It has been proven that through targeted quenching and partitioning (Q & P), medium manganese steels can exhibit excellent mechanical properties combining very high strength and ductility. In order to apply the potential of these steels in industrial press hardening and to avoid high scrap rates, it is of utmost importance to determine a robust process window for Q & P. Hence, an intensive study of dilatometry experiments was carried out to identify the influence of quenching temperature (TQ) and partitioning time (tp) on phase transformations, phase stabilities, and the mechanical properties of a lean medium manganese steel. For this purpose, additional scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and energy dispersive X-ray spectroscopy (EDX) examinations as well as tensile testing were performed. Based on the dilatometry data, an adjustment of the Koistinen–Marburger (K-M) equation for medium manganese steel was developed. The results show that a retained austenite content of 12–21% in combination with a low-phase fraction of untempered secondary martensite (max. 20%) leads to excellent mechanical properties with a tensile strength higher than 1500 MPa and a total elongation of 18%, whereas an exceeding secondary martensite phase fraction results in brittle failure. The optimum retained austenite content was adjusted for TQ between 130 °C and 150 °C by means of an adapted partitioning.
The bake hardening treatment shows great potential for increasing the yield strength of steel components for automotive applications. This study investigates the effects of bake hardening on the yield strength and ductility of an austenitic high‐Mn steel. In order to identify a promising process window, the prestrain, the bake hardening temperature, and the annealing time are varied. The bake hardening effect is evaluated by the uniaxial tensile tests with digital image correlation (DIC) in situ monitoring. The results show strong bake hardening effect on the high‐Mn steel when certain amount of prestrain is applied. Large amounts of prestrain even leads to room temperature aging. Small angle neutron scattering (SANS) measurements indicate the absence of Mn–C short range ordering (SRO) after the prestrain; however, the nano‐sized Mn–C SRO re‐occurs after the annealing. At high prestrain degree, an increase in the number density of the Mn–C SRO is found in both cases, after annealing at elevated temperature and aging at room temperature, indicating an accelerated Mn–C SRO formation. The results suggest that SRO is responsible for an increase in the yield strength and a pronounced yielding of the high‐Mn steel after bake hardening treatment.
Studying steel microstructures yields important insights regarding its mechanical characteristics. Within steel, microstructures transform based on a multitude of factors including chemical composition, transformation temperatures, and cooling rates. Martensite-austenite (MA) islands in bainitic steel appear as blocky structures with abstract shapes that are difficult to identify and differentiate from other types of microstructures. In this regard, material science may benefit from machine learning models that are able to automatically and accurately detect these structures. However, the training process of the state-of-the-art machine learning models requires a large amount of high-quality data. In this dataset, we provide 1.705 scanning electron microscopy images along with a set of 8.909 expert-annotated polygons to describe the geometry of the MA islands that appear on the images. We envision that this dataset will be useful for material scientists to explore the relationship between the morphology of bainitic steel and mechanical characteristics. Moreover, computer vision researchers and practitioners may use this data for training state-of-the-art object segmentation models for abstract geometries such as MA islands.
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