Home Journals Articles Updates About
CN
image
收藏切换
Fatigue crack growth prediction based on IPSO-PF algorithm
收藏切换
PDF
Ting JIN1, Xiaolei WANG2, Yu LIU1, Jianming YUAN1
Journal of Mechanical Strength | 2025, 47(4) : 47 - 53
Less
收藏切换
Journal of Mechanical Strength | 2025, 47(4): 47-53
• ·Fatigue·Damage·Fracture·Failure Analysis· •
Fatigue crack growth prediction based on IPSO-PF algorithm
Full
Ting JIN1, Xiaolei WANG2, Yu LIU1, Jianming YUAN1
Affiliations
  • 1.Key Laboratory of Port Cargo Handling Technology Ministry of Communications, School of Transportation and Logistics, Wuhan University of Technology, Wuhan 430063, China
  • 2.Installation Engineering Co., Ltd., China Communications First Harbor Engineering, Tianjin 300457, China
Published: 2025-04-15 doi: 10.16579/j.issn.1001.9669.2025.04.006
Outline
收藏切换

The traditional Paris formula ignores the influence of various uncertain factors in the crack growth process,which leads to a big difference between the predicted crack growth process and the real crack growth process. In order to improve the prediction accuracy of fatigue crack growth, a fatigue crack growth prediction method based on the improved particle swarm optimization particle filtering (IPSO-PF) algorithm was proposed. Firstly, based on the framework of the particle filtering (PF) algorithm, the particle swarm optimization (PSO) algorithm was used to optimize some particles based on the updated observation information，keeping the state of particles with large weights unchanged, and particles with small weights tend to high likelihood region, and IPSO-PF algorithm was designed. Then，combining IPSO-PF algorithm with Paris formula, a fatigue crack growth prediction model based on Paris formula and IPSO-PF algorithm was constructed. Finally, the validity of the model was verified by using the open 2024-T351 aluminum alloy data set. The results show that compared with the traditional PF algorithm, IPSO-PF algorithm can improve the diversity of particles. The prediction error of the crack growth prediction model based on IPSO-PF algorithm is 2.6%, which is better than 9.2% based on PF algorithm.

Fatigue crack  /  Crack growth prediction  /  Particle filtering  /  Particle swarm optimization  /  Algorithm optimization
Ting JIN, Xiaolei WANG, Yu LIU, Jianming YUAN. Fatigue crack growth prediction based on IPSO-PF algorithm[J]. Journal of Mechanical Strength, 2025 , 47 (4) : 47 -53 . DOI: 10.16579/j.issn.1001.9669.2025.04.006
Funding
Less
  • National Key Research and Development Plan Project(2022YFB2602302)
Appendix
Less
Year 2025 volume 47 Issue 4
PDF
55
29
Cite this Article
BibTeX
Article Info
doi: 10.16579/j.issn.1001.9669.2025.04.006
  • Receive Date：2023-09-02
  • Online Date：2026-03-20
  • Published：2025-04-15
Article Data
Affiliations
History
  • Received：2023-09-02
  • Revised：2023-10-20
Funding
National Key Research and Development Plan Project(2022YFB2602302)
Affiliations
    1.Key Laboratory of Port Cargo Handling Technology Ministry of Communications, School of Transportation and Logistics, Wuhan University of Technology, Wuhan 430063, China
    2.Installation Engineering Co., Ltd., China Communications First Harbor Engineering, Tianjin 300457, China

Corresponding:

YUAN Jianming, E-mail：
References
Share
https://castjournals.cast.org.cn/joweb/jxqd/EN/10.16579/j.issn.1001.9669.2025.04.006
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House