Multi-objective Optimization of Friction Welding Process Parameters using Grey Relational Analysis for Joining Aluminium Metal Matrix Composite

Sreenivasan KONGANAPURAM SUNDARARAJAN, Satish Kumar SHANMUGAM

Abstract


Aluminium metal matrix composites has gained importance in recent time because of its improved mechanical and metallurgical properties. The welding of aluminium metal matrix composites using conventional welding process has got many demerits so in order to overcome them a solid state welding process is to be employed. To achieve a good strength weld in the aluminium metal matrix composite bars an efficient and most preferred technique is friction welding. In this work the aluminium metal matrix composite AA7075 + 10 % vol SiC-T6 is selected and friction welded. The combination of friction welding process parameters such as spindle speed, friction pressure, upset pressure and burn-off- length for joining the AA7075 + 10 % vol SiCP-T6 metal matrix composite bars are selected by Taguchi’s design of experiment. The optimum friction welding parameters were determined for achieving improved ultimate tensile strength and the hardness using grey relational analysis. A combined grey relational grade is found from the determined grey relational coefficient of the output responses and the optimum friction welding process parameters were obtained as spindle speed – 1200 rpm, friction pressure – 100 MPa, upset pressure – 250 MPa, Burn-off-Length – 2 mm. Analysis of variance (ANOVA) performed shows that the friction pressure is the most significant friction welding parameter that influences the both the ultimate tensile strength and hardness of friction welded AA7075 + 10 % volSiCP-T6 joints. The fractured surface under microstructure study also revealed good compliance with the grey relational grade result.

DOI: http://dx.doi.org/10.5755/j01.ms.24.2.17725


Keywords


aluminium metal matrix composite; Taguchi’s design of experiments; friction welding; ultimate tensile strength; hardness; optimization; grey relational analysis; ANOVA

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Print ISSN: 1392–1320
Online ISSN: 2029–7289