• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2016, Volume: 9, Issue: 44, Pages: 1-7

Original Article

Experimental Investigation and Optimization of Machining Parameters using Grey-Relational Analysis Approach and Fuzzy Based Taguchi Loss Function Method


Objectives: The objective of present study is to investigate the machinability effect on turning of hybrid metal matrix composites (Al/SiC/B4C) by coated carbide inserts. Then the application of Grey-Relational Analysis approach (GRAA) and Fuzzy-Taguchi Loss Function (FTLF)are used for the optimization ofmulti quality criteria response is reported. Methods/Statistical Analysis: The bar type hybrid composite are fabricated using stir casting technique. The composite has356Alalloy as ‘matrix’ and ‘SiC’with different wt%(volume fraction) of 5%, 10%, 15%and B4C (5%) particles as reinforcement material. Force (Fz) and roughness (Ra and Rt) are considered as two quality characteristics. L9orthogonal array, the ratio of signal to noise (S/N), multi-response performance characteristics (MPC), and variance test (ANOVA) are applied to investigate the quality characteristics for developed new composites. Findings: The optimal cutting parameters are determined using Grey-relational analysis Approach (GRAA) and fuzzy-Taguchi Loss Function (FTLF). Based on both approaches, the optimal levels of machining parameters are determined as A1B1C1D1.As a result, the grey relational analysis and the fuzzy-Taguchi method confirm the effectiveness for optimization of machining parameters with multiple quality criteria responses. Among these methods, fuzzy-Taguchi Loss Function (FTLF) is the most superior. Application/Improvements: In addition, the variance test (ANOVA)is identifies, the factor D (cutting depth) and C (feed rate),two influential parameters which account 55.77% and 69.8 % of the variance for grey-relational grade (GRA) and fuzzy- reasoning grade (FRG).

Keywords: Fuzzy, Grey Relational Apporach, Investigation, Optimization, Taguchi’s Loss Function Method. 


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