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.
Author(s) Details:
Syed Altaf Hussain,
Department of Mechanical Engineering, Rajeev Gandhi Memorial College
of Engineering & Technology, Nandyal-51850, India.
Please see the link here: https://stm.bookpi.org/CAERT-V1/article/view/14159
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