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Machine learning enabled prediction of tribological propert..:
Samad, Abdul
;
Arif, Sajjad
;
Ansari, Salman
...
Journal of Materials Research and Technology. 28 (2024) - p. 2290-2312 , 2024
Link:
https://doi.org/10.1016/j.jmrt.2023.12.132
RT Journal T1 <Title>Machine learning enabled prediction of tribological properties of Cu-TiC-GNP nanocomposites synthesized by electric resistance sintering: A comparison with RSM</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1016_j.jmrt.2023.12.132&Exemplar=1&LAN=DE A1 Samad, Abdul A1 Arif, Sajjad A1 Ansari, Salman A1 Muaz, Muhammed A1 Mohsin, Mohammad A1 Ulla Khan, Anwar A1 Khan, Mohammad Ehtisham A1 Bashiri, Abdullateef H. A1 Zakri, Waleed A1 Ali, Wahid PB Elsevier BV YR 2024 SN 2238-7854 JF Journal of Materials Research and Technology VO 28 SP 2290 OP 2312 LK http://dx.doi.org/https://doi.org/10.1016/j.jmrt.2023.12.132 DO https://doi.org/10.1016/j.jmrt.2023.12.132 SF ELIB - SuUB Bremen
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