Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68378271:_____/24:00584910 RIV/68407700:21340/24:00372639

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Surface Engineering

ISSN

0267-0844

e-ISSN

1743-2944

Svazek periodika

40

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

66-72

Kód UT WoS článku

001224938300001

EID výsledku v databázi Scopus

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