PREDICTION OF FRACTURE TOUGHNESS TRANSITION FROM TENSILE TEST DATA APPLYING NEURAL NETWORKS

0484701 - ÚFM 2018 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Dlouhý, I. - Hadraba, Hynek - Chlup, Zdeněk - Válka, Libor - Žák, L.
PREDICTION OF FRACTURE TOUGHNESS TRANSITION FROM TENSILE TEST DATA APPLYING NEURAL NETWORKS.
PROCEEDING OF THE ASME PRESSURE VESSELS AND PIPING CONFERENCE. Vol. 1. New York: American Society Mechanical Engineers, 2012, s. 101-105. ISBN 978-0-7918-4451-9.
[ASME 2011 - Pressure Vessels and Piping Conference. Baltimore (MD), 17.07.2011-21.07.2011]
Grant CEP: GA ČR(CZ) GAP108/10/0466
Výzkumný záměr: CEZ:AV0Z2041904
Klíčová slova: PRECRACKED CHARPY SPECIMENS * IMPACT TOUGHNESS * MO-V * STEEL
Obor OECD: Audio engineering, reliability analysis

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, instrumented 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/11104/0279850