A pattern recognition artificial neural network method for random fatigue loading life prediction
- Submitting institution
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Oxford Brookes University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 185745707
- Type
- D - Journal article
- DOI
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10.1016/j.ijfatigue.2017.02.003
- Title of journal
- International Journal of Fatigue
- Article number
- -
- First page
- 55
- Volume
- 99
- Issue
- 1
- ISSN
- 0142-1123
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper lies in the alternative fast and better agreement with experimental results it provides for predicting fatigue life under practical fluctuating load conditions. The model is generalised, covering all feasible metal alloys, fatigue loading, and component conditions. This was achieved by developing an artificial neural networks model that takes the best features of the frequency and time domain methods for life prediction. It has the potential to be adopted as a module for FE-Fatigue analyses in packages such as SolidWorks, ANSYS and ABAQUS.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -