Metals)
Characterising and quantifying complex multiphase steels is a challenging and time-consuming process, which is often open to subjectivity when based on image analysis of optical metallographic or SEM images. The properties of multiphase steels are highly sensitive to their individual phase properties and fractions, necessitating the development of robust characterisation tools. This paper presents a method for classifying nanoindentation maps into proportional fractions of up to five distinct microstructural regions in dual-phase and complex-phase steels. The phases/regions considered are ferrite, ferrite containing mobile dislocations, bainite, tempered martensite, and untempered martensite. A range of microstructures with varying fractions of phases were evaluated using both SEM/EBSD and nanoindentation. A machine learning (ML) approach applied to EBSD data showed good consistency in characterising a two-phase system. However, as the microstructural system complexity increased, variations were observed between different analysts and the sensitivity to the ML training data increased when four phases were present (reaching up to ~11% difference in the ferrite phase fraction determined). The proposed nanoindentation mapping technique does not show operator sensitivity and enables the quantification of additional microstructural features, such as identifying and quantifying ferrite regions with a high density of mobile dislocations and the degree of martensite tempering.