Using Grey Relational Analysis for Optimization of Dry Sliding Wear Parameters of Aluminium Matrix Composites (AA7068/TiC)

The present study highlights about multi-Objective Optimization of Dry sliding wear parameters of Aluminium Matrix Composites (AA7068/TiC) using Grey Relational Analysis. Metal matrix composites are supplanting conventional materials due to their prevalent properties like high strength of weight ratio, high specific stiffness, high fracture toughness, high thermal stability and wear resistance etc. AA7068 is one of the industrially accessible strongest aluminium alloys that was taken as a matrix material and the reinforcement is titanium carbide (TiC) particles of 4 µm size. In this investigation, Al-TiC composites consist of TiC particles of an average size 4µm whose wt% of reinforcement varied from 2 to 10 wt% in steps of 2 wt%, composites have been prepared using the stir casting technique. Dry-sliding wear experiments have been performed on pin-on-disc apparatus according to Taguchi’s L25 in the design of experiments. The parameters considered are wt% of TiC, rotational speed (Nr), load (P) and sliding velocity (Vs). The motivation behind the Analysis of Variance is to figure out the process parameter that strongly influences the wear characteristics of AA7068/TiC MMCs. This can be accomplished by estimating the amount of the sum of squared deviations from the total mean of the grey relational grade for each process parameter and their error variance. Optimum combinations of parameters have been identified based on grey relational grade (GRG) to solve the wear response of AA7068/TiC MMCs. Also, analysis of variance (ANOVA) is applied to recognize the main factors affecting the wear response. Confirmation experiments with optimum conditions show that the results were nearer to the anticipated outcomes. The confirmation experiments confirm that the proposed GRA can track down the optimal combination of process parameters with multiple quality characteristics.