Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F08%3A00019531" target="_blank" >RIV/61989100:27360/08:00019531 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Time Prediction of Cooling Down Low Range Specimen with Neural Network Exploitation
Original language description
The method exploits sufficient similarity between cooling down curves of individual specimens from the same material but when specimens vary in geometric shape. Time scale altering for individual specimens leads from practical point of view to coincidence of all curves with so called ?general curve? for given material which is calculated from measured values by means of statistic methods. This operation can be denoted as a definition of time transformation coefficient ( TTC ) (for known specimens). If an artificial neural network learns itself to assign time transformation coefficient to known dimensions of specimens, it is then with sufficient accuracy able to determine time transformation coefficient even for specimens with different shapes, for which it has not been learnt. By backward time transformation is then possible to predict probable time course of the cooling down curve and accordingly also the moment of accomplishment of given temperature
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JG - Metallurgy, metal materials
OECD FORD branch
—

Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)