Wear optimization of graphene reinforced magnesium AZ31 composite using Taguchi design

Autor: Ganesan Subbiah, Sridhar Raja Sundara Raju Kachupalli, E. Mathan, Hemanandh Janarthanam, V. Kishorekumar, M. Sangeetha, Senthil Kumar Jayapalan
Rok vydání: 2020
Předmět:
Zdroj: 3RD INTERNATIONAL CONFERENCE ON FRONTIERS IN AUTOMOBILE AND MECHANICAL ENGINEERING (FAME 2020).
ISSN: 0094-243X
DOI: 10.1063/5.0034025
Popis: Magnesium is more effectively fitted for implant operation as could be in contact with the living system without producing an adverse effect. It has a modulus of elasticity close to the natural bone. In this investigation magnesium composite (AZ31) was reinforced with graphene nano particles by employing liquid casting method. The dry wear test on Mg matrix composite reinforced with GNP was carried out using the pin-on-disc method. The various parameters adopted for the pin-on-disc method are applied load, velocity and weight proportion of graphene reinforcement. The experiment was conducted based on L9 orthogonal array employing Taguchi technique. The optimal wear rate was obtained by ANOVA, the regression equation and the influencing parameters are identified. The signal to noise ratio was used to review the effect of the parameters. The Experiment results revealed that the presence of GNP in different composition increases the wear resistance of composites.
Databáze: OpenAIRE