Analysis of Machining Parameters in WEDM of Al/SiCp20 MMC Using Taguchi-Based Grey-Fuzzy Approach

Abstract

Aluminium silicate metal matrix composite (AlSiC MMC) is satisfying the requirement of material with good mechanical, thermal properties, and good wear resistance. But the difficulties during the machining are the main hurdles to its replacement for other materials. Wire electric discharge machining (WEDM) is a very effective process used for this type of difficult-to-cut material. So an effort has been taken to find out the most favourable level of input parameters for WEDM of AlSiC (20%) composite using a Taguchi-based hybrid grey-fuzzy grade (GFG) approach. e plan for experimentation is designed using Taguchi's L 9 (2 3 ) array. e various process parameters considered for the investigation are pulse on time (T ON ), pulse off time (T OFF ), wire feed rate (WFR), and peak current (IP). Surface integrity such as surface roughness measured during the different types of cutting (along straight, inclined, and curvature directions) is considered in the present work. Grey relational analysis (GRA) pooled with the fuzzy logic is effectively used to find out the grey-fuzzy reasoning grade (GFRG). e Taguchi approach is coupled with the GFRG to obtain the optimum set of process parameters. From the experimental findings, it has been observed that the most economical process parameters for WEDM of AlSiCp20 were the pulse on time is 108 microsec, pulse off time is 56 microsec, wire feed rate (WFR) is 4 m/min, and peak current (IP) is 11 amp. From the analysis of variance (ANOVA), it is observed that the pulse on time is the foremost influencing parameters that contribute towards GFRG by 52.61%, followed by the wire feed rate (WFR) 38.32% and the current by 5.45%.

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

“…WEDT was found to have advantages over the conventional turning process [269]. Many optimizations have been conducted to predict the machining performance or improve the machinability of SiC/Al MMCs, e.g., ANN-RSM [270], RSM [271][272][273][274], Taguchi's approach [275], Taguchi-based hybrid grayfuzzy grade approach [276], particle swarm optimization [277], AHP-TOPSIS (a hybrid approach obtained by integrating the AHP with TOPSIS technique) [278], and nondominated sorting genetic algorithm [279].…”

Investigation in Gas Carburizing of AISI 4140, EN36, and 16MnCr5 Steels Using the Grey Incidence-Based Taguchi (GIBT) Method

“…GRA could handle the uncertainties better by forming the grey relational grade which could be used as a single representative for the multiple responses [17]. GRA was used in its original form or in combination with other methods like TOPSIS, genetic algorithm, fuzzy, and back propagation networks in various instances of decision making and in different production processes to arrive at the optimal parameters, thus ensuring the possibilities of hybridization of techniques [18][19][20].…”

Comparative Analysis of Abrasive Wear Using Response Surface Method and Artificial Neural Network

“…Accurate prediction of wear behaviour by ANN provides an alternative to the current time-consuming testing methods. Since then, the technique has been successfully used in the tribology field, which includes wear of polymer composites [18][19][20][21][22], tool wear [23], online wear rating [24], brake performance [25,26], corrosion of polymers [27], wheel and rail wear [28], copper-aluminium nano-composite wear rate [29], wear of heattreated aluminium-clay composites [30] and wire electrical discharge process [31][32][33]. Bhaumik et al [34] implemented the technique for predicting the coefficient of friction for various friction modifiers.…”