| 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 | |
Jia, Yinfeng; Fu, Rui; Ling, Chao; Shen, Zheng; Zheng, Liang; Zhong, Zheng; Hong YS(洪友士)
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| Source Publication | INTERNATIONAL JOURNAL OF FATIGUE
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| 2023-06 | |
| Volume | 172Pages:107645 |
| ISSN | 0142-1123 |
| Abstract | Microstructural defects and inhomogeneity of titanium alloys fabricated by laser powder bed fusion (LPBF) make their fatigue behaviors much more complicated than the conventionally made ones, especially in very-high-cycle fatigue (VHCF) regime. Most of traditional models/formulae and currently-used machine learning algorithms mainly concern fatigue behavior of LPBF-fabricated titanium alloys in high-cycle fatigue (HCF) regime, but rarely in VHCF regime. In this paper, a deep belief neural network-back propagation (DBN-BP) model was proposed to predict the fatigue life of LPBF-fabricated Ti-6Al-4V up to VHCF regime. Results obtained in this study indicate that the DBN-BP model exhibits high precision and strong stability in predicting the fatigue life of LPBFfabricated Ti-6Al-4V in both HCF and VHCF regimes. The primary hyperparameters of the DBN-BP model were optimized to further improve the prediction precision of this innovative model. Finally, the optimal DBN-BP model was applied to predict the relation between mean stress and stress amplitude, and the effect of energy density on the fatigue behavior of LPBF-fabricated Ti-6Al-4V up to VHCF regime. |
| Keyword | Fatigue life prediction Deep learning method Laser powder bed fusion Ti-6Al-4V Very -high -cycle fatigue |
| DOI | 10.1016/j.ijfatigue.2023.107645 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000962190800001 |
| Funding Organization | Guangdong basic and applied basic research foundation [2019A1515110758] ; Shenzhen munic- ipal science and technology innovation council [ZDSYS20210616110000001] ; Hunan provincial leading talents pro- gram in science and technology innovations [2021RC4051] ; National Natural Science Foundation of China [11932020] |
| Classification | 一类 |
| Ranking | 3+ |
| Contributor | Zheng, L ; Zhong, Z |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92231 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.(Jia Yinfeng, Fu Rui, Ling Chao, Zheng Liang, Zhong Zheng) Harbin Inst Technol Shenzhen Sch Sci Shenzhen Peoples R China 2.(Shen Zheng) CRRC Zhuzhou Elect Co Ltd R&D Ctr Zhuzhou Hunan Peoples R China 3.(Hong Youshi) Chinese Acad Sci Inst Mech LNM Beijing Peoples R China |
| Recommended Citation GB/T 7714 | Jia, Yinfeng,Fu, Rui,Ling, Chao,et al. 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[J]. INTERNATIONAL JOURNAL OF FATIGUE,2023,172:107645.Rp_Au:Zheng, L, Zhong, Z |
| APA | Jia, Yinfeng.,Fu, Rui.,Ling, Chao.,Shen, Zheng.,Zheng, Liang.,...&洪友士.(2023).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.INTERNATIONAL JOURNAL OF FATIGUE,172,107645. |
| MLA | Jia, Yinfeng,et al."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".INTERNATIONAL JOURNAL OF FATIGUE 172(2023):107645. |
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| Jp2023Fa126.pdf（7230KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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