Effect of wear parameters on dry sliding behavior of Fly Ash/SiC particles reinforced AA 2024 hybrid composites

Kurapati, Vijaya Bhaskar; Kommineni, Ravindra

doi:10.1088/2053-1591/aa8a3e

Cited by 9 publications

(1 citation statement)

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“…Research done by Rao et al [3] reported refined grain structure, reduction of destiny, and improvements of the hardness and tensile strength of AA 6061 reinforced with particulate fly ash in comparison to the base metal. Kurapati and Kommineni [4] reported improvements in the wear behaviour of hybrid AA 2024/fly ash/SiC in comparison to the monolithic metal due to adequate distribution of the reinforcing particulates.…”

Section: Introductionmentioning

confidence: 99%

Characterization, machinability studies, and multi-response optimization of AA 6082 hybrid metal matrix composite

Ononiwu

Ozoegwu

Madushele

et al. 2021

Int J Adv Manuf Technol

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This work investigated the effect of carbonized eggshell and fly ash on the microstructure, mechanical properties and machinability of AA 6082. The fabrication method selected for this study was stir casting. For the hybrid metal matrix composite, the weight fraction was 2.5wt% carbonized eggshell and 2.5wt% fly ash. The microstructural examination of the cast composite showed an even dispersal of the reinforcing phases in the aluminium matrix. Density analysis saw a 10.66% reduction of the cast composite in comparison to the aluminium alloy. Improvements of 12.32%, 21.91%, and 8.30% were recorded for the microhardness, tensile strength and compressive strength respectively. The wear studies of the cast samples revealed coefficients of friction (CoF) of 0.499 and 0.290 for the base metal and the AMC respectively. For the machinability studies, the surface roughness and flank tool wear were the responses under consideration. The design of experiments was conducted using the Taguchi method. The input parameters for this investigation was the cutting speeds (100 mm/min, 200 mm/min, 300 mm/min), feeds (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev) and depths of cut (0.5 mm, 1.0 mm, 1.5 mm). For the multi-response optimization, Taguchi based grey relational analysis was used. The analysis of variance (ANOVA) of the grey relational grade (GRG) revealed that the feed was the most influential factor on the surface roughness and tool wear. The initial optimization showed the optimal cutting speed, feed and depth of cut as 200 mm/min, 0.2 mm/rev and 1.5 mm respectively. The confirmatory experiments revealed that the optimal combination of factors was 200mm/min, 0.2 mm/rev and 0.5 mm for the cutting speed, feed and depth of cut respectively.

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Section: Introductionmentioning

confidence: 99%