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

Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results

“…[1][2][3][4][5][6][7][8][9][10][11][12] used in this study, nT indicates the specimen thickness, and n is expressed in multiples of 25 mm. They are fundamentally extracted from previous work [30,33], but differ slightly in terms of the following: (1) K Jc > K Jc(ulimit) invalid data were excluded, (2) K Jc data were limited to cases obtained with standard specimens of thickness-to-width ratio B/W = 0.5, (3) When there were no σ YS data for the fracture toughness test temperature, it was obtained by using the following modified Z-A σ YS temperature-dependent MC [9]…”

“…Another idea was to replace time-and material-consuming fracture toughness tests with tensile tests, assuming that K Jc has a direct relationship with SED obtained via tensile tests. Thus, the artificial neural network (ANN) approach was applied to 531 K Jc data collected in our previous works [30,33] to construct a K Jc predictor based on tensile test properties, thereby eliminating the need to conduct fracture toughness tests. The data were obtained for five heats of RPV and seven heats of non-RPV steels.…”

Data‐driven approach to construct a fracture toughness master curve for ferritic steels based on tensile properties

“…While the ASTM E1921 1 MC provides one solution, a failure in predicting K Jc for some ferritic steels has been reported. 2,3 Hence, considering a data-driven approach and reexamining the physical background of K Jc temperature dependence may enable the construction of a K Jc MC that is applicable to all ferritic steels. The constructed K Jc MC considers tensile properties as explanatory parameters and avoids time-and material-consuming K Jc tests, thus holding higher merit than that of the ASTM E1921 MC.…”