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Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime

Can Wang (UGent) , Yifei Li (UGent) , Ngoc Hoa Tran, Dagang Wang, Samir Khatir (UGent) and Magd Abdel Wahab (UGent)
(2022) TRIBOLOGY INTERNATIONAL. 175.
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Abstract
The fretting problem occurs at two contact surfaces sustaining small relative displacement, and it reduces the fatigue lifetime dramatically. Estimating accurate fretting fatigue lifetime plays an important role in engineering applications. Due to the complicated stress state, and high-stress gradient in the contact surface, the average methods are necessary to obtain the precise lifetime, but the critical distance for the average zone is difficult to estimate. In this work, Artificial Neural Network (ANN) tool combined with damage parameters is proposed to determine the optimal critical distance for different fretting conditions. This tool can also be used to accurately predict the crack initiation lifetime. The fretting fatigue lifetimes calculated by using this approach have shown good agreement with experimental results from literatures. In addition, rough estimates of critical distance for different cases are made based on the numerical results.
Keywords
Surfaces, Coatings and Films, Surfaces and Interfaces, Mechanical Engineering, Mechanics of Materials, Fretting fatigue, Artificial neural network, Numerical analysis, critical distance, damage parameter, CRITICAL PLANE APPROACH, PROPAGATION LIFETIME, MESHFREE METHOD, NUCLEATION, MECHANICS

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MLA
Wang, Can, et al. “Artificial Neural Network Combined with Damage Parameters to Predict Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL, vol. 175, 2022, doi:10.1016/j.triboint.2022.107854.
APA
Wang, C., Li, Y., Tran, N. H., Wang, D., Khatir, S., & Abdel Wahab, M. (2022). Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime. TRIBOLOGY INTERNATIONAL, 175. https://doi.org/10.1016/j.triboint.2022.107854
Chicago author-date
Wang, Can, Yifei Li, Ngoc Hoa Tran, Dagang Wang, Samir Khatir, and Magd Abdel Wahab. 2022. “Artificial Neural Network Combined with Damage Parameters to Predict Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL 175. https://doi.org/10.1016/j.triboint.2022.107854.
Chicago author-date (all authors)
Wang, Can, Yifei Li, Ngoc Hoa Tran, Dagang Wang, Samir Khatir, and Magd Abdel Wahab. 2022. “Artificial Neural Network Combined with Damage Parameters to Predict Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL 175. doi:10.1016/j.triboint.2022.107854.
Vancouver
1.
Wang C, Li Y, Tran NH, Wang D, Khatir S, Abdel Wahab M. Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime. TRIBOLOGY INTERNATIONAL. 2022;175.
IEEE
[1]
C. Wang, Y. Li, N. H. Tran, D. Wang, S. Khatir, and M. Abdel Wahab, “Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime,” TRIBOLOGY INTERNATIONAL, vol. 175, 2022.
@article{8764644,
  abstract     = {{The fretting problem occurs at two contact surfaces sustaining small relative displacement, and it reduces the fatigue lifetime dramatically. Estimating accurate fretting fatigue lifetime plays an important role in engineering applications. Due to the complicated stress state, and high-stress gradient in the contact surface, the average methods are necessary to obtain the precise lifetime, but the critical distance for the average zone is difficult to estimate. In this work, Artificial Neural Network (ANN) tool combined with damage parameters is proposed to determine the optimal critical distance for different fretting conditions. This tool can also be used to accurately predict the crack initiation lifetime. The fretting fatigue lifetimes calculated by using this approach have shown good agreement with experimental results from literatures. In addition, rough estimates of critical distance for different cases are made based on the numerical results.}},
  articleno    = {{107854}},
  author       = {{Wang, Can and Li, Yifei and Tran, Ngoc Hoa and Wang, Dagang and Khatir, Samir and Abdel Wahab, Magd}},
  issn         = {{0301-679X}},
  journal      = {{TRIBOLOGY INTERNATIONAL}},
  keywords     = {{Surfaces,Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials,Fretting fatigue,Artificial neural network,Numerical analysis,critical distance,damage parameter,CRITICAL PLANE APPROACH,PROPAGATION LIFETIME,MESHFREE METHOD,NUCLEATION,MECHANICS}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{Artificial neural network combined with damage parameters to predict fretting fatigue crack initiation lifetime}},
  url          = {{http://doi.org/10.1016/j.triboint.2022.107854}},
  volume       = {{175}},
  year         = {{2022}},
}
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