Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00368411" target="_blank" >RIV/68407700:21220/23:00368411 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.engfracmech.2023.109351" target="_blank" >https://doi.org/10.1016/j.engfracmech.2023.109351</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engfracmech.2023.109351" target="_blank" >10.1016/j.engfracmech.2023.109351</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading

Popis výsledku v původním jazyce

In this article, a machine learning approach is utilized to predict lifetime under multiaxial fatigue loading. A novel hybrid physics-informed neural network is proposed, where a combination of a LSTM/GRU cell and a fully connected layer is used to extract the damage parameter of a loading cycle. A newly proposed logarithmic activation function is then used to introduce the power law relationship between the damage parameter and the predicted fatigue life. In addition, the selected parameters of the suggested network are physically guided. Two data pre-processing methods are used to ascertain the rotational invariance of the axial–torsional loading conditions. The prediction capability of the suggested approach is demonstrated by the experimental datasets that consist of axial–torsional test results obtained for 42CrMo4 steel and for 2024-T3 aluminium alloy. A good correlation between the predicted and experimental data was achieved. Finally, the extrapolation capability of the proposed approach is demonstrated through modelling the stress-life curves for the data-points outside the experimental data range.

Název v anglickém jazyce

Using hybrid physics-informed neural networks to predict lifetime under multiaxial fatigue loading

Popis výsledku anglicky

In this article, a machine learning approach is utilized to predict lifetime under multiaxial fatigue loading. A novel hybrid physics-informed neural network is proposed, where a combination of a LSTM/GRU cell and a fully connected layer is used to extract the damage parameter of a loading cycle. A newly proposed logarithmic activation function is then used to introduce the power law relationship between the damage parameter and the predicted fatigue life. In addition, the selected parameters of the suggested network are physically guided. Two data pre-processing methods are used to ascertain the rotational invariance of the axial–torsional loading conditions. The prediction capability of the suggested approach is demonstrated by the experimental datasets that consist of axial–torsional test results obtained for 42CrMo4 steel and for 2024-T3 aluminium alloy. A good correlation between the predicted and experimental data was achieved. Finally, the extrapolation capability of the proposed approach is demonstrated through modelling the stress-life curves for the data-points outside the experimental data range.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/GA21-06645S" target="_blank" >GA21-06645S: Výzkum životnosti strojních součástí při víceosém teplotně-mechanickém zatěžování s proměnnou amplitudou</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Engineering Fracture Mechanics

ISSN

0013-7944

e-ISSN

1873-7315

Svazek periodika

289

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

—

Kód UT WoS článku

001027508200001

EID výsledku v databázi Scopus

2-s2.0-85162882691