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  1. 1. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X OPERATIONAL MODELING FOR OPTIMIZING SURFACE ROUGHNESS IN MILD STEEL DRILLING USING TAGUCHI TECHNIQUE Dinesh Kumar Assistant Professor, Department of Mechanical Engineering, E-max institute of Engineering & Technology, Ambala, Haryana L.P.SinghAssistant Professor, Department of Industrial & Production Engineering, NIT Jalandhar, Punjab Gagandeep Singh Assistant Professor, Department of Mechanical Engineering, Haryana Engineering College, Jagadhri, Haryana. ABSTRACTThis investigation presents a Taguchi technique as one of the method for minimizing the surfaceroughness in drilling Mild steel. The Taguchi method, a powerful tool to design optimization forquality, is used to find optimal cutting parameters. The methodology is useful for modeling andanalyzing engineering problems. The purpose of this study is to investigate the influence ofcutting parameters, such as cutting speed and feed rate, and point angle on surface roughnessproduced when drilling Mild steel. A plan of experiments, based on L27Taguchi design method,was performed drilling with cutting parameters in Mild steel. All tests were run without coolantat cutting speeds of 7, 18, and 30 m/min and feed rates of 0.035, 0.07, and 0.14 mm/rev and point °angle of 90 , 118°, and 140°. The orthogonal array, signal-to-noise ratio, and analysis ofvariance (ANOVA) were employed to investigate the optimal drilling parameters of Mild steel.From the analysis of means and ANOVA, the optimal combination levels and the significantdrilling parameters on surface roughness were obtained. The optimization results showed thatthe combination of low cutting speed, low feed rate, and medium point angle is necessary tominimize surface roughness.Keywords: Taguchi method, Drilling. Mathematical Modeling Equations, Burr formation. International Journal of Research in Management, Economics and Commerce www.indusedu.org 66
  2. 2. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X1. INTRODUCTIONDrilling is one of the most commonly used machining processes in the shaping of Mild steel. Ithas considerable economical importance because it is usually among the finishing steps in thefabrication of industrial mechanical parts. The drilling process produces burrs on exit surface ofa work piece. The exit burr is the material extending off the exit surface of the work piece [1].Their effect on products is important because they may cause some critical problems such as thedeterioration of surface quality, thus reducing the product durability and precision .Burrformation affects work piece accuracy and quality in several ways: dimensional distortion onpart edge, challenges to assembly and handling caused by burrs in sensitive locations on thework piece, and damage done to the work subsurface from the deformation associated with burrformation [2-4].The term steel is used for many different alloys of iron. These alloys vary both in the Way theyare made and in the proportions of the materials added to the iron. All steels, However, containsmall amounts of carbon and manganese. In other words, it can be said that steel is a crystallinealloy of iron, carbon and several other elements, which hardens above its critical temperature.Like stated above, there do exist several types of steels , Which are (among others) plain carbonsteel (Mild steel), stainless steel, alloyed steel and tool steel.The Investigation presents the use of Taguchi method for minimizing the surface roughness indrilling Mild steel. Mild steel is extensively used as a main engineering material in variousindustries such as aircraft, aerospace, and automotive industries where weight is probably themost important factor. These materials are considered as easy to machining and possess superiormachinability [5] .Nihat Tosun[6] Use The grey relational analysis for optimizing the drilling process parametersfor the workpiece surface roughness and the surface roughness is introduced. Various drillingparameters, such as feed rate, cutting speed, drill and point angles of drill were considered. Anorthogonal array was used for the experimental design. Optimal machining parameters weredetermined by the grey relational grade obtained from the grey relational analysis for multi-performance characteristics (the surface roughness). Experimental results have shown that thesurface roughness in the drilling process can be improved effectively through the new approach.Stein and Dornfeld [7] presented a study on the burr height, thickness, and geometry observed in International Journal of Research in Management, Economics and Commerce www.indusedu.org 67
  3. 3. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057Xthe drilling of 0.91-mm diameter through holes in stainless steel 304L. They presented a proposalfor using the drilling burr data as part of a process planning methodology for burr control. Tominimize the burr formed during drilling, Ko and Lee [8] investigated the effect of drillgeometry on burr formation. They showed that a larger point angle of drill reduced the burr size.Sakurai et al. [9] have also tried to change the cutting conditions and determined high feed ratedrilling of aluminum alloy. The researchers examined cutting forces, drill wear, heat generated,chip shape, hole finish, etc. Gillespie and Blotter [10] studied experimentally the effects of drillgeometry, process conditions, and material properties. They have classified the machining burrsinto four types: Poisson burr, rollover burr, tear burr, and cut-off burr. Valuable review aboutburr in machining operation provided important information [11].Some of the previous works that used the Taguchi method and response surface methodology astools for the design of experiment in various areas including machining operations are listed in[12–16]. The Taguchi method was used by Yang and Chen [17] to find the optimum surfaceroughness in end milling operations. They introduced a systematic approach to determine theoptimal cutting parameters for minimum surface roughness. An application of Taguchi methodto optimize cutting parameters in end milling is performed by Ghani et al. [18]. They investigatethe influence of cutting speed, feed rate, and depth of cut on the measured surface roughness.The study shows that the Taguchi method is suitable to solve the stated within minimum numberof trials as compared with a full factorial design.The main objective of this study was to demonstrate a systematic procedure of using Taguchidesign method in process control of drilling process and to find a combination of drillingparameters to achieve low burr height and surface roughness.Experiments were designed using Taguchi method so that effect of all the parameters could bestudied with minimum possible number of experiments. Using Taguchi method, AppropriateOrthogonal Array has been chosen and experiments have been performed as per the set ofexperiments designed in the orthogonal array. Signal to Noise ratios are also calculated toanalyze the effect of parameters more accurately.Results of the experimentation were analyzed analytically as well as graphically using ANOVA.ANOVA has determined the percentage contribution of all factors upon each responseindividually. International Journal of Research in Management, Economics and Commerce www.indusedu.org 68
  4. 4. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X2. TAGUCHI METHODTraditional experimental design methods are very complicated and difficult to use. Additionally,these methods require a large number of experiments when the number of process parametersincreases [21]. In order to minimize the number of tests required, Taguchi experimental designmethod, a powerful tool for designing high-quality system, was developed by Taguchi. Thismethod uses a special design of orthogonal arrays to study the entire parameter space with smallnumber of experiments only.Taguchi recommends analyzing the mean response for each run in the inner array, and he alsosuggests analyzing variation using an appropriately chosen signal-to-noise ratio (S/N).There are 3 Signal-to-Noise ratios of common interest for optimization of Static Problems;(I) SMALLER-THE-BETTER: n = -10 Log ( )(II) LARGER-THE-BETTER: n = -10 Log10 [mean of sum squares of reciprocal of measured data](III) NOMINAL-THE-BEST: n = 10 Log10Lower is better for minimum surface roughness so, Lower is better = = -10 Log ( )Where n is no of observation, y is observed data.Regardless of category of the performance characteristics, the lower S/N ratio corresponds to abetter performance. Therefore, the optimal level of the process parameters is the level with thelowest S/N value. The statistical analysis of the data was performed by analysis of variance(ANOVA) to study the contribution of the factor and interactions and to explore the effects ofeach process on the observed value.3. DESIGN OF EXPERIMENTIn this study, three machining parameters were selected as control factors, and each parameterwas designed to have three levels, denoted 1, 2, and 3 (Table 1). The experimental design wasaccording to an L27(3^13) array based on Taguchi method, while using the Taguchi orthogonalarray would markedly reduce the number of experiments. International Journal of Research in Management, Economics and Commerce www.indusedu.org 69
  5. 5. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057XA set of experiments designed using the Taguchi method was conducted to investigate therelation between the process parameters and delamination factor. DESIGN EXPERT @ 16minitab software was used for regression and graphical analysis of the obtained data. Table 1 Drilling parameters and Levels Symbol Drilling Parameters Level 1 Level 2 Level 3 A Cutting speed, v 7 18 B (m/min) 30 C Feed rate, f 0.035 0.070 (rev/min) 0.140 Point angle, θ ( ) 90 118 1404. EXPERIMENTAL DETAILSMild Steel plates of 150×100×15 mm were used for the drilling experiments in the present study.The chemical composition and mechanical and physical properties of Mild Steel can be seen inTables 2 and 3, respectively. The drilling tests were carried out to determine the surfaceroughness under various drilling parameters. HSS drills (10-mm diameter) were used forexperimental investigations. Table 2 Chemical composition of mild steel Elements Maximum weight % C 0.45 S 0.60 Mn 1.00 P 0.40 Si 0.35 International Journal of Research in Management, Economics and Commerce www.indusedu.org 70
  6. 6. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X Table 3 Mechanical and physical properties of mild steel parameters Value -3 Density 10³ kg m 7.85 Thermal conductivity Jm- 48 1 K-1S-1 11.3 Thermal expansion 10-6 K- 210 young’s modulus GNm-2 600 Tensile strength MNm-25. RESULTS AND DISCUSSION5.1 Experiment results and Taguchi analysisIn machining operation, improving surface roughness (Ra) is an important criterion. The burrformation in drilling primarily depends upon the tool geometry, cutting parameters, andworkpiece materials.A series of drilling tests was conducted to assess the influence of drilling parameters on surfaceroughness in drilling Mild steel. Experimental results of the surface roughness for drilling Mildsteel with various drilling parameters are shown in Table 4. Table 4 also gives S/N ratio forsurface roughness. The S/N ratios for each experiment of L27 (3^13) was calculated. Theobjective of using the S/N ratio as a performance measurement is to develop products andprocess insensitive to noise factor. Table 5 shows average effect response table. Thus, byutilizing experiment results and computed values of the S/N ratios (Table 5), average effectresponse value and average S/N response ratios were calculated for surface roughness. The S/Nratio response graph for surface roughness is shown in Figs. 2For S/N ratio Feed rate (F value 9.861852), were found to be significant to Surface Roughnessfor reducing the variation & its contribution to Surface Roughness is 24.16571% followed bycutting speed (F-value 9.12035) the factor that significantly affected the Surface Roughnesswhich had contribution of 22.14368% respectively.The best results for Surface Roughness (lower is better) would be achieved when mild steelworkpiece is machined at cutting speed of 7 m/min, feed rate of 0.035 mm/rev and point angle of900. With 99% confidence interval, mean value & optimum value of Surface Roughness wasfound to be 5.988889 & 3.542222 µm respectively. International Journal of Research in Management, Economics and Commerce www.indusedu.org 71
  7. 7. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X Table 4 EXPERIMENTAL RESULT AND CORRESPONDING S/N RATIO S.No. Levels of factor Experimental Result S/N Ratio v f θ Ra (µm) Ra 1 7 0.035 90 2.285 7.1777241 2 7 0.035 118 4.875 13.759492 3 7 0.035 140 1.93 5.7111462 4 7 0.14 90 6.525 16.29161 5 7 0.14 118 6.005 15.57026 6 7 0.14 140 4.65 13.349059 7 7 0.07 90 5.545 14.878031 8 7 0.07 118 4.55 13.160228 9 7 0.07 140 6.44 16.177717 10 18 0.035 90 2.505 7.9761546 11 18 0.035 118 6.32 16.014342 12 18 0.035 140 7.11 17.037392 13 18 0.14 90 6.825 16.682053 14 18 0.14 118 5.935 15.468414 15 18 0.14 140 7.04 16.951453 16 18 0.07 90 7.185 17.128535 17 18 0.07 118 5.06 14.08301 18 18 0.07 140 9.73 19.762257 19 30 0.035 90 6.42 16.150701 20 30 0.035 118 5.735 15.170668 21 30 0.035 140 5.795 15.261069 22 30 0.14 90 9.705 19.739911 23 30 0.14 118 8.6 18.689969 24 30 0.14 140 5.58 14.932684 25 30 0.07 90 8.585 18.674806 26 30 0.07 118 7.16 17.09826 27 30 0.07 140 6.595 16.384296 International Journal of Research in Management, Economics and Commerce www.indusedu.org 72
  8. 8. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X Table 8 ANOVA table of Surface Roughness Source SS DOF Varianc F test F critical e C% F T > FC Cutting 13.3450 Speed 26.6901 2 5 15.16655 4.46 26.16802 s 11.8438Feed Rate 23.6877 2 5 13.46045 4.46 23.06687 s Point Angle 0.0999 2 0.04995 0.056768 4.46 NS 0.58712 A*B 2.3485 4 5 0.667263 3.84 NS B*C 17.6148 4 4.4037 5.004773 3.84 15.39427 s C*A 19.3354 4 4.83385 5.493636 3.84 17.17147 s Error 7.0392 8 0.8799 3.72367 Total 96.8156 26 7 0.67768E-pooled 9.4876 14 6 Table 9 Mean values of process parameters for surface roughness Process Parameters Levels Mean Surface S/N Ratio Roughness (mm) Cutting speed (A) 1 4.756111 13.54504 2 6.412222 16.14017 3 7.130556 17.06247 Feed rate (B) 1 4.775 13.57947 2 6.762778 16.6025 3 6.096667 15.70185 International Journal of Research in Management, Economics and Commerce www.indusedu.org 73
  9. 9. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X Main Effects Plot for Surface Roughness Data Means Cutting Speed Feed Rate 7.0 6.5 6.0 5.5 5.0 Mean 7 18 30 0.035 0.070 0.140 Point angle 7.0 6.5 6.0 5.5 5.0 90 118 140 Fig 2 Effect of drilling parameters on Surface roughness Table 10 Optimum Levels of Process Parameters Process Parameters Parameter Designation Optimum Level cutting speed (V) A1 7 Feed rate (f) B1 0.0355.2 RESULTS & DISCUSSIONThe effect of parameters i.e Cutting speed, feed rate and point angle and some of theirinteractions were evaluated using ANOVA analysis with the help of MINITAB 16 @ software.The purpose of the ANOVA was to identify the important parameters in prediction of Surfaceroughness . Some results consolidated from ANOVA and plots are given below:Surface RoughnessAfter the analysis of the results in ANOVA table, cutting speed is found to be the mostsignificant factor (F-value 15.16655) & its contribution to Surface roughness is 26.16802%followed by feed rate (F-value 13.46045) the factor that significantly affected the surfaceroughness which had contribution of 23.06687% respectively.The interaction between feed rate and point angle (F-value 5.004773) is found to be significantwhich contributes 15.39427% and the interaction between point angle and cutting speed (F-value5.493636) is found to be significant which contributes 17.17147%. International Journal of Research in Management, Economics and Commerce www.indusedu.org 74
  10. 10. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X6. CONCLUSION AND SCOPE FOR FUTUREThe present study was carried out to study the effect of input parameters on the surfaceroughness. The following conclusions have been drawn from the study: 1. Surface roughness is mainly affected by cutting speed and feed rate as per the main effects plot for SR. Surface Roughness is higher with the increase in cutting speed and feed rate when the experimentation is done. 2. From ANOVA analysis, parameters making significant effect on surface roughness feed rate, was found to be significant for reducing the variation followed by cutting speed respectively. 3. The best setting of input process parameters for Surface finish within the selected range is as follows: i) Low cutting speed i.e. 7m/min. ii) Low feed rate i.e. 0.35 mm/rev. iii) Low point angle i.e. 900.REFERENCES 1) Dornfeld D (2004) Strategies for preventing and minimizing burr formation, pp 1–18 2) Kim J, Dornfeldd DA (2002) Development of an analytical model for drilling burr formation in ductile materials. Trans ASME 124:192–198 3) Ko SL, Chang JE, Yang GE (2003) Burr minimizing scheme in drilling. J Mater Process Technol 140:237–242 4) Gillespie LK (1994) Process control for burrs and deburring. 3.International Conference on Precision Surface Finishing and Burr Technology, Korea, pp 1–11 5) ASM (1999) ASM handbook, vol 16: machining. ASM, USA, pp 761–804 6. Lin TR, Shyu RF (2000) Improvement of tool life and exit burr using variable feeds when drilling stainless steel with coated drills. Int J Adv Manuf Technol 16:308–313 6) Stein JM, Dornfeld DA (1997) Burr formation in drilling miniature holes. Ann CIRP 46/17:63–66 7) Ko SL, Lee JK (2001) Analysis of burr formation in drilling with a new-concept drill. J Mater Process Technol 113:392–398 8) Sakurai K, Adachi K, Kawai G, Sawai T (2000) High feed rate drilling of aluminum alloy. Mat Sci Forum 331–337:625–630 International Journal of Research in Management, Economics and Commerce www.indusedu.org 75
  11. 11. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X 9) Gillespie LK, Blotter PT (1976) The formation and properties of machining burs. Transactions ASME Journal of Engineering for Industry 98:66–74 10) Aurich JC, Dornfeld D, Arrazola PJ, Franke V, Leitz L, Min S (2009) Burrs: analysis, control and removal. CIRP Annals Manuf Technol 58:519–542 11) Zhang JZ, Chen JC, Kirby ED (2007) Surface roughness optimization in an end milling operation using the Taguchi design method. J Mater Process Technol 184:233–239 12) Tseng PC, Chiou IC (2003) The burrs formation prediction and minimization based on the optimal cutting parameters design method. JSME Int J Ser C 46(2):779–787 13) Tsao CC (2008) Comparison between response surface methodology and radial basis function network for core-center drill in drilling composite materials. Int J Adv Manuf Technol 37:1061–1068 14) Gaitonde VN, Karnik SR, Achyutha BT, Siddeswarappa B (2007) Methodology of taguchi optimization for multi-objective drilling problem to minimize burr size. Int J Adv Manuf Technol 34:1–8 15) Gaitonde VN, Karnik SR, Davim JP (2008) Prediction and minimization of delamination in drilling of medium-density fiberboard (MDF) using response surface methodology and Taguchi design. Mater Manuf Process 23:377–384 16) Yang JL, Chen JC (2001) A systematic approach for identifying optimum surface roughness performance in end-milling operations. J Ind Technol 17(2):2–8 17) Ghani JA, Choudhory IA, Hassan HH (2004) Application of Taguchi method in the optimization of end milling parameters. J Mater Process Technol 145:84–92 18) Myers RH,Montgomery DC (1995) Response surface methodology: process and product optimization using designed experiments.Wiley, New York 19) Pradhan MK, Biswas CK (2008) Modelling of machining parameters for MRR in EDM using response surface methodology. Proceedings of NCMSTA’08 Conference, Hamirpur, pp 535– 542 20) Rosa JL, Robin A, Silva MB, Baldan CA, Peres MP (2009) Electrodeposition of copper on titanium wires: Taguchi experimental design approach. J Mater Process Technol 209:1181–1188 21) Savaskan M, Taptik Y, Urgen M (2004) Performance optimization of drill bits using design of experiments. ITU Dergisi/Engineering 3:117–128 International Journal of Research in Management, Economics and Commerce www.indusedu.org 76
  12. 12. IJRMEC Volume2, Issue 3(March 2012) ISSN: 2250-057X 22) Mohan NS, Kulkarni SM, Ramachandra A (2007) Delamination analysis in drilling process of glass fiber reinforced plastic (GFRP) composite materials. J Mater Process Technol 186:265–271 23) Montgomery DC (1991) Design and analysis of experiments, 3rd edn. Arizona State University, New York 24) Petropoulos G, Ntziantzias I, Anghel C (2005) A predictive model of cutting force in turning using Taguchi and response surface techniques. Proceedings of 1st IC-EpsMsO 25) Yang WH, Tarng YS (1998) Design optimization of cutting parameters for turning operations based on the Taguchi method. J Mater Process Technol 84:122–129. International Journal of Research in Management, Economics and Commerce www.indusedu.org 77

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