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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20302 - Applied mechanics

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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