Response Surface Modeling and Optimization of Electro-Discharge Machining of Al/Al2O3p

Abstract

In this study, wire electrical discharge machining of aluminium matrix composites has been experimentally investigated. A metal matrix composite Al/Al2O3p/10% has been machined at various combinations of machining parameters such as pulse on-time, off-time, wire speed and wire tension. Empirical models have been developed to predict the cutting rate, surface roughness and kerf width of the machined composite material. The models were developed by using the technique of response surface methodology. The experiments were conducted by using carefully planned response surface design matrix (central composite design). The model predictions are in good agreement with the experimental results. The R2 values of the proposed cutting rate, and kerf width models are above 0.99 and 0.98 respectively, where as it is 0.95 for the surface roughness model. The pulse on-time was found to influence cutting rate and surface finish significantly. Kerf width was significantly affected by pulse on-time, off-time, wire speed, and wire tension. In addition, a model was developed to correlate the multiple performance characteristic called as grey relational grade and the process parameters. Thus, a new combination of response surface method and grey relational analysis is proposed. The grey relational grade was significantly affected by pulse on-time and off-time time. The R2 value for the grey relational grade model was 0.94. Optimal parameter setting was determined for the multiple performance characteristic. The improvement in cutting rate was more than 100% compared to the ‘0’ level experiments, with reasonably smooth surfaces and narrow kerf width.

Machining of Inserts with PCD Cutting-Edge Technology and Determination of Optimum Machining Conditions Based on Roundness Deviation and Chip-Cross Section of AW 5083 AL-Alloy Verified with Grey Relation Analysis

“…GRA is a method by which we measure the degree of approximation between sequences according to the Grey relational grade (GRG). This method has been applied by several researchers [35][36][37].…”

Response Surface Modeling and Grey Relational Analysis to Optimize Turning Parameters with Multiple Performance Characteristics

“…After, that calculating the weighting factor of each performance characteristic by principal component analysis technique, since the multiple performance characteristic, grey relational grades determined by using weighting method. Then, the grey relational grades were computed by averaging the grey relational coefficient corresponding to each performance characteristic (Brahmankar, 2009). The overall equation of the multiple performance characteristic is based on the grey relational grade.…”

“…Data pre-processing is a means of transferring the original sequence to a comparable sequence. Depending on the characteristics of a Data sequence, there are various methodologies of Data pre-processing available for the gray relational analysis (Brahmankar, 2009).…”

“…Lin et al (2007) used the grey relational analysis based on an orthogonal and fuzzy-based Taguchi method for optimizing a multi-response electrical discharge machining process. Brahmankar et al (2009) used new combination of response surface method and grey relational analysis to optimize Electrodischarge machining parameters with multiperformance characteristics. Siddiquee et al (2010) used grey relational analysis coupled with principal component analysis for optimization design of the process parameters in in-feed centre less cylindrical grinding.…”

Determination of optimum parameters for multi-performance characteristics in turning by using grey relational analysis

“…Lin et al [28] used the grey relational analysis based on an orthogonal and fuzzy-based Taguchi method for optimizing a multiresponse electrical discharge machining process. Brahmankar et al [29] used a new combination of response surface method and grey relational analysis to optimize electro-discharge machining parameters with multi-performance characteristics.…”