N. N. Soboleva

ANALYSIS OF METAL MICROSTRUCTURE BY NEURAL NETWORKS, EXEMPLIFIED BY SEGMENTATION OF CARBIDES IN COMPOSITE COATINGS

The use of artificial neural networks in metal science to solve image analysis problems, in particular segmentation or classification of metal microstructures, includes 6 main stages: problem definition, dataset collection, model selection, model training, model evaluation, and integration with an existing workflow. The paper discusses these stages in detail, provides an example of their implementation for semantic segmentation of microstructures of composite coatings containing coarse primary carbides. The separation of carbides by a neural network makes it possible to automate the process of determining their volume fraction in the coating structure.

Acknowledgments: The work was performed under the state assignment for the IES UB RAS, theme No. 124020600045-0, and the IMP UB RAS, theme No. 121102900049-1 “Additivity”. The equip-ment of the Plastometriya shared research facilities center of the IES UB RAS was used to obtain the microscopic images.

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