LAUSR

The objective of this research work was targeted to investigate the hybrid-statistical methodology to deduce the economicalmachining-condition through multi-output optimization in silicon carbide powder mixed-Electro Discharge Diamond Surface Grinding of Ti6Al4V. In this work, Grey-Fuzzy based principal component analysis along with Taguchi’s orthogonal array is utilised for the multi-output optimization of hybrid-machining process parameters for minimal surface roughness, minimal wheel-wear rate, and maximal material removal rate. Eighteen experiments have been conducted according to Taguchi’s L18 orthogonal array on in-house-designed and fabricated powder mixed-Electro Discharge Diamond Surface Grinding set-up. The single Multi-Output Performance Index is calculated by the aggregation of all multi-responses by using Grey-Fuzzy-Taguchi method based principal component analysis function. The optimum combination of process parameters and the effect of these parameters on the Multi-Output Performance Index are determined by the use of ANOVA analysis and response table. For the validation test, one additional confirmation experiment is conducted on this set-up according to the derived optimal condition and the outcomes of the results showed satisfactory matching between the predicted and experimented result. The specific contribution of this research work is to develop and describe the procedure of integrated statistical methodology for multi-output optimization of machining parameters in powder mixed-Electro Discharge Diamond Surface Grinding of Ti6Al4V. This integrated statistical approach hybridizes the concepts of Grey Relational Analysis, Fuzzy, principal component analysis and Taguchi approach to find the optimum combination of machining parameters for economical machining. This optimum combination of process parameters supports engineers to establish an economical and effective process.