30120140504018

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30120140504018

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 147 COMPARISON FOR SURFACE ROUGHNESS BETWEEN DUPLEX TURNING WITH SINGLE TOOL TURNING WITH HELP OF SPSS SOFTWARE MANISH KUMAR YADAV Research Scholar, ME Deptt, Sharda University, G.Noida U.P. India ABSTRACTS The present paper attempts to concentrate develop comparison between duplex turning with single tool turning with help of SPSS software. The present paper will attempt to explain the role of coefficient of determination, R-Squared which signifies that variance in dependent variable (DV i.e. Surface Roughness) can be explained more precisely while using duplex machining process as compared to single machining and also explain adjusted R-square. This paper also tell the effect of the number of independent variables (IVs) in a multiple regression model makes the R-square larger. Keywords: Surface Roughness, R-Square. 1. INTRODUCTION In manufacturing there are some criteria on the basis of which the various control parameters can be selected in order to attain the desired level of the surface finish on the material. During actual machining operation there are various factors which adversely affect the finish and therefore, the proper methological consideration of these factors appear to be most crucial for achieving the proper and desired level of surface finish. Preliminary shaping gives an opportunity to provide the proper and desired shape and is treated as the first step of a manufacturing process. Casting, molding, forging, welding etc can provide the shape to the material. Further, different machining operation like turning, shaping, planning, milling, drilling, etc brings the dimensions of the part under manufacturing to a proper size. In many applications particularly when the part is made of some brittle material the scratches found on the surface becomes the source of stress concentration and this may lead to initiation of crack- propagation and finally to the failure of the part while in operation. INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2014): 7.5377 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 148 Therefore it is important to achieve good surface finish of such parts because this improve its strength as well as the life mostly if the loading condition is dynamic and repeated in nature. 2. LITERATURE REVIEW A number of studies have been made to investigate the surface finish. It has been seen that Lin, W.S. (1) have studied the study of high speed fine turning of austenitic stainless steel. Sze-Wei, Gan and Han-Seok, Lim and Rahman, M Frank Watt(2) have discussed a fine tool servo system for global position error compensation for a miniature ultra-precision lathe. Vikram Kumar, CH R. and Ramamoorthy, B (3) have dealt with Performance of coated tools during hard turning under minimum fluid application. Further Sharma, D.K. and Dixit, U.S.(4) have compared the dry and air- cooled turning of grey cast iron with mixed oxide ceramic tool. Go¨kkaya, Hasan and Nalbant, Muammer(5) have studied The effects of cutting tool geometry and processing parameters on the surface roughness of AISI 1030 steel. However, Isik, Yahya(6) have investigated the machinability of tool steels in turning operations. Dhar, N.R_ and Ahmed, M.T. and Islam, S.(7), did an experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel and Chang, Chih-Wei and Kuo, Chun-Pao (8) have attempted to evaluate surface roughness in laser- assisted machining of aluminum oxide ceramics with Taguchi method. It is to be noted that all the above investigators have reported their results for single tool surface finish operation only. And Yadav, Manish kumar and Sinha, Dr.P.K and Sinha, Dr.Gopal P.(10) did an analysis on single tool turning with duplex turning. In the proposed work an attempt has been made to compare the effect of cutting parameters on surface roughness in multi tool turning and single tool turning with the help of SPSS on AISI-1018. 3. FACTORIAL DESIGN OF EXPERIMENT Factorial Design of the experiment is the method to recognize the significant factors in a process, make out and fix the problem in a practice, and also identify the possibility of estimating interactions. This is done using a full factorial DOE. A two level factorial DOE has been used. This means two levels of each factor will be studied at once. If there are K factors that we need to evaluate in a process we need to run the experiment 2k times. Each factor will have two levels, a “high” and “low” level. 4. EXPERIMENTATION If two tools, both side of the bed on the lathe carriage and mounted in such a way that both tool moving in one direction then this is term as Duplex attachment or Duplex turning. Duplex turning provide two tool cutting operation moving in one direction. Experiments are conducted in accordance with the statistical technique of experimental design.
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 149 4.1. EXPERIMENTAL SETUP Fig 1: Attachment for Duplex turning on Lathe machine 5. CUTTING PARAMETERS AND TOOL Feed, Depth of cut, Cutting speed, HSS tool 6. ANALYSIS OF EXPERIMENTAL DATA Table 1: Comparison between Single tool and Duplex machining roughness S.NO. Single tool machining Duplex Machining F D S R1 F D S R2 1 0.071 0.8 180 4.3 0.071 0.4+0.4 180 2.7 2 0.14 0.8 180 6.3 0.14 0.4+0.4 180 3.8 3 0.071 1.4 180 5.9 0.071 0.7+0.7 180 4.3 4 0.14 1.4 180 6.0 0.14 0.7+0.7 180 4.0 5 .071 0.8 280 4.1 0.071 0.4+0.4 280 2.2 6 0.14 0.8 280 5.4 0.14 0.4+0.4 280 3.5 7 0.071 1.4 280 5.6 0.071 0.7+0.7 280 3.7 8 0.14 1.4 280 5.8 0.14 0.7+0.7 280 3.3
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 150 Tests of Between-Subjects Effects Dependent Variable:Roughness_SINGLE Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 4.435a 6 .739 9.240 .247 Intercept 235.445 1 235.445 2943.063 .012 FEED_SINGLE 1.620 1 1.620 20.250 .139 DEPTH_SINGLE 1.280 1 1.280 16.000 .156 SPEED_SINGLE .320 1 .320 4.000 .295 FEED_SINGLE * DEPTH_SINGLE 1.125 1 1.125 14.063 .166 DEPTH_SINGLE * SPEED_SINGLE .045 1 .045 .562 .590 FEED_SINGLE * SPEED_SINGLE .045 1 .045 .563 .590 Error .080 1 .080 Total 239.960 8 Corrected Total 4.515 7 a. R Squared = .982 (Adjusted R Squared = .876) Tests of Between-Subjects Effects Dependent Variable:ROUGHNESS_DUPLEX Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 3.347a 6 .558 49.593 .108 Intercept 94.531 1 94.531 8402.778 .007 FEED_DUPLEX .361 1 .361 32.111 .111 DEPTH_DUPLEX 1.201 1 1.201 106.778 .061 SPEED_DUPLEX .551 1 .551 49.000 .090 FEED_DUPLEX * DEPTH_DUPLEX 1.201 1 1.201 106.778 .061 DEPTH_DUPLEX * SPEED_DUPLEX .031 1 .031 2.778 .344 FEED_DUPLEX * SPEED_DUPLEX .001 1 .001 .111 .795 Error .011 1 .011 Total 97.890 8 Corrected Total 3.359 7 a. R Squared = .997 (Adjusted R Squared = .977) 7. RESULT AND DISCUSSION So in case of single machine experiment the coefficient of determination, R squared=98.2%, whereas in case of duplex machine experiment R squared =99.7%.Which signifies that variance in dependent variable (DV i.e. Surface Roughness) can be explained more precisely while using duplex machining process as compared to single machining. One important indicator which suggest that duplex machining procedure more appropriately explained the roughness, is adjusted R-square. The number of independent variables (IVs) in a multiple regression model makes the R- square larger. Hence R square increases because of the total number of IVs and not because of added IVs is a good predictor of DV. To address such effects adjusted R-square is considered.
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 151 In single machine experiment adjusted R-square= 87.6 % in comparison to R-suaqre= 98.2%, which shows a difference of 10.6%. But in case of duplex machining experiment adjusted R-square is = 97.7 %, in comparison to R-square= 99.7%, which shows a difference of only 2 %. So we can conclude that duplex machining process gives more better and consistent outcome as comparison to single machining process. From the following interaction diagrams it is also clear that duplex machining reduces (almost removed) the interaction between Feed and Speed IVs, as comparison to single machining process.(A parallel graph shows no interaction between variables)
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online), Volume 5, Issue 4, April (2014), pp. 147-152 © IAEME 152 REFERENCES (1) Lin W.S. “The study of high speed fine turning of austenitic stainless steel”, Journal of Achievements in Materials and Manufacturing Engineering, vol-27(2008). (2) Sze-Wei Gan, Han-Seok Lim, Rahman M., Watt Frank “A fine tool servo system for global position error compensation for a miniature ultra-precision lathe”, International Journal of Machine Tools & Manufacture 47 (2007) 1302–1310. (3) Kumar Vikram, R. CH and Ramamoorthy, B., “Performance of coated tools during hard turning under minimum fluid application” Journal of Materials Processing Technology 185 (2007) 210–216. (4) Sarma, D.K and Dixit, U.S, “A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool”, Journal of Materials Processing Technology 190 (2007) 160–172. International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012 16 ISSN 2229-5518 IJSER © 2012 http ://www.ijser.org. (5) Hasan and Nalbant, Muammer, “The effects of cutting tool geometry and processing parameters on the surface roughness of AISI 1030 steel” Gokkaya, Materials and Design 28 (2007) 717–721. (6) Isik,Yahya, “Investigating the machinability of tool steels in turning operations”, Materials and Design 28 (2007) 1417–1424. (7) Dhar, N.R. and Ahmed, M.T. and Islam, S. “An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel”, International Journal of Machine Tools & Manufacture 47 (2007) 748–753. (8) Chang, Chih-Wei and Kuo, Chun-Pao, “Evaluation of surface roughness in laser-assisted machining of aluminum oxide ceramics with Taguchi method”, International Journal of Machine Tools & Manufacture 47 (2007) 141–147. (9) Yadav, Manish kumar and Sinha, Dr.P.K. and Sinha, Dr. Gopal P. “Development of Techniques Using Design of Experiments for Acquiring Appropriate Surface Finish by Multi Tool Machining”, VSRD International Journal of Mechanical, Auto. & Prod. Engg. Vol. 2 (4), 2012 (10) Yadav, Manish kumar and Sinha, Dr.P.K. and Sinha, Dr. Gopal P. “A comparative study of surface roughness in Multi tool turning with single tool turning through factorial design of experiments” International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012. (11) Montgomery, Douglas C..; “Design of Experiments”, published by John Wiley and Sons (Asia) Pvt. Ltd., Page 223-236S, 243. (12) Tejinder pal singh, Jagtar singh, Jatinder madan and Gurmeet kaur, “Effects of Cutting Tool Parameters on Surface Roughness”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 1, Issue 1, 2010, pp. 182 - 189, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. (13) A. Hemantha Kumar, Krishnaiah.G and V.Diwakar Reddy, “Ann Based Prediction of Surface Roughness in Turning”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 162 - 170, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. (14) Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning Parameters Optimization for Surface Roughness by Taguchi Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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