{"title":"Fatigue life prediction method based on polynomial chaotic expansion and Weibull distribution","authors":"GaoFei Ji, LingHui Hu","doi":"10.1007/s10704-025-00858-y","DOIUrl":null,"url":null,"abstract":"<div><p>This study proposes a fatigue life prediction method combining small-sample data expansion with the Weibull distribution function, incorporating the first order reliability factor (FOSM) to improve accuracy. Using Generalized Polynomial Chaos Expansion (GPC) and Latin Hypercube Sampling (LHS), small-sample fatigue data is expanded, followed by enhancing the two-parameter Weibull model with FOSM. Results show the generalized polynomial chaotic expansion method and Latin hypercube sampling are used to obtain the probability density curve when the stress level is 350 MPa, and the original data are all on this probability density curve, indicating that the expansion method is more credible. High prediction precision within a 1.5 × error range, with logarithmic safety life linearly related to stress level and decreasing with higher failure probability.</p></div>","PeriodicalId":590,"journal":{"name":"International Journal of Fracture","volume":"249 3","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fracture","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10704-025-00858-y","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a fatigue life prediction method combining small-sample data expansion with the Weibull distribution function, incorporating the first order reliability factor (FOSM) to improve accuracy. Using Generalized Polynomial Chaos Expansion (GPC) and Latin Hypercube Sampling (LHS), small-sample fatigue data is expanded, followed by enhancing the two-parameter Weibull model with FOSM. Results show the generalized polynomial chaotic expansion method and Latin hypercube sampling are used to obtain the probability density curve when the stress level is 350 MPa, and the original data are all on this probability density curve, indicating that the expansion method is more credible. High prediction precision within a 1.5 × error range, with logarithmic safety life linearly related to stress level and decreasing with higher failure probability.
期刊介绍：
The International Journal of Fracture is an outlet for original analytical, numerical and experimental contributions which provide improved understanding of the mechanisms of micro and macro fracture in all materials, and their engineering implications.
The Journal is pleased to receive papers from engineers and scientists working in various aspects of fracture. Contributions emphasizing empirical correlations, unanalyzed experimental results or routine numerical computations, while representing important necessary aspects of certain fatigue, strength, and fracture analyses, will normally be discouraged; occasional review papers in these as well as other areas are welcomed. Innovative and in-depth engineering applications of fracture theory are also encouraged.
In addition, the Journal welcomes, for rapid publication, Brief Notes in Fracture and Micromechanics which serve the Journal''s Objective. Brief Notes include: Brief presentation of a new idea, concept or method; new experimental observations or methods of significance; short notes of quality that do not amount to full length papers; discussion of previously published work in the Journal, and Brief Notes Errata.
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