International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN 2249-6890 Vol. 3, Issue 2, Jun 2013, 193-198 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
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) 0.5 1.0 Feed (mm/rev) 0.16 0.8 Spindle speed (rpm) 760 1580 Pressurized Coolant Jet (bar) 0.5 1.0 Rake angle (degrees) 4 0 7 0 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) SNRA1 01 0.16 780 0.5 4 0 0.5 44.2-32.9084 02 0.16 780 0.5 7 0 1.0 55.3-34.8545 03 0.16 780 1.0 4 0 1.0 42.5-32.5678 04 0.16 780 1.0 7 0 0.5 133.5-42.5096 05 0.16 1560 0.5 4 0 1.0 27.5-28.7867 06 0.16 1560 0.5 7 0 0.5 95.0-39.5545 07 0.16 1560 1.0 4 0 0.5 87.4-38.8302 08 0.16 1560 1.0 7 0 1.0 49.8-33.9446 09 0.80 780 0.5 4 0 1.0 40.0-32.0412 10 0.80 780 0.5 7 0 0.5 47.5-33.5339 11 0.80 780 1.0 4 0 0.5 25.0-27.9588 12 0.80 780 1.0 7 0 1.0 35.0-30.8814 13 0.80 1560 0.5 4 0 0.5 129.6-42.2521 14 0.80 1560 0.5 7 0 1.0 122.5-41.7627 15 0.80 1560 1.0 4 0 1.0 75.0-37.5012 16 0.80 1560 1.0 7 0 0.5 105.0-40.4238
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) 1 123 123 123 0.11 0.750 Spindle Speed (rpm) 1 4516 4516 4516 3.92 0.076 Depth of Cut (mm) 1 4 4 4 0.00 0.952 Rake Angle (Degrees) 1 1858 1858 1858 1.61 0.233 Pressurized coolant jet (bar) 1 3014 3014 3014 2.62 0.137 Error 10 11510 11510 1151 Total 15 21025 Table 4: ANOVA Table for Signal to Noise Ratio Factors DF Seq SS Adj SS Adj MS F P Feed Rate (mm/rev) 01 0.36 0.36 0.36 0.02 0.893 Spindle Speed (rpm) 01 80.10 80.10 80.10 4.28 0.065 Depth of Cut (mm) 01 0.07 0.07 0.07 0.00 0.952 Rake Angle (Degrees) 01 37.88 37.88 37.88 2.02 0.185 Pressurized coolant jet (bar) 01 41.06 41.06 41.06 2.19 0.169 Error 10 187.17 187.17 187.72 Total 15 346.64 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) 1-35.49-37.88-35.71-34.11-37.25 2-35.79-33.41-35.58-37.18-34.04 Delta 00.30 04.48 00.13 03.08 03.20 Rank 4 1 5 3 2 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+[-35.49-(-35.64)]+[-33.41-(35.64)]+[-35.58-(-35.64)]+[-34.11-(-35.64)]+[-34.04-(-35.64)] =-41.18. 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
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) 01 0.16 780 1.0 4 1.0 42.5 02 0.16 780 1.0 4 1.0 42.3 03 0.16 780 1.0 4 1.0 41.9 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.
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