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
文献类型：期刊论文
| 作者 | Jia, Yinfeng; Fu, Rui; Ling, Chao; Shen, Zheng; Zheng, Liang; Zhong, Zheng; Hong YS(洪友士)
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| 刊名 | INTERNATIONAL JOURNAL OF FATIGUE
 |
| 出版日期 | 2023-06 |
| 卷号 | 172页码:107645 |
| ISSN号 | 0142-1123 |
| 关键词 | Fatigue life prediction Deep learning method Laser powder bed fusion Ti-6Al-4V Very -high -cycle fatigue |
| DOI | 10.1016/j.ijfatigue.2023.107645 |
| 英文摘要 | 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. |
| 分类号 | 一类 |
| WOS研究方向 | WOS:000962190800001 |
| 语种 | 英语 |
| 资助机构 | 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] |
| 其他责任者 | Zheng, L ; Zhong, Z |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/92231]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.(Jia Yinfeng, Fu Rui, Ling Chao, Zheng Liang, Zhong Zheng) Harbin Inst Technol Shenzhen Sch Sci Shenzhen Peoples R China 2.(Hong Youshi) Chinese Acad Sci Inst Mech LNM Beijing Peoples R China 3.(Shen Zheng) CRRC Zhuzhou Elect Co Ltd R&D Ctr Zhuzhou Hunan Peoples R China |
| 推荐引用方式 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. |
| 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. |
入库方式： OAI收割
来源：力学研究所
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