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Prediction of fracture toughness transition from tensile test data applying neural network
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SYSNO ASEP 0361265 Document Type O - Others R&D Document Type Others Title Prediction of fracture toughness transition from tensile test data applying neural network Author(s) Dlouhý, Ivo (UFM-A) RID, ORCID
Hadraba, Hynek (UFM-A) RID, ORCID
Chlup, Zdeněk (UFM-A) RID, ORCID
Válka, Libor (UFM-A)
Žák, L. (CZ)Source Title Proceedings of the ASME 2011 Pressure Vessels & Piping Division Conference. - Baltimore, Maryland : ASME, 2011
S. 1-6Number of pages 6 s. Publication form CD-ROM - CD-ROM Action Pressure Vessels & Piping Division Conference PVP2011 Event date 17.07.11-21.07.11 VEvent location Baltimore, Maryland Country US - United States Event type WRD Language eng - English Country US - United States Keywords Fracture toughness ; Low alloy steel ; Tensile test ; Artificial neural network Subject RIV JL - Materials Fatigue, Friction Mechanics R&D Projects GAP108/10/0466 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z20410507 - UFM-A (2005-2011) Annotation 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. Workplace Institute of Physics of Materials Contact Yvonna Šrámková, sramkova@ipm.cz, Tel.: 532 290 485 Year of Publishing 2012
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