A 3D dendrite microstructure database of a Ni-base SX based on relational geometric ontology (RGO)

Material: Nickel-base superalloy ERBO/1 (more details: Parsa, A. B., et al. Advanced scale bridging microstructure analysis of single crystal Ni-base superalloys. Adv. Eng. Mater. 2015, 17 (2), 216-230, https://doi.org/10.1002/adem.201400136)

Casting: Bridgman seed technique; Withdrawal rate: 180 mm/h, Thermal gradient 13.3 K/mm (more details: Hallensleben, P., et al. On the evolution of cast microstructures during processing of single crystal Ni-base superalloys using a Bridgman seed technique, Mat. Des. 2017, 128, 98–111, https://doi.org/10.1016/j.matdes.2017.05.001)

Sample: Cross sectional slices extracted perpendicular to the growth direction of a single crystal superalloy cylinder (diameter 12mm, length 120 mm).

Preperation: Each slice was individually mounted, grinded, polished and etched 6 seconds with an etching solution consisting of 100ml H2O, 100ml HCl, 100ml HNO3 and 3g MoO3. 

Image acquisition: Optical microscope of type Axio (Carl Zeiss GmbH) equipped with a high-resolution CCD-camera of type Leica DFC320 and stepper-motor driven sample stage of type Tango Desktop (Märzhäuser)

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The published data is a compilation of 20 serial sectioned optical micrographs resolving the dendritic microstructure of the sample described above. They show a central region of the specimen at different heights of the cylindrical sample. The names of those micrographs correspond to the z-coordinate in millimeters, i.e. micrograph "012.tif" was extracted at 12mm. For each micrograph, an object detector based on a neural network was used to identify the dendrite core positions. Afterwards, registration algorithms were used to determine the growth directions together with branching and extinction events of all dendrites. Neighboring dendrites were identified by calculating a triangulation for each micrograph.

This quantitative data was transformed into a microstructure database stored as a .JSON file using the "Relational Geometric Ontology" approach described in A.R. Richter, F. Scholz, G. Eggeler, J. Frenzel, P. Thome, Microstructure informatics: Using computer vision for the characterization of dendrite growth phenomena in Ni-base single crystal Superalloys, Materials Characterization, Volume 223, 2025, 114878, https://doi.org/10.1016/j.matchar.2025.114878.