Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Hyungson Ki"'
Autor:
Myeonggyun Son, Hyungson Ki
Publikováno v:
Journal of Materials Research and Technology, Vol 27, Iss , Pp 718-732 (2023)
Laser heat treatment of carbon steel is generally performed to increase the hardness of the specimen. However, when the heating temperature is high, softening of the specimen can occur along with melting. It is important to predict both hardening and
Externí odkaz:
https://doaj.org/article/e536b747bf58411986e1667ab9147d4d
Publikováno v:
Materials & Design, Vol 226, Iss , Pp 111639- (2023)
In this study, two deep-learning models are presented to predict the crystalline phases of femtosecond laser-processed silicon. To obtain the datasets, single-crystal silicon was processed by a femtosecond laser using 49 different combinations of las
Externí odkaz:
https://doaj.org/article/1ae9ded5c6d14d8eb05d453009530d47
Publikováno v:
IEEE Access, Vol 9, Pp 79316-79325 (2021)
Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets and predict the cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) and three
Externí odkaz:
https://doaj.org/article/019a2475f119412392bc4082ac25416d
Autor:
Sehyeok Oh, Hyungson Ki
Publikováno v:
IEEE Access, Vol 8, Pp 73359-73372 (2020)
A deep learning model was applied for predicting a cross-sectional bead image from laser welding process parameters. The proposed model consists of two successive generators. The first generator produces a weld bead segmentation map from laser intens
Externí odkaz:
https://doaj.org/article/86d0be88a87e4b7589a899cc52f80556
Publikováno v:
IEEE Access, Vol 8, Pp 116254-116267 (2020)
Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including
Externí odkaz:
https://doaj.org/article/2db7ec7f373a410d8ee9677599c3f903
Publikováno v:
Journal of Manufacturing Processes. 84:1274-1283
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fed3717520f40061c52fbc74ac357368
https://doi.org/10.1016/j.jmapro.2022.11.004
https://doi.org/10.1016/j.jmapro.2022.11.004
Publikováno v:
Journal of Manufacturing Processes. 80:75-86
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e0c96017659c2919250c3f4e85ac7f2
https://doi.org/10.1016/j.jmapro.2022.05.044
https://doi.org/10.1016/j.jmapro.2022.05.044
Publikováno v:
Journal of Manufacturing Processes. 68:1018-1030
In laser keyhole welding, the keyhole exhibits inherently unstable behavior, and the laser beam absorptance inside a keyhole varies rapidly. In this study, a real-time full-penetration laser keyhole welding monitoring system was established using a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dab8fedde58df70b037a226b07244607
https://doi.org/10.1016/j.jmapro.2021.06.029
https://doi.org/10.1016/j.jmapro.2021.06.029
Publikováno v:
IEEE Access, Vol 9, Pp 79316-79325 (2021)
Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets and predict the cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) and three
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc2e5650839ae12bcfa24763c446f9d4
https://ieeexplore.ieee.org/document/9442714/
https://ieeexplore.ieee.org/document/9442714/