Analysis and optimization of surface finish of wire electrical discharge machined miniature gears. (May 2014)

• Record Type: Journal Article
• Authors: Gupta, Kapil; Jain, Neelesh K
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 5(2014:May)
• Page Start: 673
• Page End: 681
• Publication Date: 2014-05
• Subjects: Miniature gears -- wire electrical discharge machining -- optimization -- surface roughness -- response surface methodology -- artificial neural network -- modeling; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405413508938
• Languages: English
• ISSNs: 0954-4054

Abstract

This article reports about analysis and optimization of surface roughness parameters (i.e. average roughness Ra and maximum roughness Rt ) of wire electrical discharge machined fine-pitch miniature spur gears made of brass. Effects of four wire electrical discharge machining process parameters (i.e. voltage, pulse-on time, pulse-off time and wire feed rate) on the surface roughness parameters of the miniature gears were studied by conducting 29 experiments with two replicates each and designed based on Box–Behnken approach of response surface methodology. Analysis of variance study found all four input parameters to be significant. Experimentally, the surface roughness has been found to increase with higher voltage and longer pulse-on time and decrease with longer pulse-off time and higher wire feed rate. Desirability analysis was used to optimize the wire electrical discharge machining parameters, so as to minimize the Ra and Rt simultaneously. Optimum values of Ra and Rt obtained from the confirmation experiments conducted at the optimized wire electrical discharge machining parameters are superior than the values reported in the literature. Artificial neural network model has been developed for prediction of the surface roughness of the wire electrical discharge machined miniature gears. Very close agreement was found among the surface roughness values predicted by response surface methodology and artificial neural network with the corresponding experimental values.