THE APPLICATION OF TAGUCHI S OPTIMIZATION METHOD IN WET TURNING OPERATION OF EN 19 STEEL

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1 International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN Vol. 3, Issue 2, Jun 2013, TJPRC Pvt. Ltd. THE APPLICATION OF TAGUCHI S OPTIMIZATION METHOD IN WET TURNING OPERATION OF EN 19 STEEL RAHUL DAVIS 1, VIVEK JOHN 2, VIVEK KUMAR LOMGA 3 & RAJA PAUL HORO 4 1,2 Assistant Professor, Department of Mechanical Engineering, SSET, SHIATS, Allahabad, Uttar Pradesh, India 3,4 B.Tech Mechanical Engineering, Department of Mechanical Engineering, SSET, SHIATS, Allahabad, Uttar Pradesh, India ABSTRACT The present work is associated with turning operation of En-19 steel. The paper represents the influences of five different cutting parameters like pressurized coolant jet, rake angle, depth of cut, spindle speed and feed rate on the surface roughness of the En-19 steel. In the experiment Taguchi technique was used to calculate the various readings by using MINITAB15 software. Orthogonal L16 array was used and signal to noise ratio and the analysis of variance (ANOVA) are employed to interpret the cutting parameters. The carbide tipped tool having negative and positive rake angle according to the combination of the experiment was used. The measured results were then collected and analyzed with the help of software called MINITAB15. The experiment setup included spindle speed of 780 and 1560 rev/min, pressurized coolant jet of 0.5 and 1 bar, rake angle 4 and 7 degrees, depth of cut of 0.5 and 1 mm and feed rate 0.16 and 0.8 mm/rev. At last confirmation test was done to compare the value with final outcome to confirm the effectiveness of the surface roughness of En-19 steel. KEYWORDS: EN-19 Steel, Turning Operation, Surface Finish, Taguchi Method INTRODUCTION Highly competitive market requires high quality products at minimum cost, products are manufactured by the transformation of raw materials. Industries in which the cost of raw material is a big percentage of the cost of finished goods, higher productivity can be achieved through proper selection and use of the materials. Surface roughness has a great impact and therefore industries are focusing on achieving high quality surface finished products by measuring surface finish of the material, machinability of the material can be determined. It greatly affects the performance of manufacturing cost and mechanical parts as well, En-19 is a high quality, high tensile alloy steel usually ready to machine, giving good ductility and shock resisting properties combined with resistance to wear 1. In the Taguchi design of experiment method the parameters factors which can be controlled and noise factors which can t be controlled and which influence product qualities are considered 2. EN-19 was originally introduced for the use in the machine tool and motor industries for gears, pinions, shaft, spindles, now widely used in areas like oil and gas industries 2. A considerable number of studies have investigated the general effects of various cutting parameters on the surface roughness 3. Application of taguchi design of experiment is a very simple and valuable equipment to calculate surface roughness by acquiring the data, by performing the 16 experiment on lathe machine with five different parameters. Surface finish of the material depends upon feed, nature of working material, hardness of the material, cutting speed, depth of cut, cutting timing, nose radius of the tool and workpiece arrangement, chattering, and use of cutting fluids. Taguchi method 4 consist of a plan of experiments with the objective of acquiring data in a controlled way, executing these experiments and analyzing data, in order to obtain information about the behaviour of a given process. It uses orthogonal arrays to define the experimental plans and the

2 194 Rahul Davis, Vivek John, Vivek Kumar Lomga & Raja Paul Horo treatment of the experimental results is based on the analysis of variance (ANOVA) 5. METHODS This experimental setup consists of using L16 orthogonal array of taguchi technique to study the five different cutting parameters, of the specimen En-19 steel. The turning operation trial had been done 16 times and measurement of the surface roughness is taken from the specimen. The setup was established at Sparko engineering works kareilly Allahabad where having lathe machine was used for the experimental work. The measurement were taken by dividing the length of the specimen into five equal parts and it is found to be (38) mm around each workpiece. The turning operation were performed by using carbide tipped tool having positive and negative rake angle and pressurized coolant jet is used. EN-19 steel with carbon 0.39 % was selected as the specimen material. In proposed work, EN19 steel with carbon (0.39%), Silicon (0.2 %), Chromium (1.2 %) and Molybdenum (0.3 %) was selected as the specimen material. The values of the input process parameters for the Turning Operation are as under: Table 1: Details of the Turning Operation Factors Level 1 Level 2 Depth of cut (mm) Feed (mm/rev) Spindle speed (rpm) Pressurized Coolant Jet (bar) Rake angle (degrees) In the present experimental work, the assignment of factors was carried out using MINITAB-15 Software. The trial runs specified in L16 orthogonal array were conducted on Lathe Machine for turning operations. S.No. Table 2: Results of Experimental Trial Runs for Turning Operation Feed Rate (mm/rev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (Degrees) Pressurized Coolant Jet (Bar) Surface Roughness (µm) SNRA

3 The Application of Taguchi s Optimization Method in Wet Turning Operation of En 19 Steel 195 Table 3: ANOVA Table for Means Factors DF Seq SS Adj SS Adj MS F P Feed Rate (mm/rev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (Degrees) Pressurized coolant jet (bar) Error Total Table 4: ANOVA Table for Signal to Noise Ratio Factors DF Seq SS Adj SS Adj MS F P Feed Rate (mm/rev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (Degrees) Pressurized coolant jet (bar) Error Total Levels1 Feed Rate (mm/rev) (A) Table 5: Response Table for Signal-to-Noise Ratio Spindle Speed (rpm) (B) Depth of Cut (mm) (C) Rake Angle (Degrees) (D) Pressurized Coolant Jet (Bar) (E) Delta Rank From Table 4, Optimal Parameters for Turning Operation were A 1, B 2, C 2, D 1 and E 2. Therefore the Predicted value of SN Ratio for Turning Operation can be obtained using the predictive equation η p (Surface Roughness) = [ (-35.64)]+[ (35.64)]+[ (-35.64)]+[ (-35.64)]+[ (-35.64)] = RESULTS The Comparison of F values of ANOVA Table 4 of Surface Roughness with the suitable F values of the Factors (F 0.05;1;10 = 4.96) for 95% confidence level respectively depicts that the all these factors are insignificant. Figure 1: Main Effects Plot for Means

4 196 Rahul Davis, Vivek John, Vivek Kumar Lomga & Raja Paul Horo Main Effects Plot for Means Figure 1 shows the effect of the each level of the three factors on surface roughness for the mean values of measured surface roughness at each level for all the 16 trial run Main Effects Plot for SN Ratio Figure 2: Main Effects Plot for S/N Ratio Figure 2 shows the effect of the each level of the three factors on surface roughness for the mean values of obtained SN ratio at each level for all the 16 trial runs Table No. 5 indicates the results of Signal-to-Noise ratio for Surface Roughness. Comparing the F values of ANOVA Table No. 4 of Surface Roughness with the suitable F values of the Factors (F 0.05;1;10 = 4.96) for 95 % confidence level, depicts that all these factors are insignificant factor. DISCUSSIONS From Table 6, Figure 1 and Figure 2, optimal parameters for Surface Roughness are first level of feed rate (0.16 mm/rev), second level of spindle speed (780 rpm), second level of depth of cut (1.0mm), first level of rake angle (4 0 ), second level of pressurized coolant jet(1.0 bar). Therefore the combination of the optimal level of the factors is found in third trial of Table No. 2, that will give the minimum surface roughness. below Few results of the confirmation test of the same combination of the optimal levels of the parameters are given CONCLUSIONS Table 6: Results of the Confirmation Tests of the Optimal Levels of the Factors S.No. Feed Rate (mm/rev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (Degrees) Pressurized Coolant Jet (Bar) Surface Roughness (µm) The Optimization of the control input parameters was done using Taguchi method and the optimal value was predicted using a predictive equation. A Validation test was then done to confirm the obtained results, which resulted in the confirmation that the selected parameters and predictive equation were genuine.

5 The Application of Taguchi s Optimization Method in Wet Turning Operation of En 19 Steel 197 All the results attained by the present research work can be proposed to achieve the minimum surface roughness for further similar research works. In the present research work, the specimen material used is EN19 with 0.39% carbon content. The experimentation can also be done on other materials also. The present research work can be done along with all the possible chosen interactions among the different levels of the parameters in order to expand the study. REFERENCES 1. Palanikumar, L. Karunamoorthy, R. Krathikeyan (2006), Assessment Journal of Materials and Design, 27, Jeyapaul R., Shahabudeen P., Krishnaiah K. (2006), Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm, International Journal of Journal Advanced Manufacturing Technology 30: 870:878, DOI /s Copyright china longhair special steel Co. LTD. 4. West yorkshire steel LtdSandbeck industrial Estate, Wetherby, leeds 7DN UK. 5. Website: 6. Website: 7. Diwakar Reddy.V, Krishnaiah.G. et al (2011), ANN Based Prediction of Surface Roughness in Turning, International Conference on Trends in Mechanical and Industrial Engineering (ICTMIE'2011) Bangkok 8. Mahapatra, S.S. et al (2006). Parametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method, Proceedings of the International Conference on Global Manufacturing and Innovation - July Ross, Philip J. (2005). Taguchi Techniques for Quality Engineering, Tata McGraw-Hill Publishing Company Ltd 10. Raghuwanshi, B. S. (2009). A course in Workshop Technology Vol.II (Machine Tools), Dhanpat Rai & Company Pvt. Ltd Suhail, Adeel H. et al (2010). Optimization of Cutting Parameters Based on Surface Roughness and Assistance of Workpiece Surface Temperature in Turning Process, American J. of Engineering and Applied Sciences 3 (1): Van Luttervelt, C. A. et al (1998). Present situation and future trends in modelling of machining operations, CIRP Ann. 13. Kirby, Daniel (2010). Optimizing the Turning Process toward an Ideal Surface Roughness Target. 14. Selvaraj, D. Philip et al (2010). optimization of surface roughness of aisi 304 austenitic stainless steel in dry turning operation using Taguchi design method Journal of Engineering Science and Technology,Vol. 5, no , school of engineering, Taylor s university college. 15. Kirby, E. Daniel (2006). Optimizing surface finish in a turning operation using the Taguchi parameter design method Int J Adv Manuf Technol:

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