A new expression to evaluate the critical fracture load for bainitic functionally graded steels under mixed mode (I + II) loading. (February 2015)

• Record Type: Journal Article
• Authors: Salavati, H.; Alizadeh, Y.; Kazemi-Moridani, Amir; Berto, F.
• Is Part Of: Engineering failure analysis. Volume 48(2015:Feb.)
• Page Start: 121
• Page End: 136
• Publication Date: 2015-02
• Subjects: Functionally graded steel -- Strain energy density -- Critical fracture load -- Artificial neural network; 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.2014.11.005
• Languages: English
• ISSNs: 1350-6307

Abstract

Graphical abstract: Highlights: Functionally graded steels weakened by notches are investigated. A criterion based on local energy is used for fracture assessment. The developed criterion is used in combination with artificial neural network. The agreement between the experimental results and the theoretical ones is sound. Abstract: The averaged value of the Strain Energy Density (SED) over a well-defined volume is used to assess the critical fracture load of notched components made of Functionally Graded Steels (FGSs) under mixed mode loading (I + II). The boundary of the control volume and the corresponding mean value of SED are determined by a numerical approach. A comparison is carried out between different size (fine/coarse) and shape (triangular/quad) of elements as well as different type of shape function (linear/quadratic) in the mean value of SED evaluation. It can be found from the comparison that one can assess the mean value of SED with any kind of element and mesh. Moreover, over 700 finite element models by considering different values of notch radius (0.2–1 mm), notch depth (4.5–7 mm), notch opening angle (10–90°) and distance of the applied load from the notch bisector line (5–15 mm) have been studied. The models have been used to train an Artificial Neural Network (ANN) to obtain a new simple model to predict the critical fracture load of FGS. The output of the ANN model sounds a good agreement with the experimental and finite element data.