Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F22%3A00358685" target="_blank" >RIV/68407700:21220/22:00358685 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.ijfatigue.2022.107067" target="_blank" >https://doi.org/10.1016/j.ijfatigue.2022.107067</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijfatigue.2022.107067" target="_blank" >10.1016/j.ijfatigue.2022.107067</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

Popis výsledku v původním jazyce

In this article, machine learning is used to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading, both of which represent the most complex loadings that couple creep, fatigue and oxidation damage. A uniaxial fatigue and fatigue–creep dataset, which was obtained for temperatures of between 300 °C and 600 °C for a low-alloy martensitic steel, is utilized in this study. Two different machine learning based approaches to lifetime prediction are demonstrated. The first approach is based only on a shallow neural network, whereas the second approach is proposed as a combination of a sequence learning based model – either long short-term memory network or gated recurrent unit – with the shallow neural network. A good correlation between the experiment and the prediction suggests that lifetime under complex thermo-mechanical loading can be reasonably predicted via the proposed machine learning based damage models.

Název v anglickém jazyce

Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

Popis výsledku anglicky

In this article, machine learning is used to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading, both of which represent the most complex loadings that couple creep, fatigue and oxidation damage. A uniaxial fatigue and fatigue–creep dataset, which was obtained for temperatures of between 300 °C and 600 °C for a low-alloy martensitic steel, is utilized in this study. Two different machine learning based approaches to lifetime prediction are demonstrated. The first approach is based only on a shallow neural network, whereas the second approach is proposed as a combination of a sequence learning based model – either long short-term memory network or gated recurrent unit – with the shallow neural network. A good correlation between the experiment and the prediction suggests that lifetime under complex thermo-mechanical loading can be reasonably predicted via the proposed machine learning based damage models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

International Journal of Fatigue

ISSN

0142-1123

e-ISSN

1879-3452

Svazek periodika

163

Číslo periodika v rámci svazku

107067

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000829347600004

EID výsledku v databázi Scopus

2-s2.0-85132933722