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Prediction of fracture toughness temperature dependence applying neural network
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SYSNO ASEP 0366644 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Prediction of fracture toughness temperature dependence applying neural network Author(s) Dlouhý, Ivo (UFM-A) RID, ORCID
Hadraba, Hynek (UFM-A) RID, ORCID
Chlup, Zdeněk (UFM-A) RID, ORCID
Šmída, T. (SK)Source Title Structural Integrity and Life - ISSN 1451-3749
Roč. 11, č. 1 (2011), s. 9-14Number of pages 6 s. Language eng - English Country RS - Serbia Keywords brittle to ductile transition ; fracture toughness ; artificial neural network ; steels 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 is predicted based on tensile test data. The regularization neural network is developed to solve the correlation of these properties. Three-point bend specimens were applied to determine fracture toughness. The fracture toughness transition dependence is quantified by means of master curve concept enabling to represent it by using one parameter, i.e. reference temperature. Tensile samples with circumferential notch are also examined. In total 29 data sets from low-alloy steels are applied for the analysis. A good correlation of predicted and experimentally determined values of reference temperature is 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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