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Fatigue life prediction in presence of mean stresses using domain knowledge-integrated ensemble of extreme learning machines
  • Lei Gan,
  • Hao Wu,
  • Zheng Zhong
Lei Gan
Harbin Institute of Technology Shenzhen

Corresponding Author:15575876640@163.com

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Hao Wu
Tongji University School of Aerospace Engineering and Applied Mechanics
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Zheng Zhong
Harbin Institute of Technology Shenzhen
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Abstract

An accurate and stable data-driven model is proposed in this work for fatigue life prediction in presence of mean stresses. In the model, multiple independent extreme learning machines are trained using different training data and neural network configurations, and are then combined equally in an ensemble to model the complex correlations between fatigue life, material properties and mechanical responses. Meanwhile, theoretical prediction, as a representation of domain knowledge, is integrated to optimize the data-driven processes of model training and prediction, diversifying the information source of fatigue life modeling. Extensive experimental results covering thirteen metallic materials and a wide range of mean stress levels are collected from the open literature for model training and evaluation. The results demonstrate that the proposed model can achieve high accuracy and good stability [simultaneously](javascript:;), even with a small training dataset, showing great applicability for fatigue life prediction under mean stress loading conditions.
12 May 2022Submitted to Fatigue & Fracture of Engineering Materials & Structures
12 May 2022Submission Checks Completed
12 May 2022Assigned to Editor
16 May 2022Reviewer(s) Assigned
09 Jun 2022Review(s) Completed, Editorial Evaluation Pending
11 Jun 2022Editorial Decision: Revise Minor
27 Jun 20221st Revision Received
27 Jun 2022Submission Checks Completed
27 Jun 2022Assigned to Editor
27 Jun 2022Reviewer(s) Assigned
02 Jul 2022Review(s) Completed, Editorial Evaluation Pending
03 Jul 2022Editorial Decision: Accept
14 Jul 2022Published in Fatigue & Fracture of Engineering Materials & Structures. 10.1111/ffe.13792