INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET)

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 ISSN 0976 6340 (Print) ISSN 0976 6359 (Online) Volume 3, Issue 1, January- April (2012), pp. 300-309 IAEME: www.iaeme.com/ijmet.html Journal Impact Factor (2011): 1.2083 (Calculated by GISI) www.jifactor.com IJMET I A E M E DEVELOPMENT OF RSM BASED MODEL FOR MACHINING OF T105CR EN31 STEEL BY TIALN COATED TWIST DRILL ABSTRACT Sumedh. S. Pathak Dept. of Mechanical Engineering, M.B.E S College of Engineering, Ambajogai - 431517, Maharashtra, India Email: sumedh81@gmail.com Dr. M. S. Kadam, Dept. of Mechanical Engineering, J.N. E.C. Aurangabad, Maharashtra, India The main objective of the paper is to develop mathematical model based on Central Composite Design Response Surface Methodology for Torque, chip load and machining time. The input parameters considered for drilling of T105Cr1 EN31 steel were spindle speed (N), feed rate (f), drill diameter (D) and point angle (θ). The drill bit used was Titanium Aluminum Nitride coated twist drill having 6 mm and 8 mm in diameters. The drilling operation was carried out on Haas Tool Room Mill USA made CNC milling machine under dry condition. To confirm the validity and correctness of the established mathematical model ANOVA is used for the indepth analysis of effect of finish drilling process parameters on the responses. Analysis of the parametric influence on responses were plotted through 3D surface plot and contour plots. Keywords RSM, Torque, Chip Load, Machining Time, ANOVA 1. INTRODUCTION Dry machining is the process in which cutting operations were carried out in the absence of coolant. Various coatings are used in the market to avoid the use of coolant and its disposal, which is also a problem. EN31 steel is used to manufacture punches in press tools. Drilling is the cutting process in which a hole is originated or enlarged by means of a multipoint, fluted, end cutting tool. They are widely used in the 300

aerospace, aircraft and automotive industries. Although non-traditional machining methods have improved in the manufacturing industries in response to new and unusual machining requirement that could not be satisfied by conventional methods. Non-traditional machining includes ultrasonic machining, abrasive water jet cutting, electrochemical machining (ECM), chemical machining (CHM) and electric discharge machining (EDM) etc but conventional drilling still remains as one of the most common machining processes. [8] The factors affecting the process were divided into controllable and non controllable. The controllable factors includes spindle speed, feed rate, workpiece material, with drill geometry and its material. The Non controllable factors were machine accuracy, operating environment and humidity. The two main coating processes used on cutting tools are Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). The development of CVD coating process include titanium carbide (TiC), titanium nitride (TiN), titanium carbonitride (TiCN) and aluminum oxide (Al 2 O 3 ) has produced complex multilayer coatings for specific applications. TiN is widely applied as coatings on cutting tools because of its hardness and low coefficient of friction. However, it cannot be used at high temperature due to its poor chemical stability. The PVD coating includes Titanium Carbo-Nitride (TiCN) and titanium aluminum nitride (TiAlN) which provide better high speed performance and increase abrasive wear resistance. [11] The recent interest in TiAlN coatings to enhance the high temperature performance of PVD coated tools has diverse the interest in alternative coating technologies. TiAlN thin film intensified the wear resistant coatings for high speed machining due to their high hardness, excellent oxidation and corrosion resistance. The presence of Al in TiAlN coatings overcomes the oxidation problems due to the presence of a superficial layer of (Al 2 O 3 ) formed at high temperatures. 2. LITERATURE REVIEW Panda et al [9] used High-speed steel drills with different diameters for drilling in cast iron workpiece at different cutting conditions in the absence of coolant. The output parameters are thrust force and torque. Spindle speed and Feed rate was varied in four steps. Different combinations of three design variables viz. spindle speed, feed rate and drill diameter have been used to perform 64 different drilling operations on cast iron plate. To predict flank wear two different types of artificial neural network were used. Neural network was used to monitor geometric properties of tool wear by C. sanjay [3], Patra [6], Paliwal [7]. Gaitonde et.al [10] show the effects of cutting speed, feed rate and point angle on delamination factor. Gaitonde et al [10] focus on Taguchi optimization method for simultaneous minimization of delamination factor at entry and exit of the holes in drilling of SUPERPAN D ECOR (melamine coating layer) MDF panel. The experiments were carried out as per L9 orthogonal array with each experiment performed under different conditions of feed rate and cutting speed. The analysis of means (ANOM) was performed to determine the optimal levels of the parameters and the analysis of variance (ANOVA) was employed to identify the level of importance of the machining parameters on delamination factor. The investigations revealed that the delamination can be effectively reduced in drilling of MDF materials by employing the higher cutting speed and lower feed rate values. 301

Young et al [12] considered input parameters Spindle speed, feed rate, drill size, workpiece material and feed motor current to predict the breakage of a drill bit used and artificial neural network to predict drill failure. Su et al [11] in their work employs the PVD process to deposit coatings of single layer TiN, binary layer TiN/TiCN, multilayer TiN\Ti\TiN, and sequenced TiN\TiCN\TiN multilayer coatings with variable individual TiN-layer and TiCN-layer thicknesses on tungsten carbide disks and inserts. Actual milling tests identify wear performance. Experimental results indicate that the coating with a total thickness of 7 mm and layer sequence TiN/TiCN/TiN exhibits good wear resistance on SRV wear test and milling test. The thickest multilayer TiN/Ti/TiN coating, although having the highest hardness, has the worst wear resistance for all tests. Notably zero-wear performance was observed for all coating disks under cutting fluid lubricated condition due to the transferred layers formed between the contact interface. 3. Development of RSM CCD based mathematical model for machining of T105Cr1 EN31 steel RSM is used to establish the mathematical relation between the output response, y and the various drilling parameters. In most RSM problems, the form of relationship between the response and the independent variables is unknown. Thus, the first step in RSM is to find a suitable approximation for the true functional relationship between output and set of independent variables. Usually, in low-order polynomial if the response is well modeled by a linear function of independent variables, then the approximating function is the first-order model. y = β 0 + β 1 x 1 + β 2 x 2 +.. + β k x k + Є ------- 1] In the present study, spindle speed, feed rate, drill diameter and point angle were considered as controllable variables. Five levels were defined for each of the factor as shown in Table 1. The effects of factors on responses were tested through a set of planned experiments based on five levels four factor fractional factorial Central Composite Design (CCD). It consist of fractional factorial design with star and centre points. Table 1: Factors and their levels for machining of T105Cr1EN 31 steel Factor Symbol Unit Levels -2-1 0 +1 +2 Spindle speed N rpm 800 1000 1200 1400 1600 Feed rate f mm/rev 80 120 168 224 288 Drill diameter D Mm 6 6 6 8 8 Point angle θ degrees 135 120 135 120 135 302

Table 2: Machining conditions for drilling operation HAAS CNC mini mill machine Floor space : 6.5 X6.5 Spindle speed: Max. 4000 rpm Torque: 45Nm @ rpm Power: 9KVA 3 ph or 240V AC 4. EXPERIMENTAL WORK FOR T105CR1 EN31 STEEL Experimental setup showing twist drill bit and workpiece for HAAS CNC mini milling machine is shown in Figure 1. Figure 1: Dry Drilling condition on T105Cr1 EN31 steel The material used for drilling was T105Cr1 EN31 steel with its chemical composition as shown in Table 3. The responses considered are machining time, torque and chip load. The machining time is measured in seconds, torque in Nm and chip load in mm. Centre drill bit was used to provide a starting hole for a larger-sized drill bit. In this experimentation, the center drill bit used was 2 mm for guidance of twist drill. Drilling tests were performed using 8 mm and 6 mm diameter TiAlN coated drill bit with two flutes. Machining conditions for CNC milling machine were kept constant for drilling tests throughout the experiment as shown in Table 2. Dimensional properties of the cutting tool were shown in Table 4. Table 3: Chemical Composition of T105Cr1 EN31 steel % C % Mn % Cr % Si 0.98 0.26 1.1 0.23 303

Table 4: Dimensional properties of the cutting tool Drill type HSS TiAlN Tool Diameter 6 mm 8 mm 6 mm 8 mm Point angle 118 135 118 135 118 135 118 135 Helix Angle 28 28 Flute 2 flute 2 flute Flute length 72 mm 72 mm Overall length 113 mm 113 mm Shank type Cylindrical Cylindrical Table 5: CCD design matrix for TiAlN coated twist drill for machining of T105Cr1 EN31 steel under dry condition. Spindle Drill Point Chip Feed (f) Tm Torque Experimental speed diameter angle(θ) load in in in Run (N) in (D) in in in mm/rev (Sec.) (Nm) rpm mm degree (mm) 1. 1000 120 6 120 42 55.30 0.059 2. 1400 120 6 120 43 53.43 0.042 3. 1000 224 6 120 33 57.0 0.112 4. 1400 224 6 120 38 55.13 0.080 5. 1000 120 8 120 43 54.89 0.060 6. 1400 120 8 120 44 53.00 0.042 7. 1000 224 8 120 35 55.48 0.112 8. 1400 224 8 120 38 53.59 0.080 9. 1000 120 6 120 43 55.69 0.060 10. 1400 120 6 120 42 53.90 0.043 11. 1000 224 6 120 35 56.78 0.112 12. 1400 224 6 120 37 55.48 0.080 13. 1000 120 8 120 43 54.00 0.060 14. 1400 120 8 120 43 52.65 0.043 15. 1000 224 8 120 34 55.90 0.112 16. 1400 224 8 120 36 54.63 0.080 17. 800 168 6 135 36 57.58 0.105 18. 1600 168 6 135 38 33.70 0.052 19. 1200 80 6 135 45 45.11 0.033 20. 1200 288 6 135 32 44.88 0.120 21. 1200 168 6 135 39 45.04 0.070 22. 1200 168 8 135 36 44.96 0.070 23. 1200 168 6 135 39 45.11 0.070 24. 1200 168 6 135 39 44.81 0.070 25. 1200 168 6 135 39 44.93 0.070 26. 1200 168 6 135 39 45.08 0.070 27. 1200 168 6 135 39 45.00 0.070 304

28. 1200 168 6 135 39 44.93 0.070 29. 1200 168 6 135 39 45.08 0.070 30. 1200 168 6 135 39 45.00 0.070 31. 1200 168 6 135 39 45.08 0.070 32. 1200 168 6 135 39 45.00 0.070 Table 6: Coefficient table for p values Input parameter P values for Tm P values for T P values for Cl Spindle speed 0.004 0.000 0.000 Feed rate 0.000 0.365 0.000 Drill diameter 0.644 0.405 0.949 Point angle 0.025 0.000 0.553 5. ADEQUACY TEST ANALYSIS OF THE MODEL Central Composite Design for T105Cr1 EN 31 steel material under dry condition using TiAlN coated twist drill as shown in Table 5. The p values of the input variables for TiAlN coated twist drill on T105Cr1 EN31 steel with responses shown in Table 6. The adequacy of model has been tested through analysis of variance (ANOVA) by using Minitab software 14 with 95% confidence level. The mathematical model to predict machining time, torque and chip load was given by Tm = 55.6 + 0.0035 N - 0.0661 f - 0.1150 D - 0.0697 θ -------- 2] T = 152-0.0127 N + 0.00842 f - 0.4440 D - 0.6730 θ -------- 3] Cl = 0.0834-0.000063 N + 0.000426 f - 0.000053 D - 0.000059 θ ------- 4] Table 7: Analysis of Variance table Source DF Machining time Torque Chip load F value P value F value P value F value P value Regression 4 63.72 0.000 42.20 0.000 299.53 0.000 Residual error 27 Total 31 From ANOVA Table 7, the associated P values for the model is lower than 0.05 (i.e. α= 0.05 or 95% confidence) indicating that the model is statistically significant for machining time, torque and chip load. F-ratio is an index used to check the adequacy of the model in which calculated value of F should be greater than the F table value. From statistical table for machining time, F 0.05,4,27 = 2.73 and F calculated Value is 63.72. F Calculated Value > F Statistical Table. The F value 63.72 implies the model is significant. The adequacy of the model is further analyzed by using R Sq values. The larger value of R Sq is desirable. In this case, the R-Sq value is 90.4%, which shows the high correlation that exists between the experimental values and predicted values. From statistical table for torque, i.e. F 0.05,4,27 = 2.73 and F calculated value is 42.20. F Calculated Value > F Statistical Table. The F value 42.20 implies the model is significant. For torque, the R-Sq value is 86.2%, which shows the high correlation that exists between the experimental values and predicted values. From statistical table for chip load, i.e. F 0.05,4,27 = 2.73. F calculated Value is 299.53. 305

F Calculated Value > F Statistical Table. The F value 299.53 implies the model is significant. The R-Sq value for chip load is 97.8%, which shows the high correlation that exists between the experimental values and predicted values. 6. ANALYSIS OF PARAMETRIC INFLUENCE ON RESPONSES The development of RSM model for responses were used to analyze two factor interaction effects by plotting 3D response surface plot and contour plot. Contour plots plays very important role in the study of response surface. By generating contour plots using software, the experimenter can characterize the shape of surface and locate optimum with reasonable precision. 3.a) Response surface 3.b) Contour plot Figure 3 (a,b): Response surface and contour plot of spindle speed, feed rate Vs Machining time for TiAlN coated drill bit under dry drilling keeping 120 point angle and drill diameter 8 mm as constant. Figure 3 illustrates the influence of feed rate and spindle speed on machining time keeping point angle 120 and drill diameter 8 mm as constant. It can be observed from Figure 3.a) minimum machining time results from the higher feed rate and minimum spindle speed. As the feed rate goes on increasing time required to machining is minimum but very less effect of spindle speed on response in this case. From contour plot of Figure 3.b), it is clear that, with feed rate at higher value, minimum machining time can be achieved. 4.a) Response surface 4.b) Contour plot 306

Figure 4 (a,b): Response surface and contour plot of spindle speed, feed rate torque for TiAlN coated drill bit under dry drilling keeping 120 and drill diameter 8 mm point angle as constant. Figure 4 illustrates the influence of feed rate and spindle speed on torque keeping point angle 120 and drill diameter 8 mm constant. It can be observed from Figure 4.a) that minimum torque results from the lower feed rate and maximum spindle speed. As the spindle speed increases and feed rate decreases, minimum torque obtained. From contour plot of Figure 4.b), it is clear that, with feed rate at lower value, minimum torque can be achieved. 5.a) Response surface 5.b)Contour plot Figure 5(a,b): Response surface and contour plot of spindle speed, feed rate Vs chip load for TiAlN coated drill bit under dry drilling keeping 120 point angle and drill diameter 8 mm as constant. Figure 5 illustrates the influence of feed rate and spindle speed on chip load keeping point angle 120 and drill diameter 8 mm as constant. It can be observed from Figure 5.a) that minimum chip load results from the lower feed rate and higher spindle speed. From contour plot of fig. 5.b), it is clear that, with feed rate at lower value and spindle speed at higher value, minimum chip load can be achieved. Figure 6: Residual distribution of machining time Figure 7: Residual distribution of torque 307

Figure 8: Residual distribution of chip load Validation of proposed models shown in Figures 6, 7 and 8. It shows residual distribution diagrams for machining time, torque and chip load under dry drilling of TiAlN coated twist drill on T105Cr1 EN31 steel. The residuals are distributed around the normal line. Therefore, the developed mathematical models are appropriate models for predicting parameters effect. CONCLUSION An investigational analysis of parametric influence on machining time, torque and chip load on T105Cr1 EN31 steel with dry drilling condition. The machining time, torque and chip load were studied with respect to spindle speed, feed rate, drill diameter and point angle by developing Response Surface Model. The database required to construct the model was obtained by conducting drilling experiments as per Central Composite Design (CCD). The model developed was validated through Analysis of Variance. The influence of selected process parameters on responses was analyzed by generating 3D response plots and the corresponding contour plots. It has been observed from the analysis that - 1) For machining time, feed rate plays major role followed by spindle speed, point angle and drill diameter 2) For torque, spindle speed and point angle are significant parameters followed by feed rate and drill diameter. 3) For chip load, spindle speed and feed rate are profound followed by point angle and drill diameter during drilling of T105Cr1 EN31 steel with TiAlN coated drill bit. Nomenclature List of Abbreviations and Symbols ANOVA ----- Analysis of Variance CCD ----- Closed Composite Design CNC ----- Computer Numerical Control DOE ----- Design of Experiment RSM ----- Response Surface Methodology TiAlN ----- Titanium Aluminum Nitride T ----- Torque Tm ----- Machining Time Cl ----- Chip Load 3D ----- 3 Dimensional y ----- Response 308

ε ------ Error ----- Degree / Angle Ө ----- Point Angle Acknowledgement The authors are thankful to IGTR (Indo-German Tool Room), Aurangabad for the experimental setup and for assisting the experimental work. The author wish to thank to Mr. G.S. Awsekar and Mr. Rakhewar for their useful help and discussions. REFERENCES 1] ] Ahmad Fauzi Ahmaed, Effect of cutting parameters on the hole diameter and surface roughness for dry drilling of Aluminum Alloy 6061, Thesis Report. 2] C. R.Kothari Research Methodology, Methods & Technique, Analysis Of Variance and Covariance pp258-264 3] C. Sanjay, M.L. Neema, C.W. Chin, Modeling of tool wear in drilling by statistical analysis and artificial neural network Journal of Materials Processing Technology 170 (2005) 494 500 4] D. C. Montgomery, Design and Analysis of Experiments, Wiley India Edition 5th edition Reprint 2007 pp 71-72 and Appendix table IV pp 644. 5] HMT Bangalore, Production Technology, Tata Mc Graw Hill Pub. 2000 VIIth reprint section 5 pp 123 6] Karali Patra, Surjya K. Pal, Kingshook Bhattacharyya, Artificial neural network based prediction of drill flank wear from motor current signals, Applied Soft Computing 7(2007) 929 935 7] Mukta Paliwal, Usha A. Kumar, "Review Neural networks and statistical techniques: A review of applications", Expert Systems with Applications 36 (2009) 2 17. 8] M. Kurt & Y. Kaynak & E. Bagci, Evaluation of drilled hole quality in Al 2024 alloy, Int J Adv Manuf Technol (2008) 37:1051 1060 9] S. S. Panda, A. K. Singh, D. Chakraborty, S. K. Pal, Drill wear monitoring using an artificial neural network, Journal of Materials Processing Technology 172 (2006) 283 290 10] V.N. Gaitonde, S.R. Karnik, J. Paulo Davim, Taguchi multiple-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept, journal of materials processing technology 196 (2008) 73 78 11] Y.L. Su and W.H. Kao, Tribological Behavior and Wear Mechanisms of TiN/TiCN/TiN Multilayer Coatings ASM International, JMEPEG (1998) 7:601-612. 12] Young Jun Choi, Min Soo Park, Chong Nam Chu, Prediction of drill failure using features extraction in time and frequency domains of feed motor current International Journal of Machine Tools & Manufacture 48 (2008) 29 39 13] http://www.vortextool.com/images/chiploadchart.pdf 14] http://www.ehow.com/how_7286545_calculate-chip-load.html 309