Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach
Original language description
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.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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
Journal of Materials Research and Technology
ISSN
2238-7854
e-ISSN
2214-0697
Volume of the periodical
31
Issue of the periodical within the volume
July–August 2024
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
1837-1852
UT code for WoS article
001262824300001
EID of the result in the Scopus database
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