Fracture and Fatigue of Materials Based on Machine Learning
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Physics".
Deadline for manuscript submissions: 10 October 2025 | Viewed by 26
Special Issue Editors
Interests: fatigue; finite element analysis; artificial intelligence; materials behavior
Interests: fatigue; railway; materials behavior; structural integrity
Special Issue Information
Dear Colleagues,
As artificial intelligence tools become more prevalent in all scientific areas, researchers must learn how to use and integrate them in their workflows. Material fatigue and fracture behavior analyses have also been the focus of applications of both machine learning and deep learning tools, allowing for enhanced design practices and the design of safer components. Therefore, we invite you to submit your original research or review papers regarding the use of artificial intelligence tools in material fatigue and fracture analysis, component design, failure analysis, structural integrity, etc. Both experimental and numerical papers are welcome, as long as artificial intelligence tools have been integrated in the workflow. Physics-informed neural network developments and other theoretical subjects are also welcome, as they are one of the most promising topics under development currently. The main areas of interest of this Special Issue include the following:
- Fatigue life analysis under uniaxial or multiaxial loading, considering the applied stress ratio and loading path.
- Fatigue crack growth rate analysis under pure mode I or mixed-mode conditions, considering the effect of the applied stress ratio and frequency.
- Fatigue crack propagation under mixed-mode conditions, considering the prediction of the crack path.
- Plasticity-induced crack closure analysis, under constant or variable amplitude and overloads.
- Displacement, strain and stress fields around the crack tip, for both pure mode I and mixed-mode conditions.
- Crack identification under static or cyclic loads.
- Failure analysis problems and structural integrity analysis.
- Physics-informed neural networks models for fatigue and fracture analysis.
Prof. Dr. Ricardo Miguel Gomes Simões Baptista
Prof. Dr. Virgínia Isabel Monteiro Nabais Infante
Dr. Paulo Jorge Pires Moita
Guest Editors
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Keywords
- fatigue
- crack growth
- crack identification
- crack closure
- mixed-mode fatigue
- multiaxial fatigue
- finite element analysis
- artificial neural networks
- deep learning
- physics-informed neural networks
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