Development of material removal rate model and performance evaluation of ultrasonic turning process

Abstract

ABSTRACT In the present research work, a predictive model of material removal rate (MRR) for a novel ultrasonic turning (UST) process was developed and analyzed. Experimentation was performed to examine the effect of UST process variables viz. workpiece rotation speed, tool diameter, abrasive size, concentration and power rating on MRR, tool wear rate (TWR), and surface roughness (SR) while turning of glass rod. The experimentation was planned and executed as per Taguchi’s L27 orthogonal array. In addition, the UST process parameters were optimized to obtain higher MRR and lower TWR and SR using Taguchi grey relational analysis. The ANOVA results showed that tool diameter was the most significant parameter, whereas power rating was the least significant parameter affecting the UST process. The optimum parametric combination obtained by Taguchi grey relational analysis exhibited in an overall enhancement of 36% in UST process performance. Both the predicted and experimental results showed an acceptable agreement. Further, the statistical analysis revealed that the developed model has a R-value of 0.9984 and MAPE of 1.25%. Using this model, the MRR in UST process can be estimated for other brittle materials also.