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Fatigue modeling using neural networks: a comprehensive review
  • Jie Chen,
  • Yongming Liu
Jie Chen
Arizona State University School for Engineering of Matter Transport and Energy

Corresponding Author:[email protected]

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Yongming Liu
Arizona State University School for Engineering of Matter Transport and Energy
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Abstract

Neural network (NN) models have made a significant impact on fatigue-related engineering communities and are expected to increase rapidly soon due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.
11 Sep 2021Submitted to Fatigue & Fracture of Engineering Materials & Structures
11 Sep 2021Submission Checks Completed
11 Sep 2021Assigned to Editor
11 Sep 2021Reviewer(s) Assigned
05 Oct 2021Review(s) Completed, Editorial Evaluation Pending
06 Oct 2021Editorial Decision: Revise Major
30 Oct 20211st Revision Received
01 Nov 2021Submission Checks Completed
01 Nov 2021Assigned to Editor
01 Nov 2021Reviewer(s) Assigned
01 Dec 2021Review(s) Completed, Editorial Evaluation Pending
03 Dec 2021Editorial Decision: Revise Major
10 Dec 20212nd Revision Received
10 Dec 2021Submission Checks Completed
10 Dec 2021Assigned to Editor
10 Dec 2021Reviewer(s) Assigned
15 Dec 2021Review(s) Completed, Editorial Evaluation Pending
19 Dec 2021Editorial Decision: Accept
07 Jan 2022Published in Fatigue & Fracture of Engineering Materials & Structures. 10.1111/ffe.13640