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

Autor: Abdul Samad, Sajjad Arif, Salman Ansari, Muhammed Muaz, Mohammad Mohsin, Anwar Ulla Khan, Mohammad Ehtisham Khan, Abdullateef H. Bashiri, Waleed Zakri, Wahid Ali
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Materials Research and Technology, Vol 28, Iss , Pp 2290-2312 (2024)
Druh dokumentu: article
ISSN: 2238-7854
DOI: 10.1016/j.jmrt.2023.12.132
Popis: In the present study, copper matrix composites were successfully produced through the powder metallurgy route by applying the electrical resistance sintering technique. Copper composites were reinforced with 5 wt. % TiC and different concentrations of GNP (0.1, 0.2, and 0.3 wt. %). Microstructural investigations confirmed uniform dispersion of TiC and GNP micro and nanoparticles in the copper matrix. A sintering temperature of 900 °C resulted in better densification and hardness of the prepared composites. Moreover, ML models were developed to predict the sintered density, hardness, and wear loss of the composites. Further, it was found that Multi-Layer Perceptron outperforms all other ML models with R2 values of 0.975, 0.934, and 0.948 in the prediction of density, hardness, and wear loss of the composites. On the other hand, RSM shows predicted R2 values of 0.8012, 0.8507, and 0.8756.
Databáze: Directory of Open Access Journals