Fatigue Life Prediction and Reliability Enhancement Technology for Manufacturing Equipment
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 25 May 2026 | Viewed by 152
Special Issue Editors
Interests: time-varying reliability analysis; resilience design; design of experiments
Special Issues, Collections and Topics in MDPI journals
Interests: reliability-based design optimization (RBDO); robust design; model validation; accelerated testing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the context of the present age, characterized by the imperative for elevated reliability and protracted service life in domains such as high-end manufacturing equipment, technologies that predict fatigue life and assess reliability are confronted with both challenges and opportunities that are without precedent. Fatigue failure, a predominant form of equipment failure, is considerably influenced by numerous uncertainties, encompassing load, material properties, manufacturing tolerances, environmental factors, and multi-physics coupling. The development of advanced theories, models, simulations, tests and data analysis methods that can effectively characterize these uncertainties, achieve high-precision fatigue life prediction and support reliability design and maintenance decision-making is of significant theoretical and engineering value.
The present Special Issue has been conceived with the aim of compiling the latest research progress and innovative achievements in this field, with a focus on the cutting-edge developments in fatigue life prediction models and algorithms, as well as reliability assessment theories and technologies. The Special Issue invites the submission of high-quality research papers encompassing original theoretical explorations, efficient numerical simulations, innovative experimental techniques, intelligent data analysis methods, and practical applications.
Potential topics include but are not limited to the following:
- Fatigue damage models based on physical mechanisms;
- Data-driven fatigue life prediction;
- Application of machine learning/deep learning in fatigue prediction;
- Multi-axis fatigue and non-proportional loading fatigue prediction;
- High/low cycle fatigue and thermomechanical fatigue prediction;
- Fatigue crack initiation and propagation simulation;
- Probabilistic fatigue life prediction;
- Structural reliability analysis methods;
- Reliability modeling based on fatigue failure;
- Uncertainty quantification;
- Sensitivity analysis;
- Time-varying reliability/dynamic reliability;
- Accelerated fatigue testing methods and techniques;
- In situ monitoring and damage identification;
- Non-destructive testing and fatigue state assessment;
Dr. Pengpeng Zhi
Prof. Dr. Zhonglai Wang
Guest Editors
Manuscript Submission Information
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Keywords
- fatigue life prediction
- fatigue reliability analysis
- predictive models
- time-varying reliability
- deep learning
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