Probabilistic S-N fields based on statistical distributions applied to metallic and composite materials: State of the art

• Published in: Advances in Mechanical Engineering Vol. 11; no. 8
• Main Authors: Barbosa, Joelton Fonseca, Correia, José AFO, Freire Júnior, RCS, Zhu, Shun-Peng, De Jesus, Abílio MP
• Format: Book Review Journal Article
• Language: English
• Published: London, England SAGE Publications 01-08-2019; Sage Publications Ltd; SAGE Publishing
• Subjects: Aluminum; Composite materials; Design engineering; Fatigue failure; Fatigue life; Fracture mechanics; Life prediction; Low cycle fatigue; Materials selection; Probability theory; S N diagrams; Statistical analysis; Statistical distributions; Fatigue; probabilistic analysis; P-S-N curves; life prediction; Weibull distribution
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/1687814019870395

Fatigue life prediction of materials can be modeled by deterministic relations, via mean or median S-N curve approximation. However, in engineering design, it is essential to consider the influence of fatigue life scatter using deterministic-stochastic methods to construct reliable S-N curves and determine safe operation regions. However, there are differences between metals and composites that must be considered when proposing reliable S-N curves, such as distinct fracture mechanisms, distinct ultimate strengths under tension and compression loading, and different cumulative fatigue damage mechanisms including low-cycle fatigue. This study aims at conducting a review of the models used to construct probabilistic S-N fields (P-S-N fields) and demonstrate the methodologies applied to fit the P-S-N fields that are best suited to estimate fatigue life of the selected materials. Results indicate that the probabilistic Stüssi and Sendeckyj models were the most suitable for composite materials, while, for metals, only the probabilistic Stüssi model presented a good fitting of the experimental data, for all fatigue regimes.7