Prediction of Fracture Toughness Transition from Tensile Test Parameters Applying Artificial Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F10%3A00354571" target="_blank" >RIV/68081723:_____/10:00354571 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Prediction of Fracture Toughness Transition from Tensile Test Parameters Applying Artificial Neural Networks
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/GAP108%2F10%2F0466" target="_blank" >GAP108/10/0466: Fracture behaviour prediction based on kvantification of local material response</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
New Methods of Damage and Failure Analysis of Structural Parts
ISBN
978-80-248-2265-5
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
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Publisher name
VŠB - TU Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
Sep 6, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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