International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Optimization of Cutting Parameters for surface roughness and MRR in CNC Turning of 16MnCr5 Jitendra Kumar Verma M. Tech Student, Department of Mechanical Engineering, Geeta Engineering College, Panipat, Haryana, India ------------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The aim of this research work is to investigate the effects of cutting parameters such as cutting speed, feed
rate and depth of cut on surface roughness and MRR in CNC dry turning of 16MnCr5 material which is case hardening steel with the help of Design of Experiments. Experimental work has been carried out based on Taguchi L 9 orthogonal array design with three cutting parameters. Optimal cutting conditions for surface roughness and MRR were determined using the signal-to-noise (S/N) ratio which was calculated for roughness average (R a) and MRR according to the smallerthe-better and larger the better approach. The experimental results were analyzed by using main effects plots and response tables for S/N ratio. Result of this study shows Depth of cut has highest effect on surface roughness followed by speed and Feed rate has lowest effect on surface roughness. Keywords: Turning, Surface Roughness, MRR, Taguchi, Regression, ANOVA
1. INTRODUCTION Quality of a machine part depends on parameters like surface roughness, dimensional accuracy, tolerance zone etc. A component which has good surface finish always subjected to low wear and tear during its functioning and also offer low friction between two touching surfaces because it provides low value of friction coefficient and reduces the requirement of lubrication, and generates low heat, means chances to change property of component due to heating reduces. For reducing machining time and cost of production it is necessary that the material removal rate (MRR) during machining should be high. Due to all of the above reasons a manufacturer always tries to produce a machine component with minimum surface roughness and high MRR. Here we are going to obtain best combination of cutting parameters namely cutting speed, feed, and depth of cut for dry CNC turning operation to produce minimum surface roughness and high MRR with the help of Design of Experiments for 16MnCr5 material which is a case hardening steel. Garcia et al. [1] worked to investigate the effect of coating of the cutting tool and cutting parameters on the surface residual stresses generated in AISI 4340 steel due to turning operation. The results concluded that with increase in cutting temperature the residual stresses became more tensile due to which surface roughness increases. They concluded that high cutting speed, low feed rate, and tools without coating with small nose radius must be use to obtain a good surface finish. Saurabh Singhvi et al. [2] optimized the cutting parameter namely rake angle and feed for MRR by using Taguchi method and tey conclude that MRR increase with increase in feed and decrease with increase in rake angle Y. Kevin Chou, Hui Song [3] established a model to analyze the chip formation forces. Assuming quadratic decay of stresses in the wear land forces and linear development of plastic zone on the wear land are modelled. Increasing cutting speed and feed rate adversely affect maximum temperature of machined surface in new cutting tool but increasing depth of cut favourably affect the maximum temperature of machined surface. Er. Sandeep Kumar et al. [4] investigated the effects of speed feed and depth of cut on MRR during CNC turning of Mild steel 1018 by using Taguchi method and their result shows feed rate has highest influence on MRR. Ilhan Asilturk et al. [5] investigated the effects machining parameters such as feed rate, cutting speed and depth of cut on the surface roughness of AISI 1040 steel. It was used full factorial design of experiment, Artificial Neural Networks (ANN) and multiple regression approaches. It has been finalised that, the formulated models are able to predict the surface roughness very well. The artificial neural network model estimates the surface roughness with high accuracy. Asilturk and Akkus[6] investigated for optimization of cutting parameters cutting speed, feed and depth of cut in dry turning of hardened AISI 4140 steel by using coated carbide cutting inserts. They used Taguchi method of design of experiment. They measured roughness average value of surface roughness and use it as experiments output data and analysed these data with input parameters data by using Taguchi method. They concluded that the feed rate has the highest significant effect on surface roughness followed by other cutting parameters. Mandal et al. [7] used Taguchi method of design of experiments and regression analysis to analyse the machining property of AISI 4340 steel in terms of cutting parameters and their effects on machining. They used Zirconia Toughened Alumina ceramic inserts. Their final results concluded that the most contributing cutting factors for tool flank wear are depth of cut and speed. The feed rate has least effect on the flank wear. A. Mohanty et al. [8] performed an experimental research for MRR, surface roughness and microstructure in Electrochemical machining of Inconel 825 with the help of Taguchi and ANOVA by considering electrolyte concentration, voltage and feed as factors for design of experiments. Finally they concluded that voltage significantly affecting the MRR and surface roughness. Aouici et
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