Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

Abstract The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to captu...

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Autores principales:	Jaimyun Jung, Juwon Na, Hyung Keun Park, Jeong Min Park, Gyuwon Kim, Seungchul Lee, Hyoung Seop Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/6bbe639d1e2f45d6a9cfc4f5f009486b
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spelling	oai:doaj.org-article:6bbe639d1e2f45d6a9cfc4f5f009486b2021-12-02T16:06:05ZSuper-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis10.1038/s41524-021-00568-82057-3960https://doaj.org/article/6bbe639d1e2f45d6a9cfc4f5f009486b2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00568-8https://doaj.org/toc/2057-3960Abstract The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.Jaimyun JungJuwon NaHyung Keun ParkJeong Min ParkGyuwon KimSeungchul LeeHyoung Seop KimNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park Gyuwon Kim Seungchul Lee Hyoung Seop Kim Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
description	Abstract The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.
format	article
author	Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park Gyuwon Kim Seungchul Lee Hyoung Seop Kim
author_facet	Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park Gyuwon Kim Seungchul Lee Hyoung Seop Kim
author_sort	Jaimyun Jung
title	Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
title_short	Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
title_full	Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
title_fullStr	Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
title_full_unstemmed	Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
title_sort	super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6bbe639d1e2f45d6a9cfc4f5f009486b
work_keys_str_mv	AT jaimyunjung superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT juwonna superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT hyungkeunpark superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT jeongminpark superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT gyuwonkim superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT seungchullee superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis AT hyoungseopkim superresolvingmaterialmicrostructureimageviadeeplearningformicrostructurecharacterizationandmechanicalbehavioranalysis
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Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

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