Assessments of Desirability Wear Behaviour on Al-Coconut Shell Ash - Metal Matrix Composite using Grey - Fuzzy Reasoning Grade

Siva Sankara Raju, Gunji Srinivas Rao
<span title="2017-04-01">2017</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
Objectives: To prepare Aluminium Metal matrix Composite (AMC) reinforced with Coconut Shell Ash (CSA) via compo casting route by 5, 10 and 15% percentage of volumes. Afterward, the tribological behaviour of Al-CSA composite has been determined. Methods/Statistical Analysis: To known the better optimal condition for tribological performance of composite, the input parameters are considered as load, percentage of CSA, sliding distance and sliding velocity whereas output responses are wear,
more &raquo; ... c wear rate and Coefficient of Friction (COF). Experiments were designed based on Taguchi [L 16 ] orthogonal array and conducted on pin-on-disc setup. The multi-responses were altered into a single-response using Desirability Hunctional Analysis (DFA), Grey Relational Grade (GRG), and Fuzzy Interface System (FIS). Findings: The composite hardness and tensile strength is increased with increasing % of CSA. Similarly, density and elongation decreased. The Grey-Fuzzy Reasoning Grade (GFRG) used for optimization of response with decrease the uncertainty in decision making. The tribological performances of characteristics are improved using grey-fuzzy relation. Analysis of variance (ANOVA) of GFRG results specify load is the most influencing parameter followed by percentage of CSA, sliding velocity and sliding distance. The initial parameter of the DFA, GRG and GRFG is 0.348, 0.736 and 0.816 through optimal conditional values of the dfa, grg and gfrg are 0.854, 0.806 and 0.864 respectively. So, it states that GFRG in wear behavioral parameters of csap composites has greater improve by using Grey-Fuzzy Reasoning Approach (GFRA). The achieved optimal condition is l1r2d2v2 (i.e. Load (10n), % of CSA (15), sliding distance (2000m) and velocity (2m/s). Finally, confirmation test is conducted to validate the regression equation and the worn-out surface is examined by Scanning Electron Microscopic (SEM). Application/Improvements: The Al-CSA composite properties are enhanced with increasing of volume of CSA. From the Overall approaches Grey -Fuzzy Reasoning Grade (GFRG) is obtained less error (0.015) than the others.
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