Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645))

Yinfeng Jia; Rui Fu; Chao Ling; Zheng Shen; Liang Zheng; Zheng Zhong; Youshi Hong

doi:10.1016/j.ijfatigue.2023.107861

Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645))

Yinfeng Jia
, Rui Fu
, Chao Ling
, Zheng Shen
, Liang Zheng^*
, Zheng Zhong
, Youshi Hong

^*Corresponding author for this work

Harbin Institute of Technology
CRRC Corporation Limited
CAS - Institute of Mechanics

Research output: Contribution to journal › Comment/debate

Abstract

The authors regret reference [2] was not correct in the published article, please correct reference [2] to Horňas J, Běhal J, Homola P, Senck S, Holzleitner M, Godja N, Pásztor Z, Hegedüs B, Doubrava R, Růžek, R, Petrusová L. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. International Journal of Fatigue 2023; vol. 169. https://doi.org/10.1016/j.ijfatigue.2022.107483. The authors would like to apologise for any inconvenience caused.

Original language	English
Article number	107861
Journal	International Journal of Fatigue
Volume	176
DOIs	https://doi.org/10.1016/j.ijfatigue.2023.107861
State	Published - Nov 2023
Externally published	Yes

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Jia, Y., Fu, R., Ling, C., Shen, Z., Zheng, L., Zhong, Z., & Hong, Y. (2023). Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645)). International Journal of Fatigue, 176, Article 107861. https://doi.org/10.1016/j.ijfatigue.2023.107861

Jia, Yinfeng ; Fu, Rui ; Ling, Chao et al. / Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645)). In: International Journal of Fatigue. 2023 ; Vol. 176.

@article{2645ae780fa64698937c66548bcefaa9,

title = "Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645))",

abstract = "The authors regret reference [2] was not correct in the published article, please correct reference [2] to Hor{\v n}as J, B{\v e}hal J, Homola P, Senck S, Holzleitner M, Godja N, P{\'a}sztor Z, Heged{\"u}s B, Doubrava R, Rů{\v z}ek, R, Petrusov{\'a} L. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. International Journal of Fatigue 2023; vol. 169. https://doi.org/10.1016/j.ijfatigue.2022.107483. The authors would like to apologise for any inconvenience caused.",

author = "Yinfeng Jia and Rui Fu and Chao Ling and Zheng Shen and Liang Zheng and Zheng Zhong and Youshi Hong",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2023",

month = nov,

doi = "10.1016/j.ijfatigue.2023.107861",

language = "英语",

volume = "176",

journal = "International Journal of Fatigue",

issn = "0142-1123",

publisher = "Elsevier Ltd",

}

Jia, Y, Fu, R, Ling, C, Shen, Z, Zheng, L , Zhong, Z & Hong, Y 2023, 'Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645))', International Journal of Fatigue, vol. 176, 107861. https://doi.org/10.1016/j.ijfatigue.2023.107861

Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645)). / Jia, Yinfeng; Fu, Rui; Ling, Chao et al.
In: International Journal of Fatigue, Vol. 176, 107861, 11.2023.

Research output: Contribution to journal › Comment/debate

TY - JOUR

T1 - Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645))

AU - Jia, Yinfeng

AU - Fu, Rui

AU - Ling, Chao

AU - Shen, Zheng

AU - Zheng, Liang

AU - Zhong, Zheng

AU - Hong, Youshi

PY - 2023/11

Y1 - 2023/11

N2 - The authors regret reference [2] was not correct in the published article, please correct reference [2] to Horňas J, Běhal J, Homola P, Senck S, Holzleitner M, Godja N, Pásztor Z, Hegedüs B, Doubrava R, Růžek, R, Petrusová L. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. International Journal of Fatigue 2023; vol. 169. https://doi.org/10.1016/j.ijfatigue.2022.107483. The authors would like to apologise for any inconvenience caused.

AB - The authors regret reference [2] was not correct in the published article, please correct reference [2] to Horňas J, Běhal J, Homola P, Senck S, Holzleitner M, Godja N, Pásztor Z, Hegedüs B, Doubrava R, Růžek, R, Petrusová L. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach. International Journal of Fatigue 2023; vol. 169. https://doi.org/10.1016/j.ijfatigue.2022.107483. The authors would like to apologise for any inconvenience caused.

UR - https://www.scopus.com/pages/publications/85170378646

U2 - 10.1016/j.ijfatigue.2023.107861

DO - 10.1016/j.ijfatigue.2023.107861

M3 - 评论/辩论

AN - SCOPUS:85170378646

SN - 0142-1123

VL - 176

JO - International Journal of Fatigue

JF - International Journal of Fatigue

M1 - 107861

ER -

Jia Y, Fu R, Ling C, Shen Z, Zheng L , Zhong Z et al. Corrigendum to “Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime” [Int. J. Fatigue 172 (2023) 107645] (International Journal of Fatigue (2023) 172, (S0142112323001469), (10.1016/j.ijfatigue.2023.107645)). International Journal of Fatigue. 2023 Nov;176:107861. doi: 10.1016/j.ijfatigue.2023.107861

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