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An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime

Sutao Han (UGent) , Samir Khatir (UGent) , Can Wang (UGent) and Magd Abdel Wahab (UGent)
(2023) TRIBOLOGY INTERNATIONAL. 183.
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Abstract
Fretting fatigue is a common type of problem in the aviation and other engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue conditions. Fretting fatigue crack initiation lifetime is a crucial part of the total lifetime, and the currently dominant method for research on fatigue behavior is the combination of the theoretical and numerical models. With the advent of the era of data science, machine learning has been widely used to predict fatigue behavior, but there are no many applications in the field of fretting fatigue. This paper proposed an improved Artificial Neural Network (ANN) using Balancing Composite Motion Optimization (BCMO) for quick prediction of fretting fatigue crack initiation lifetime. Physical-mechanical reasoning parameters, axial stress amplitude, shear stress amplitude, half contact width, and half stick zone width are considered as input parameters, and fretting fatigue crack initiation lifetime is set as the output feature. The main aim of BCMO is to improve the robustness of the ANN based on the influential pa-rameters, namely bias and weight. The provided results using ANN-BCMO are robust compared to ANN and traditional techniques from the literature. The Matlab code of improved ANN using BCMO can be found at https ://github.com/Samir-Khatir/BCMO-ANN.git
Keywords
Surfaces, Coatings and Films, Surfaces and Interfaces, Mechanical Engineering, Mechanics of Materials, Crack initiation lifetime, Fretting fatigue, Artificial Neural Network, (ANN), Balancing Composite Motion Optimization&nbsp, (BCMO), PROPAGATION LIFETIME, MESHFREE METHOD, PARTICLES, MECHANICS, BLADE

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Citation

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MLA
Han, Sutao, et al. “An Improved Artificial Neural Network for the Direct Prediction of Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL, vol. 183, 2023, doi:10.1016/j.triboint.2023.108411.
APA
Han, S., Khatir, S., Wang, C., & Abdel Wahab, M. (2023). An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime. TRIBOLOGY INTERNATIONAL, 183. https://doi.org/10.1016/j.triboint.2023.108411
Chicago author-date
Han, Sutao, Samir Khatir, Can Wang, and Magd Abdel Wahab. 2023. “An Improved Artificial Neural Network for the Direct Prediction of Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL 183. https://doi.org/10.1016/j.triboint.2023.108411.
Chicago author-date (all authors)
Han, Sutao, Samir Khatir, Can Wang, and Magd Abdel Wahab. 2023. “An Improved Artificial Neural Network for the Direct Prediction of Fretting Fatigue Crack Initiation Lifetime.” TRIBOLOGY INTERNATIONAL 183. doi:10.1016/j.triboint.2023.108411.
Vancouver
1.
Han S, Khatir S, Wang C, Abdel Wahab M. An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime. TRIBOLOGY INTERNATIONAL. 2023;183.
IEEE
[1]
S. Han, S. Khatir, C. Wang, and M. Abdel Wahab, “An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime,” TRIBOLOGY INTERNATIONAL, vol. 183, 2023.
@article{01GVJ175PH8ETRV99KBBJ3TG6D,
  abstract     = {{Fretting fatigue is a common type of problem in the aviation and other engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue conditions. Fretting fatigue crack initiation lifetime is a crucial part of the total lifetime, and the currently dominant method for research on fatigue behavior is the combination of the theoretical and numerical models. With the advent of the era of data science, machine learning has been widely used to predict fatigue behavior, but there are no many applications in the field of fretting fatigue. This paper proposed an improved Artificial Neural Network (ANN) using Balancing Composite Motion Optimization (BCMO) for quick prediction of fretting fatigue crack initiation lifetime. Physical-mechanical reasoning parameters, axial stress amplitude, shear stress amplitude, half contact width, and half stick zone width are considered as input parameters, and fretting fatigue crack initiation lifetime is set as the output feature. The main aim of BCMO is to improve the robustness of the ANN based on the influential pa-rameters, namely bias and weight. The provided results using ANN-BCMO are robust compared to ANN and traditional techniques from the literature. The Matlab code of improved ANN using BCMO can be found at https ://github.com/Samir-Khatir/BCMO-ANN.git}},
  articleno    = {{108411}},
  author       = {{Han, Sutao and Khatir, Samir and Wang, Can and Abdel Wahab, Magd}},
  issn         = {{0301-679X}},
  journal      = {{TRIBOLOGY INTERNATIONAL}},
  keywords     = {{Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials,Crack initiation lifetime,Fretting fatigue,Artificial Neural Network,(ANN),Balancing Composite Motion Optimization&nbsp,(BCMO),PROPAGATION LIFETIME,MESHFREE METHOD,PARTICLES,MECHANICS,BLADE}},
  language     = {{eng}},
  pages        = {{10}},
  title        = {{An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime}},
  url          = {{http://doi.org/10.1016/j.triboint.2023.108411}},
  volume       = {{183}},
  year         = {{2023}},
}
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