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The microalloying with niobium (Nb) and titanium (Ti) is standardly applied in low carbon steel high-strength low-alloy (HSLA) steels and enables austenite conditioning during thermo-mechanical controlled processing (TMCP), which results in pronounced grain refinement in the finished steel. In that respect, it is important to better understand the precipitation kinetics as well as the precipitation sequence in a typical Nb-Ti-microalloyed steel. Various characterization methods were utilized in this study for tracing microalloy precipitation after simulating different austenite TMCP conditions in a Gleeble thermo-mechanical simulator. Atom probe tomography (APT), scanning transmission electron microscopy in a focused ion beam equipped scanning electron microscope (STEM-on-FIB), and electrical resistivity measurements provided complementary information on the precipitation status and were correlated with each other. It was demonstrated that accurate electrical resistivity measurements of the bulk steel could monitor the general consumption of solute microalloys (Nb) during hot working and were further complemented by APT measurements of the steel matrix. Precipitates that had formed during cooling or isothermal holding could be distinguished from strain-induced precipitates by corroborating STEM measurements with APT results, because APT specifically allowed obtaining detailed information about the chemical composition of precipitates as well as the elemental distribution. The current paper highlights the complementarity of these methods and shows first results within the framework of a larger study on strain-induced precipitation.

The quantification of microstructures is crucial for the development of new materials as well as for the quality assurance. The progress in material science is accompanied by more and more complex and fine microstructures. With this, the quantification of these microstructures is becoming increasingly challenging. One way to get more information from microstructures is by combining images made with different imaging techniques, like scanning electron microscopy and light optical microscopy. Despite a wide variety of commercial software, these different images can still not be combined without a considerable proportion of manual work. In this work, we present a workflow to combine micrographs of different imaging sources by means of image registration with the open source tool FiJi and describe how to extract and analyse single grains concerning their substructure with Matlab© on the example of a martensitic dual phase steel.

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