Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Nam Hoon Goo"'
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
Abstract Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established theoretical and empirical models. A data-driven approach
Externí odkaz:
https://doaj.org/article/4f8b5e5a22314369822c383ea96bedd5
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
Abstract Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the i
Externí odkaz:
https://doaj.org/article/97f45af18b5640c5a76a5470b7e82b31
Publikováno v:
Engineering Reports, Vol 3, Iss 1, Pp n/a-n/a (2021)
Abstract 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 o
Externí odkaz:
https://doaj.org/article/b03d9f1852c84bbdb928f52ee4dd5c3d
Autor:
Jean-Yves Maetz, Matthias Militzer, Yu Wen Chen, Jer-Ren Yang, Nam Hoon Goo, Soo Jin Kim, Bian Jian, Hardy Mohrbacher
Publikováno v:
Metals, Vol 8, Iss 10, p 758 (2018)
Nb–Mo low-alloyed steels are promising advanced high strength steels (AHSS) because of the highly dislocated bainitic ferrite microstructure conferring an excellent combination of strength and toughness. In this study, the potential of precipitatio
Externí odkaz:
https://doaj.org/article/0f121669ee8043a9a8f2f727fe4637a9
Publikováno v:
Korean Journal of Metals and Materials. 58:822-829
To automatically reconstruct the prior austenite grains from as-quenched martensitic structure, we applied a deep learning algorithm to recognize the prior austenite grains boundaries hidden in the martensitic matrix. The FC-DenseNet architecture bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cafb81715bed90f61f8946dacc83dc95
https://doi.org/10.3365/kjmm.2020.58.12.822
https://doi.org/10.3365/kjmm.2020.58.12.822
Autor:
Seung-Hyun Hong, Jae Hyeok Shim, Young Kook Lee, Minwoo Kang, Nam Hoon Goo, Kyung Jong Lee, Jeong Min Kim
Publikováno v:
Metallurgical and Materials Transactions A. 51:4422-4426
A simple mathematical model for establishing isothermal transformation kinetics from continuous cooling transformation data is presented. A new regression function of k, which is a reaction parameter of the Johnson–Mehl–Avrami equation, is propos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cee9060d007643e3913a8c024dfb177
https://doi.org/10.1007/s11661-020-05910-1
https://doi.org/10.1007/s11661-020-05910-1
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020)
Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a5c3787a3243544270cc8b185454d3d
https://doi.org/10.1038/s41598-020-77575-0
https://doi.org/10.1038/s41598-020-77575-0
Publikováno v:
Engineering Reports, Vol 3, Iss 1, Pp n/a-n/a (2021)
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 wer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58e28fa6e89ce3b4de2f271a0b7267f9
https://doi.org/10.1002/eng2.12274
https://doi.org/10.1002/eng2.12274
Autor:
Jeong Min Kim, Kyung Jong Lee, Seung-Hyun Hong, Minwoo Kang, Nam Hoon Goo, Jae Hyeok Shim, Young Kook Lee
Publikováno v:
SSRN Electronic Journal.
A simple mathematical model for establishing isothermal transformation kinetics from continuous cooling transformation data is presented. A new regression function of k, which is a reaction parameter of the Johnson-Mehl-Avrami equation is proposed. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e750622aa84e71622be0f1c2f5dd666
https://doi.org/10.2139/ssrn.3538807
https://doi.org/10.2139/ssrn.3538807