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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F11%3A00361265" target="_blank" >RIV/68081723:_____/11:00361265 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • 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, nstrumented ball indentation test etc. have been applied. A very promising correlation of predicted and experimentally determined values of reference temperature was found.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

    JL - Fatigue and fracture mechanics

  • OECD FORD branch

Result continuities

  • 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)

Others

  • Publication year

    2011

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů