Bainite viewed three different ways

Microstructure Formation and Carbon Partitioning with Austenite Decomposition during Isothermal Heating Process in Fe-Si-Mn-C Steel Monitored by In Situ Time-of-Flight Neutron Diffraction

Retained austenite is a key feature used to realize the transformation-induced plasticity in bainitic high strength steels. In this study, the authors focused on the formation of metastable austenite in Fe-0.61C-1.9Si-0.98Mn (mass%) during isothermal heating processes using in situ neutron diffraction techniques. Quantitative discussion of carbon partitioning processes is enabled by applying an in situ phase fraction analysis considering crystallographic textures, in addition to the carbon concentration estimation based on the lattice parameter of austenite. The carbon partitioning behavior is inhomogeneous, resulting in a bimodal carbon concentration distribution in austenite. The carbon enriched, high carbon austenite is stable during isothermal heating at 673 K and is retained even after cooling to room temperature. The remainder is low carbon austenite, which is gradually consumed by bainite transformation. Above 723 K, the high carbon austenite also decomposes to ferrite and cementite due to the fast diffusion of Si. Conversely, below 623 K, cementite is stabilized even without the diffusion of Si. These cementite formation mechanisms prevent the formation and retention of high carbon austenite. The inhomogeneous carbon distribution and cementite formation must be carefully considered to precisely predict the microstructure formation in Si-added bainitic steels.

Aachen-Heerlen annotated steel microstructure dataset

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.