Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Seung-Hyeok Shin"'
Publikováno v:
Archives of Metallurgy and Materials, Vol vol. 69, Iss No 2, Pp 415-419 (2024)
This study describes how microstructural constituents affected the hydrogen embrittlement resistance of high-strength pipeline steels. The American Petroleum Institute (API) X60, X70, and X80 pipeline steels demonstrated complicated microstructure co
Externí odkaz:
https://doaj.org/article/2827280bc8a943caa847cccfa404b4c5
Publikováno v:
Journal of Materials Research and Technology, Vol 26, Iss , Pp 3558-3570 (2023)
In this study, a novel multi-step heat treatment method, including intercritical annealing and tempering, is proposed to improve the low-temperature toughness of Fe-6.5Mn-0.08C medium-manganese (Mn) steel. The effects of the subsequent tempering trea
Externí odkaz:
https://doaj.org/article/ba7df6ff9b9b4bd099055f224c4e300a
Publikováno v:
Archives of Metallurgy and Materials, Vol vol. 67, Iss No 4, Pp 1487-1490 (2022)
In this study, the effect of the coiling temperature on the tensile properties of API X70 linepipe steel plates is investigated in terms of the microstructure and related anisotropy. Two coiling temperatures are selected to control the microstructure
Externí odkaz:
https://doaj.org/article/a69e2ea4e1e6495a9c02750928d88911
Publikováno v:
Archives of Metallurgy and Materials, Vol vol. 67, Iss No 4, Pp 1497-1501 (2022)
In-situ observation of the transformation behavior of acicular ferrite in high-strength low-alloy steel using confocal laser scanning microscopy was discussed in terms of nucleation and growth. It is found that acicular ferrite nucleated at dislocati
Externí odkaz:
https://doaj.org/article/f2bb443c4f8043e798f874c601c38daa
Publikováno v:
Journal of Materials Research and Technology, Vol 19, Iss , Pp 2794-2798 (2022)
This study introduces a machine learning approach to predict the effect of alloying elements and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic steels for the first time. The correlation between input features and
Externí odkaz:
https://doaj.org/article/6a9969fd5b2a4111ba7bd1e23f2ae7f4
Publikováno v:
Metals, Vol 13, Iss 12, p 1912 (2023)
In this study, the effect of subsequent heat treatment applied to high-strength low-alloy steel (HSLA) on the structure–property relationships was investigated. Tempering and intercritical annealing processes are introduced to elucidate the influen
Externí odkaz:
https://doaj.org/article/d230806c362e4b8598a1fdb4fa67082d
Publikováno v:
Archives of Metallurgy and Materials, Vol vol. 66, Iss No 3, Pp 719-723 (2021)
An artificial neural network (ANN) model was developed to predict the tensile properties of dual-phase steels in terms of alloying elements and microstructural factors. The developed ANN model was confirmed to be more reasonable than the multiple lin
Externí odkaz:
https://doaj.org/article/ba3c794b98d242c0be145c10e912cfb3
Autor:
Hojong Choi, Seung-Hyeok Shin
Publikováno v:
Sensors, Vol 22, Iss 24, p 9709 (2022)
Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used i
Externí odkaz:
https://doaj.org/article/3391e8600627429ea654c542fba305c8
Publikováno v:
Computer Assisted Surgery, Vol 24, Iss 0, Pp 73-78 (2019)
Purpose: Encryption of patient information has become an important issue in medical ultrasound instrumentation to secure information when images are accessed off-site. The proposed algorithm is used to encrypt private medical images and transfer the
Externí odkaz:
https://doaj.org/article/88612df5231149e2878d47e263dd32d6
Publikováno v:
Metals, Vol 11, Iss 8, p 1314 (2021)
An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bain
Externí odkaz:
https://doaj.org/article/2f3bfc472ec248afb60768950b256405