Cutting Parameters Optimization in CNC Turning Operation for Controlled Surface Roughness and Higher Material Removal Rate using Design of Experiment Approach

I. INTRODUCTION

Under the Make in India initiative, the Government of India aims to increase the share of the manufacturing sector in terms of the gross domestic product (GDP) to 25 per cent by 2022, from 16 per cent recently. It is also expected to create 100 million new jobs by 2022 showing the positive response on Make in India Initiative [1]. The engineering sector, being closely associated with the manufacturing and infrastructure sectors of the economy, is of strategic importance to India‘s economy. According to the top economists, mechanical industries and manufacturing play a crucial role in GDP of any country either economically developed or developing country [2]. Throughout the world, all the industrial sectors are facing a new challenge of customized demand. To fulfill the customer need in due time is a great challenge for all kind of industries today. To accomplish these needs industries are trying to evolve new techniques and flexibility in manufacturing system. Companies throughout the world have embraced ‗Mass Customization‘ in an attempt to provide unique value to their customers in an efficient manner. Thus, with ‗Mass Customization‘, the manufacturing enterprises are forced to make use of the modern machineries with programmable operation and high rate of output. Thus, present work is focused on use of CNC Cell. Manufacturing has conventionally played a major role in the economic growth of developing countries. Kaldor (1967) [3] characterized the manufacturing sector as ―The main engine of fast growth.‖ Meng Q. et al. (2000) [4] studied for calculating the optimum cutting conditions in turning for minimizing the cost or maximizing the production rate. In their work, modified form of Taylor tool life equation was used for predicting response values and optimizing its value. Robust design for the engineering is a better methodology for obtaining best set of results which are minimally sensitive to the numerous causes of variation to produce best quality products at least cost. Taguchi and ANOVA parameters are important tool for such kind of robust design which controlling the design parameters in several experimental runs. Taguchi design can optimize the results through setting of design parameters as per requirement11. On the other hand, ANOVA is employed to recognize the most significant variables and their interaction effects along with their percentage contribution.

Various factors have been considered by numerous researchers and scientists to study their effect on surface roughness. Sunderam & Lambert12-13

considered six controlled factors which may affect the surface roughness i.e. speed, depth of cut, feed, time of cut, nose radius and type of tool. But finally it is found that the three factors i.e. spindle speed, feed rate and depth of cut can affect the surface roughness14-18. Mukharjee et al., (2006) [19] worked for optimization of Material removal rate using SAE 1020 material in CNC turning process. In his study three cutting parameters were used namely feed rate, depth of and spindle speed which are the highly contributing parameters in machining process. The present research work describes about how to select the controlled factors (spindle speed, depth of cut and feed rate) that can minimize the effect of noise factor on the response (surface roughness and MRR).

After final finish cut, the surface roughness was measured for each work piece along the circumference using Mitutoyo Surface roughness measuring tester SJ-210. Measurements were obtained with the help of movement of stylus with

The schedule of runs was created at various combinations according to full factorial design of experiments specified in Minitab Statistical software. The work pieces from the bar were turned with specified cutting conditions. After completion of all the runs, the surface roughness of all the work pieces was measured. Data processing and its analysis were performed through MINITAB-18 statistical software.

For Taguchi analysis, experimental results of surface roughness and MRR are transformed into Signal to Noise (S/N) ratio. For this experimental analysis, the smaller is better was employed to calculate S/N ratio of surface roughness and larger is better was employed for MRR. Furthermore, variance analysis was performed for collected data to analyze the results for Ra and MRR.

III. CONCLUSIONS

Finally, it can be concluded that, it is impossible to achieve surface roughness in controlled manner or higher MRR by ‗Trial and Error‘ method. It needs an in-depth analysis of the parameters affecting it. As identified from the results, it is concluded that Surface roughness is highly affected by Feed rate followed by depth of cut and spindle speed and Coefficient of determination is also significant i.e. 87.41 % which shows a best set of parameters. In similar manner conclusions are drawn for MRR which shows that MRR is highly affected by Feed rate followed by depth of cut and spindle speed and Coefficient of determination using ANOVA table for MRR is found to be 90.59% showing high level of significance.

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Department of Mechanical Engineering, SS College of Engineering, India