Abstract
The reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization neural network was developed to solve the correlation of these properties. Three-point bend specimens were applied to determine fracture toughness. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter, i.e. reference temperature. The reference temperature was calculated applying the multi-temperature method. Different strength and deformation characteristics and parameters were determined from standard tensile specimens focusing on data from localized deformation during specimen necking. Tensile samples with circumferential notch were also examined. In total 29 data sets from low-alloy steels were applied for the analyses. A good correlation of predicted and experimentally determined values of reference temperature was found.

Key words
steels, brittle to ductile transition, fracture, toughness, artificial neural network

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