Abstract
Obtaining a good statistical representation of material microstructures is crucial for establishing robust process–structure–property linkages and machine learning techniques can bridge this gap. One major difficulty in leveraging recent advances in deep learning for this purpose is the scarcity of good quality data with enough metadata. In machine learning, similarity metric learning using Siamese networks has been used to deal with sparse data. Inspired by this, the authors propose a Siamese architecture to learn microstructure representations. The authors show that analysis tasks such as the classification of microstructures can be done more efficiently in the learned representation space.
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MD Hecht, BL Decost, T Francis, YN Picard, EA Holm and BA Webler: Ultra High Carbon Steel Micrographs. https://hdl.handle.net/11256/940.
M.D. Hecht, B.L. DeCost, T. Francis, Y.N. Picard, E.A. Holm, and B.A. Webler: Ultra High Carbon Steel Micrographs. https://hdl.handle.net/11256/940, 2017.
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Sardeshmukh, A., Reddy, S., Gautham, B.P. et al. Microstructure representation learning using Siamese networks. MRS Communications 10, 613–619 (2020). https://doi.org/10.1557/mrc.2020.55
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DOI: https://doi.org/10.1557/mrc.2020.55