Quantitative comparison of the predictions of fracture toughness temperature dependence using ASTM E1921 master curve and stress distribution T-scaling methods. (April 2020)

• Record Type: Journal Article
• Authors: Meshii, Toshiyuki; Yakushi, Goh; Takagishi, Yoichi; Fujimoto, Yohei; Ishihara, Kenichi
• Is Part Of: Engineering failure analysis. Volume 111(2020)
• Publication Date: 2020-04
• Subjects: Fracture toughness -- Ductile-to-brittle transition temperature region -- Temperature dependence -- T-scaling -- Machine learning; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2020.104458
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The performances of CDS and ASTM E1921 MC were quantitatively compared. CDS exhibited better performance in predicting fracture temperature dependence. Based on machine learning, fracture toughness was directly correlated to yield stress. Abstract: The fracture toughness temperature dependence of ferritic steels in the ductile-to-brittle transition temperature region, predicted using the ASTM E1921 master curve (MC) and stress distribution T-scaling (CDS) methods, is presented in this paper. A total of 34 cases (i.e., combination of material heats and specimen types) including 661 fracture toughness test data were considered. First, the direct correlation between fracture toughness and yield stress—as suggested by the CDS method—was validated using machine learning techniques. Subsequently, the accuracy of the predictions obtained by each method was quantitatively evaluated using a coefficient of determination. In 25 out of the 34 cases, the CDS method showed better prediction ability than the MC method. In the other 9 cases, the difference between the two methods was small, if considered from an engineering perspective.