Low-cycle fatigue of laser powder bed fusion-processed AlSi10Mg using recycled powder: Experiments and machine learning-assisted lifetime prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F25%3A00619086" target="_blank" >RIV/68081723:_____/25:00619086 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21220/25:00384526
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0264127525003466?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0264127525003466?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.matdes.2025.113926" target="_blank" >10.1016/j.matdes.2025.113926</a>
Alternative languages
Result language
angličtina
Original language name
Low-cycle fatigue of laser powder bed fusion-processed AlSi10Mg using recycled powder: Experiments and machine learning-assisted lifetime prediction
Original language description
As additive manufacturing technologies advance, the increased use of recycled powder feedstock becomes inevitable. However, recycling may compromise the purity and quality of material inputs, potentially leading to inferior component properties. In this study, strain-controlled Low-Cycle Fatigue (LCF) tests were performed on laser powder bed fusion-processed AlSi10Mg using recycled powder. The use of recycled powder led to an increased oxygen content, resulting in more pores in the microstructure. The LCF tests covered various strain amplitudes under tension-compression for both horizontally and vertically built specimens. After the initial softening, the cyclic response stabilized, with the Hall-Petch effect identified as the main strengthening mechanism due to the eutectic cell walls, regardless of build direction. Investigations into the damage mechanisms revealed deposition defects as the main factor influencing transgranular crack initiation and propagation. Horizontally built specimens exhibited shorter fatigue lifetimes due to a higher number of deposition defects apparently caused by their positions on the build platform. A physics-informed neural network, combined with a strain-life approach, was proposed to predict the fatigue lifetime of small datasets and account for the damaging effects of deposition-related defects. The predicted data showed a good correlation with the experimental results.
Czech name
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Czech description
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Classification
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
20501 - Materials engineering
Result continuities
Project
<a href="/en/project/EH22_008%2F0004634" target="_blank" >EH22_008/0004634: Mechanical engineering of biological and bio-inspired systems</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Materials and Design
ISSN
0264-1275
e-ISSN
1873-4197
Volume of the periodical
253
Issue of the periodical within the volume
MAY
Country of publishing house
GB - UNITED KINGDOM
Number of pages
19
Pages from-to
113926
UT code for WoS article
001476787500001
EID of the result in the Scopus database
2-s2.0-105002634887