Turning SKD 11 Hardened Steel: An Experimental Study of Surface Roughness and Material Removal Rate Using Taguchi Method

Abstract

Heat-treated steel is widely used in industrial applications due to its high strength and other desirable mechanical qualities. Grinding, which requires a lot of power and is expensive, is typically used to harden machining. In recent times, hard machining has emerged as a viable alternative to grind in select applications. In this investigation, turning operations with a carbide insert (CNMA 120408-KR3215) were carried out on SKD 11 (53 HRC) hardened steel. A total of nine machining tests were completed using the L9 orthogonal array. The response variables considered in this study were surface roughness (Ra) and material removal rate (MRR). The analysis of the signal to noise ratio reveals that the optimal combination of cutting process parameters for achieving a desired surface roughness consists of a cutting speed of 119 m/min, a feed rate of 0.11 mm/rev, and a depth of cut of 0.2 mm. The contribution of each process parameter to the machining performance of the carbide tool-work piece combination is determined through the use of ANOVA. Depth of cut has the greatest impact (57.33%) to MRR, while feed rate has the highest contribution (82.15%) to Ra. Moreover, desirability function analysis (DFA) was conducted to optimize the multiple responses. DFA suggested that, to attain a satisfactory response to the output parameters, higher range of cutting speed, depth of cut, and lower range of feed rate are appreciable; therefore, the analytical findings suggest that a cutting speed of 189 m/min, feed rate of 0.11 mm/rev, and a depth of cut of 0.5 mm can induce a favorable Ra of 0.971 μm and MRR of 10.248 cm3/min. In hard machining, cutting speed has a bigger influence on surface finish than feed rate.

Wavelets and Artificial Neural Networks for Surface Roughness Estimation of Ceramic Tiles

“…The results in the table 2 gives the estimated values of Arithmetic Average Roughness for different surfaces based the values of standard deviation, Entropy, Ea, Ed, Eh and Ev are taken from the table 1. This Ra [22]can be obtained using the neural network model. The results in the table 3 gives the values of Root Mean Square Roughness, Skewness, and Kurtosis from Arithmetic Average Roughness.…”