Version 1
: Received: 4 June 2020 / Approved: 5 June 2020 / Online: 5 June 2020 (14:37:20 CEST)
How to cite:
Pütz, F.; Henrich, M.; Fehlemann, N.; Roth, A.; Münstermann, S. Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks. Preprints2020, 2020060056. https://doi.org/10.20944/preprints202006.0056.v1
Pütz, F.; Henrich, M.; Fehlemann, N.; Roth, A.; Münstermann, S. Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks. Preprints 2020, 2020060056. https://doi.org/10.20944/preprints202006.0056.v1
Pütz, F.; Henrich, M.; Fehlemann, N.; Roth, A.; Münstermann, S. Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks. Preprints2020, 2020060056. https://doi.org/10.20944/preprints202006.0056.v1
APA Style
Pütz, F., Henrich, M., Fehlemann, N., Roth, A., & Münstermann, S. (2020). Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks. Preprints. https://doi.org/10.20944/preprints202006.0056.v1
Chicago/Turabian Style
Pütz, F., Andreas Roth and Sebastian Münstermann. 2020 "Generating Input Data for Microstructure Modelling: A Deep Learning Approach Using Generative Adversarial Networks" Preprints. https://doi.org/10.20944/preprints202006.0056.v1
Abstract
For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio and slope of the grain relative to the rolling direction are applied. However, usually simple distribution functions like log normal or gamma distribution are used. Yet, these do not take the interdependencies between the microstructural parameters into account. To fully describe any metallic microstructure though, these interdependencies between the singular parameters need to be accounted for. To accomplish this representation, a machine learning approach was applied in this study. By implementing a Wasserstein generative adversarial network, the distribution, as well as the interdependencies could accurately be described. A validation scheme was applied to verify the excellent match between microstructure input data and synthetically generated output data.
Keywords
Microstructure Modelling; Representative Volume Elements; DP-steel; Machine Learning; Deep Learning; Wasserstein GAN
Subject
Chemistry and Materials Science, Metals, Alloys and Metallurgy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
