{"title":"考虑小样本特性的缺口试样P-S-N曲线拟合方法研究","authors":"Ziyang Zhang, Jianhui Liu, Juntai Hu, Qingjun Wu, Shenglei Wu","doi":"10.1111/ffe.14490","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Aiming at the issue of fatigue test data for large-scale mechanical components of building steel are very limited, a method for fitting <i>P-S-N</i> curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the <i>P-S-N</i> curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting <i>P-S-N</i> curve is more reliable and accurate.</p>\n </div>","PeriodicalId":12298,"journal":{"name":"Fatigue & Fracture of Engineering Materials & Structures","volume":"48 1","pages":"404-422"},"PeriodicalIF":3.1000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on the P-S-N Curve Fitting Method of Notched Specimens Considering Small Sample Properties\",\"authors\":\"Ziyang Zhang, Jianhui Liu, Juntai Hu, Qingjun Wu, Shenglei Wu\",\"doi\":\"10.1111/ffe.14490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Aiming at the issue of fatigue test data for large-scale mechanical components of building steel are very limited, a method for fitting <i>P-S-N</i> curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the <i>P-S-N</i> curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting <i>P-S-N</i> curve is more reliable and accurate.</p>\\n </div>\",\"PeriodicalId\":12298,\"journal\":{\"name\":\"Fatigue & Fracture of Engineering Materials & Structures\",\"volume\":\"48 1\",\"pages\":\"404-422\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fatigue & Fracture of Engineering Materials & Structures\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ffe.14490\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fatigue & Fracture of Engineering Materials & Structures","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ffe.14490","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Research on the P-S-N Curve Fitting Method of Notched Specimens Considering Small Sample Properties
Aiming at the issue of fatigue test data for large-scale mechanical components of building steel are very limited, a method for fitting P-S-N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P-S-N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P-S-N curve is more reliable and accurate.
期刊介绍:
Fatigue & Fracture of Engineering Materials & Structures (FFEMS) encompasses the broad topic of structural integrity which is founded on the mechanics of fatigue and fracture, and is concerned with the reliability and effectiveness of various materials and structural components of any scale or geometry. The editors publish original contributions that will stimulate the intellectual innovation that generates elegant, effective and economic engineering designs. The journal is interdisciplinary and includes papers from scientists and engineers in the fields of materials science, mechanics, physics, chemistry, etc.