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