Machine learning enabled prediction of tribological properties of Cu-TiC-GNP nanocomposites synthesized by electric resistance sintering: A comparison with RSM-Reference-Cited by-同舟云学术

Machine learning enabled prediction of tribological properties of Cu-TiC-GNP nanocomposites synthesized by electric resistance sintering: A comparison with RSM

Published:2024-01 Issue: Volume:28 Page:2290-2312
ISSN:2238-7854
Container-title:Journal of Materials Research and Technology
language:en
Short-container-title:Journal of Materials Research and Technology

Author:

Samad Abdul^ORCID,Arif Sajjad,Ansari Salman,Muaz Muhammed,Mohsin Mohammad^ORCID,Ulla Khan Anwar,Khan Mohammad Ehtisham,Bashiri Abdullateef H.,Zakri Waleed,Ali Wahid

Publisher

Elsevier BV

Subject

Metals and Alloys,Surfaces, Coatings and Films,Biomaterials,Ceramics and Composites

Reference79 articles.

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3. Development and performance analysis of novel in situ Cu–Ni/Al2O3 nanocomposites;Ali;Ceram Int,2022

4. Characterization of surface morphology, wear performance and modelling of graphite reinforced aluminium hybrid composites;Arif;Eng Sci Technol Int J,2020

5. Microstructure and mechanical characterization of Cu–Ni/Al2O3 nanocomposites fabricated using a novel in situ reactive synthesis;Ali;Ceram Int,2022

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