Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00372639" target="_blank" >RIV/68407700:21220/24:00372639 - isvavai.cz</a>
Alternative codes found
RIV/68378271:_____/24:00584910 RIV/68407700:21340/24:00372639
Result on the web
<a href="https://doi.org/10.1177/02670844231221974" target="_blank" >https://doi.org/10.1177/02670844231221974</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/02670844231221974" target="_blank" >10.1177/02670844231221974</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening
Original language description
The industry's demand for intricate geometries has spurred research into additive manufacturing (AM). Customising material properties, including surface roughness, integrity and porosity reduction, are the key industrial goals. This necessitates a holistic approach integrating AM, laser shock peening (LSP) and non-planar geometry considerations. In this study, machine learning and neural networks offer a novel way to create intricate, abstract models capable of discerning complex process relationships. Our focus is on leveraging the certain range of laser parameters (energy, spot area, overlap) to identify optimal residual stress, average surface roughness, and porosity values. Confirmatory experiments demonstrate close agreement, with an 8% discrepancy between modelled and actual residual stress values. This approach's viability is evident even with limited datasets, provided proper precautions are taken.
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
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Surface Engineering
ISSN
0267-0844
e-ISSN
1743-2944
Volume of the periodical
40
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
7
Pages from-to
66-72
UT code for WoS article
001224938300001
EID of the result in the Scopus database
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