DEFORM 3D Simulations and Taguchi Analysis in Dry Turning of 35CND16 Steel

Abstract

Steel (35CND16) has excellent strength with good hardenability and dimensional stability, and it could be widely used in engineering, mining, and tooling. The present study focused on minimizing cutting forces, flank wear, and temperature generation in the machining zone. The machining process factors include cutting speed, feed rate, and depth of cut. DEFORM 3D simulation outputs closely agreed with the experimental results. The predictive model developed by DEFORM 3D can predict the cutting force and temperature before the actual experiment; therefore, the machining cost can be avoided, which would incur due to improper selection of machining factors. Further, the machining factors were optimized based on ANOVA and regression analysis. Flank wear was increased at high level factors of speed and feed; however, flank wear tends to reduce at the middle level of depth of cut. The average percentage error for cutting force and temperature generation between experimental values and simulated values for force and temperature at machining zone was found to be 2.21% and 1.22%, respectively.