Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10255781" target="_blank" >RIV/61989100:27230/24:10255781 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2238785424014947" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2238785424014947</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmrt.2024.06.183" target="_blank" >10.1016/j.jmrt.2024.06.183</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Popis výsledku v původním jazyce

Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54-56% and 29-34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.

Název v anglickém jazyce

Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach

Popis výsledku anglicky

Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54-56% and 29-34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Materials Research and Technology

ISSN

2238-7854

e-ISSN

2214-0697

Svazek periodika

31

Číslo periodika v rámci svazku

July–August 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1837-1852

Kód UT WoS článku

001262824300001

EID výsledku v databázi Scopus

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