期刊
ENGINEERING REPORTS
卷 3, 期 1, 页码 -出版社
WILEY
DOI: 10.1002/eng2.12274
关键词
cycle GAN; DCGAN; metallography; micrograph; microstructure; Pix2Pix; steel
类别
资金
- Creative Materials Discovery Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT, and Future Planning [2015M3D1A1069705, 2018R1C1B6006943, 2019H1D8A2106002, 2020R1I1A1A01071589]
- National Research Foundation of Korea [2020R1I1A1A01071589, 2018R1C1B6006943] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The prediction of macro-scale materials properties from microstructures is crucial for research, and generative adversarial network (GAN) techniques can help in building high-quality PMPR models.
The prediction of macro-scale materials properties from microstructures, and vice versa, should be a key part in modeling quantitative microstructure-physical property relationships. It would be helpful if the microstructural input and output were in the form of visual images rather than parameterized descriptors. However, only a typical supervised learning technique would be insufficient to build up a model with real-image-output. A generative adversarial network (GAN) is required to treat visual images as output for a promising PMPR model. Recently developed deep-learning-based GAN techniques such as a deep convolutional GAN (DCGAN), a cycle-consistent GAN (Cycle GAN), and a conditional GAN-based image to image translation (Pix2Pix) could be of great help via the creation of realistic microstructures. In this regard, we generated virtual micrographs for various types of steels using a DCGAN, a Cycle GAN, and a Pix2Pix and confirmed the generated micrographs are qualitatively indistinguishable from the ground truth.
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