Prediction of fracture toughness transition from tensile test data applying neural network

0361265 - ÚFM 2012 RIV US eng O - Ostatní výsledky
Dlouhý, Ivo - Hadraba, Hynek - Chlup, Zdeněk - Válka, Libor - Žák, L.
Prediction of fracture toughness transition from tensile test data applying neural network.
Proceedings of the ASME 2011 Pressure Vessels & Piping Division Conference. Baltimore, Maryland: ASME, 2011. s. 1-6
Grant CEP: GA ČR(CZ) GAP108/10/0466
Výzkumný záměr: CEZ:AV0Z20410507
Klíčová slova: Fracture toughness * Low alloy steel * Tensile test * Artificial neural network
Kód oboru RIV: JL - Únava materiálu a lomová mechanika

Reference temperature localizing the fracture toughness temperature diagram on temperature axis was predicted based on tensile test data. Regularization artificial neural network (ANN) was adjusted to solve the interrelation of these properties. For analyses, 29 data sets from low-alloy steels were applied. The fracture toughness transition dependence was quantified by means of master curve concept enabling to represent it using one parameter - reference temperature. Different strength and deformation characteristics from standard tensile specimens and notched specimens, nstrumented ball indentation test etc. have been applied. A very promising correlation of predicted and experimentally determined values of reference temperature was found.
Trvalý link: http://hdl.handle.net/11104/0198620