Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26316919%3A_____%2F24%3AN0000010" target="_blank" >RIV/26316919:_____/24:N0000010 - isvavai.cz</a>

Result on the web

<a href="https://www-sciencedirect-com.ezproxy.techlib.cz/science/article/pii/S0142112324004675?via%3Dihub" target="_blank" >https://www-sciencedirect-com.ezproxy.techlib.cz/science/article/pii/S0142112324004675?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijfatigue.2024.108608" target="_blank" >10.1016/j.ijfatigue.2024.108608</a>

Result language

angličtina

Original language name

Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading

Original language description

Axial-torsional Low-Cycle Fatigue (LCF) tests were conducted under strain control on Additively Manufactured (AM) 316L stainless steel using laser powder bed fusion. The tests covered various strain amplitudes tension-compression, proportional, and pure shear loading paths. The AM 316L stainless steel exhibited softening and transgranular cracking under all the investigated loading conditions. The presence of deposition defects, predominantly the lack of fusion type, was identified as the main factor influencing the crack initiation and propagation, as well as the scatter in the fatigue lifetime. Therefore, to account for the damaging of these deposition related defects on fatigue lifetime, a novel physics-informed neural network was proposed. Subsequently, this neural network was combined with the critical plane approach, based on the tensile of failure, in order to predict the lifetime of AM 316L stainless steel. The predicted data exhibited correlation with the experimental results.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

INTERNATIONAL JOURNAL OF FATIGUE

ISSN

0142-1123

e-ISSN

1879-3452

Volume of the periodical

190

Issue of the periodical within the volume

JAN 2025

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

nestránkováno

UT code for WoS article

001322093600001

EID of the result in the Scopus database

2-s2.0-85204448639