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VOLUME 31 ISSUE 2 of Achievements in Materials December and Manufacturing Engineering 2008

Experimental study to analyse the workpiece surface temperature in deep hole of aluminium alloy engine blocks using MQL technology

M.I. Hussain a, K.S. Taraman b,*, A.J. Filipovic a, I. Garrn c a General Motors Corporation, GMPT Headquarters, 823 Joslyn Road, Pontiac, Michigan 48340, USA b Mechanical Engineering, Lawrence Technological University, 21000 W. Ten Mile Road, Southfield, MI 48075, USA c Guhring oHG, Winterlinger Str. 12 D-72488 Sigmaringen-Liaz, Germany * Corresponding author: E-mail address: [email protected] Received 11.10.2008; published in revised form 01.12.2008

Analysis and modelling

ABSTRACT

Purpose: The objective of this applied research is to investigate the MQL deep hole drilling method, in order to increase productivity and replace the current method of drilling main oil gallery holes in aluminum alloy cylinder blocks that uses MWFs. Design/methodology/approach: The experimentation was performed at the Guhring, Inc. (a tool manufacturing company) in Germany. The MQL drilling machine, machine operators, CNC programming, hole drilling, and tool layouts were be provided by Guhring. The main components of this experiment were the MQL machine with dual channel system, , special carbide , data acquisition system, thermal optical camera to measure surface temperature, and computers. The surface along the axis of the of the oil gallery hole was milled to produce uniform thickness of 2.5 mm. The was spinning but not moving into the engine block, the engine block was moving into the drill. All experiments were performed in random order with no replications. The other output variables, surface finish, true position, roundness, straightness, diameter and misalignment, were measured by a surface analyzer and coordinate measuring machine (CMM). Findings: Based on this research it can be concluded that MQL is a viable production solution for DHD in automotive cast aluminum alloy. Good part quality characteristics were achieved using this method with production feeds and speeds. Practical implications: The MQL method has shown potential to be even more productive as compared to traditional deep hole drilling which would result in less capital investment. Originality/value: Good part quality characteristics were achieved using this method with production feeds and speeds. Keywords: Minimum Quantity Lubricant (MQL); Aluminum alloys; Temperature; Deep Hole Drilling (DHD)

© Copyright by International OCSCO World Press. All rights reserved. 2008 Research paper 485 Journal of Achievements in Materials and Manufacturing Engineering Volume 31 Issue 2 December 2008

1. Introduction oil mist on tool temperature was the greatest on drilling as 1. Introduction compared to and milling. In their carbon steel AISI1045 (S45C) drilling process, temperature dropped from 430 ºC to Alternative techniques had been evaluated to 330ºC when MQL process was compared to dry drilling. eliminate the use of coolants in metal cutting processes. The goal The objective of this applied research is to investigate the is to improve efficiency, reduce costs, boost productivity, and MQL deep hole drilling method, in order to increase productivity minimize cycle time, while simultaneously safeguarding the work and replace the current method of drilling main oil gallery holes environment. Dry or near dry processes are the potential in aluminum alloy cylinder blocks that uses MWFs. candidates to replace wet machining processes. Zeilmann and Weingaertner [16] stated that cooling effect in dry or near dry drilling is negligible as compared to drilling with metal working fluids (MWFs). Deep hole drilling (DHD) in aluminum alloys 2.RESEARCH Research APPROACH approach becomes more challenging in the absence of MWFs because of elevated temperature in the cutting zone. Gundrills and Gdrills Model Postulation had been used to drill oil gallery holes in engine blocks using metal working fluids. Human health and safety, system Based on the literature survey, there are two types of input inflexibility, coolant cost and degradation, chips management variables: drill variables and process variables. The drill variables cost, and slow cycle time are the main disadvantages of the are point angle of the drill (A, degree) and drill body taper (T, mm current method of oil gallery hole drilling using gundrills and per 100 mm length of the drill body). The process variables are Gdrills. cutting speed (V, meters per minute), feed rate (F, mm per In an aluminum alloy drilling process build up edge (BUE) revolution), compressed air pressure (Pr, bars) and oil delivery and build up layer (BUL) due to heat are the most common rate (D, ml per hour). The output variable is surface temperature problems in the absence of MWFs. Pure dry machining in (Ts, degree centigrade ºC). aluminum alloys is difficult due to the high tendency of aluminum The relationship of the surface temperature (R) and sticking to the tool. Fleischer et al. [6] studied the relationship of independent variables may be postulated as follows: heat with cutting speed and feed rate in dry drilling. They 2 k l m n o p concluded that the surface heat flux (W/mm ) of the drilled hole R = C A T V F Pr D (1) decreases with the increase of cutting speed and feed. Chung [5] modeled one and two dimensional heat transfer phenomenon in The C constant and all exponents k, l, m, n, o, and p are to be the drilling processes. He stated that heat source and temperature estimated. The logrithmic form of Equation (1) can be expressed distribution can be estimated in thick workpieces. Agapiou and as: Stephenson [1] developed a method to compute drill temperature and concluded that point angle and helix angle influence the drill lnR = lnC + k lnA+ l lnT + m lnV + n lnF + o lnPr + p lnD (2) temperature. Bono and Ni [3] concluded that the maximum temperature occurs near the chisel edge. Translating Equation (2) into linear form produces Equation (3): Alverio et al. [2] conducted drilling experiments using 390 aluminum plates. They concluded that tool life is inversely ǔ = b0x0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 (3) proportional to the cutting speed raised to the third power. Ozcelik and Bagci [11] studied the influence of spindle speed and In Equation (3), ǔ is the predicted value of the output in feed rate on the drill temperature using Taguchi’s method. Kelly natural logarithmic form and b0 in Equation (3) is an estimate of and Cotterell [8] used thermocouples to investigate drilling lnC in equation (2). The Coefficients b1 through b6 in Equation (3) temperature of the workpiece. The wall thickness was 0.3 mm are the estimates of the coefficients k through p in Equation (2). from the cutting zone. They concluded that temperature of the The variables x1 through x6 in Equation (3) are coded in natural work piece increases with the increase of cutting speed and feed logarithm form to represent input variables A, T, V, F, Pr, and D. rate. Schwenck et al. [12] concluded in their study that higher In Table I, code “-1” represents low level, “0” represents middle speed and feed rate using MQL technology helps reducing heat level, and “1” represents high level of each input variable. transfer form the chips to the cutting tool. They also reported that TABLE I provides the six independent variables and their codes. chips evacuation speed is significantly high in MQL process when compared to wet drilling process. Strenkowski et al. [13] modeled Table 1. three-dimensional drilling process and suggested technique to Levels and coding of input variables predict drill tip temperature that can forecast tool life and Drill Body Feed Oil Point Cutting Compressed Input Taper Rate Delivery performance. Kalidas et al. [7] concluded that in dry drilling Angle Speed Air Pressure Variables Æ (mm/10.0m (mm/rev.) Rate process high feed rate induces lower workpiece temperature. (deg.) A (m/min)V (Bars) Pr Zeilmann and Weingaertner [16] conducted a study comparing m length) T F (ml/hr) D MQL (internal mixing through the tool delivery) and MQL Levels applied with an external nozzle in drilling TiA16V4 material and (below) & X1 X2 X3 X4 X5 X6 concluded that the temperature of the workpiece is 50% higher in Codes (right) MQL external process. Ueda et al. [15] used infrared radiation -1 118 0 150 133 0.45 4.2 20 pyrometer to measure the cutting temperature of turning, milling 0 126 0 200 200 0 6 5 40 and drilling process using MQL. They concluded that the effect of 1 135 0 266 300 0 8 6 80

486 Research paper M.I. Hussain, K.S. Taraman, A.J. Filipovic, I. Garrn Analysis and modelling

Appendix A and Kumar [10] also used a similar equation to evaluate thrust and Design matrix [4] torque in their drilling study. x1 x2 x3 x4 x5 x6 2 2 -1 -1 0 -1 0 0 ǔ = b0x0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b11x1 + b22x2 + 2 2 2 2 1 -1 0 -1 0 0 b33x3 + b44x4 + b55x5 + b66x6 + b12x1x2 + b13x1x3 + b14x1x4 + -1 1 0 -1 0 0 b15x1x5 + b16x1x6 +b23x2x3 + b24x2x4 + b25x2x5 + b26x2x6 + b34x3x4 + 1 1 0 -1 0 0 b35x3x5+ b36x3x6 + b45x4x5 + b46x4x6 + b56x5x6 (4) -1 -1 0 1 0 0 1 -1 0 1 0 0 Design of Experiments -1 1 0 1 0 0 1 1 0 1 0 0 Traditional one variable at a time approach to investigate six 0 -1 -1 0 -1 0 variables at three levels would require (36) 729 experiments. An 0 1 -1 0 -1 0 efficient design four from Box and Behnken [4] was selected to 0 -1 1 0 -1 0 investigate the effects of these six variables. It requires only 54 0 1 1 0 -1 0 experiments as shown in Appendix A. The design utilizes 48 0 -1 -1 0 1 0 experiments at different levels of the investigated variables and 0 1 -1 0 1 0 0 -1 1 0 1 0 six experiments at the center levels to determine the pure error. 0 1 1 0 1 0 The 54 experiments were conducted in two orthogonal blocks: 0 0 -1 -1 0 -1 block 1 and block 2. Each block had 24 experiments plus three 0 0 1 -1 0 -1 additional experiments at the center point. In this design, the main 0 0 -1 1 0 -1 effects and two-variable interactions are not aliased with any 0 0 1 1 0 -1 other main effects or two-variable interactions [9]. 0 0 -1 -1 0 1 0 0 1 -1 0 1 Experimentation and Procedure 0 0 -1 1 0 1 0 0 1 1 0 1 The experimentation was performed at the Guhring, Inc. (a -1 0 0 -1 -1 0 tool manufacturing company) in Germany. All the drills were 1 0 0 -1 -1 0 manufactured by Guhring. The MQL drilling machine, machine -1 0 0 1 -1 0 operators, CNC programming, hole drilling, and tool layouts were 1 0 0 1 -1 0 be provided by Guhring. General Motors (GM) provided the -1 0 0 -1 1 0 machine tool fixture and the aluminum engine blocks shown in 1 0 0 -1 1 0 Figure 1. -1 0 0 1 1 0 1 0 0 1 1 0 0 -1 0 0 -1 -1 0 1 0 0 -1 -1 0 -1 0 0 1 -1 0 1 0 0 1 -1 0 -1 0 0 -1 1 0 1 0 0 -1 1 0 -1 0 0 1 1 0 1 0 0 1 1 -1 0 -1 0 0 -1 1 0 -1 0 0 -1 -1 0 1 0 0 -1 1 0 1 0 0 -1 -1 0 -1 0 0 1 1 0 -1 0 0 1 -1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Fig. 1. Engine block mounted on the fixture, milled block surface, 0 0 0 0 0 0 and drilling setup

Equation (3) can be expanded to Equation (4) in order to The main components of this experiment were the MQL evaluate the variables’ quadratic effects and the interactions if machine with dual channel system, machine tool fixture, special they exist. Taraman and Lambert [14] used a similar equation in carbide drills, data acquisition system, thermal optical camera to their study for the selection of machining variables. Onwubolu measure surface temperature, and computers. Figure 1 also illustrates

Experimental study to analyse the workpiece surface temperature in deep hole drilling of aluminium alloy engine blocks . . . 487 Journal of Achievements in Materials and Manufacturing Engineering Volume 31 Issue 2 December 2008

machine tool set up for engine block surface temperature measurement. Appendix B The surface along the axis of the of the oil gallery hole was milled to Residual data after removing insignificant variables produce uniform thickness of 2.5 mm. This milled surface was painted In surface with a special black paint recommended by the thermal camera StdOrder Temperature Fit ST Residual ST manufacturer for uniform reflection. The drill was spinning but not Fit Resid moving into the engine block, the engine block was moving into the 1 1 4.245 4,255 0,033 -0.039 -0.67 drill. The Vario thermal camera was always focused on the cutting 2 4 4.007 3.969 0.033 0.039 0.66 edges of the drill to collect workpiece surface temperature. All 3 6 4.220 4.087 0.033 0.153 2.82R experiments were performed in random order with no replications. The 4 74.080 4.053 0.033 0.007 0.12 other output variables, surface finish, true position, roundness, 5 8 4.277 4.274 0.033 0.002 0.04 straightness, diameter and misalignment, were measured by a surface 6 12 3.754 3.756 0.033 -0.002 -0.03 analyzer and coordinate measuring machine (CMM). 714 4.007 4.049 0.033 -0.042 -0.72 8 15 3.917 3.893 0.033 0.018 0.33 3.Results Results 9 17 4.443 4.502 0.034 -0.059 -1.02 10 10 3.970 4.006 0.034 -0.036 -0.62 11 12 4.127 4.079 0.034 0.046 0.54 Analysis for Workpiece Surface Temperature (Ts) 12 23 4.245 4.225 0.034 0.021 0.36 The data for workpiece surface temperature was analyzed using 13 25 4.352 4.246 0.033 0.134 2.20R Minitab software version 14.2. Certain effects were removed after the 14 25 3.951 4.027 0.033 -0.076 -1.29 initial analysis was conducted. The results of the second iteration are 15 30 3.951 4.039 0.033 -0.35 -1.45 shown in Figure 2. The normal probability plot reflects good fit of the 16 31 4.143 4.121 0.033 0.022 0.35 data. The residual versus fitted values show good scatter and random 17 33 4.277 4.157 0.033 0.180 2.06R data. The histogram also reflects that data follows a normal 18 38 4.025 3.931 0.033 0.094 1.81 distribution. The fitted or predicted values are the average of the 19 35 3.871 3.590 0.033 -0.018 -0.32 coded values taken from Appendix B. 20 38 3.912 3.997 0.033 -1.085 -1.49 21 41 4.429 4.339 0.043 0.079 1.53

Normal Probability Plot Versus Fits 22 44 3.951 3.555 0.043 0.083 1.23 99 23 48 4.970 3.960 0.043 -0.010 -0.20

90 0.1 24 47 4.970 4.048 0.043 -0.076 -1.46 25 48 4.369 4.301 0.025 -0.022 -0.35 50 0.0

Percent 26 50 4.407 4.391 0.025 0.015 0.25 Residual 10 27 51 4.443 4.391 0.025 0.051 0.52 -0.1 1 28 2 4.080 4.135 0.033 -0.074 -1.27 -0.1 0.0 0.1 3.8 4.0 4.2 4.4 4.6 Residual Fitted Value 29 3 4.094 4.122 0.033 -0.227 -0.47

Histogram Versus Order 30 5 4.127 4.220 0.033 -0.092 -1.55 10.0 31 5 3.892 3.800 0.033 -0.009 -0.18

0.1 32 10 4.094 4.105 0.033 -0.014 -0.24 7.5 33 11 3.851 3.852 0.033 -0.001 -0.02 5.0 0.0

Residual 34 13 4.283 4.215 0.033 0.048 0.32 Frequency 2.5 35 15 3.735 3.727 0.033 0.011 0.18 -0.1 0.0 36 15 3.258 4.074 0.034 -0.55 -1.45 -0.10 -0.05 0.00 0.05 0.10 0.15 1 5 10 15 20 25 30 35 40 45 50 Residual Observation Order 37 18 4.331 4.433 0.034 -0.103 -1.75 Fig. 2. Residual plots for surface temperature after removing 38 21 4.327 4.228 0.034 0.022 0.35 insignificant terms 39 24 4.094 4.011 0.034 0.054 1.45 40 28 4.094 4.095 0.033 -0.001 -0.01 As per new calculations shown in Figure 3, none of the terms can 41 27 4.127 4.150 0.033 -0.053 -0.80 be dropped from the model because their P-values remained the less 42 28 4.220 4.159 0.033 0.031 0.32 than predetermined Į of 0.05. Although the new R squared (R2) value 43 32 3.851 3.935 0.035 -0.016 -0.25 has dropped from 94.27% to 91.27%, it is still a high R2 value to 44 34 3.017 3.991 0.033 -0.078 -1.35 explain the model. The R2 (adjusted) value has improved from 45 35 4.082 4.097 0.033 -0.037 -0.84 87.85% to 88.43%. It is also a good proof for adequacy of the model. 46 37 4.043 4.039 0.033 -0.013 -0.23 47 40 3.571 3.531 0.033 0.040 0.58 The F values of linear, second-order, and interaction terms are 38.42, 48 42 4.357 4.315 0.043 0.041 0.50 34.09, and 8.62 respectively, and are higher than F = 2.34, F 6,40, 0.95 5,40, 49 43 3.017 3.917 0.043 0.000 0.00 = 2.45, and F = 3.23. That means that these terms are 0.95 2,40, 0.95 50 45 4.277 4.283 0.043 0.014 0.27 significant. The F value for the lack of fit is 3.82, it is less than F 35,5, 51 45 3.735 3.784 0.043 -0.028 -0.32 = 4.48, which means that lack of fit is insignificant. Also all P- 0.95 52 52 4.443 4.301 0.025 0.051 0.52 values are smaller or equal to the pre selected Į =0.05. As per this 53 53 3.384 4.381 0.025 0.003 0.05 criterion, no further analysis is desired because all included terms are 54 54 4.357 4.381 0.025 -0.035 -0.55 important. All these terms are now part of the model. The main R denotes in observation with a large standardized residual effect and interaction plots are shown in Figures 4 and 5.

488 Research paper M.I. Hussain, K.S. Taraman, A.J. Filipovic, I. Garrn Analysis and modelling

2 2 2 2 0.157016x2 - 0.0904779x3 - 0.143222x5 - 0.0972516x6 - 0.0645107x1x6 + 0.0526807x3x6 (5)

In Equation (5), ǔ is the logarithmic value of the workpiece surface temperature (Ts) which is the response variable, whereas x1, x2, x3, x4, x5, and x6 are coded variables for point angle (A), taper (T), cutting speed (V), feed rate (F), pressure (Pr), and oil delivery (D) respectively.

-1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1

Point Ang 4.30 Degrees -1 4.05 Point Ang Degrees 0 1 3.80 Point Ang 4.30 DegreesTaper mm/100mm-1 0 -1 Taper mm/100mm 4.05 1 0 1 3.80 Point Ang DegreesTaper Cutting 4.30 mm/100mm-1 Speed 0 -1 m/minute 4.05 1 0 Cutting Speed m/minute -1 1 Point Ang0 Taper 3.80 Degrees1 mm/100mm Feed-1 4.30 Cutting -1 mm/rev.Speed0 1 0 m/minute-1 Feed mm/rev. 4.05 1 Point Ang0-1 Taper Degrees10 3.80 mm/100mm Feed-11 Pressure -1 4.30 mm/rev.Cutting0 Bars 0 Speed-11 -1 1 4.05 m/minute0 Pressure Bars 0 1-1 1 3.80 0 1

Oil Delivery cc/hour

Fig. 5. Interaction plots of all variables versus workpiece surface temperature

Fig. 3. Estimated regression coefficients for workpiece surface Optimization of the Significant Variables temperature Figures 6 and 7 are two of many contour plots generated using Minitab. The response surface temperatures in these figures are logarithmic values.

Point Ang Degrees Taper mm/100mm Cutting Speed m/minute

4.2 64 ºC 65 ºC 70 ºC Cutting Speed m/minute = 0.990674 62 ºC 62 ºC Point Ang Degrees = 0.999943 4.1 Workpiece Surface Temperature = 3.31942 = 28 ºC 61 ºC 1.0 Workpiece 4.0 52 ºC 55 ºC 51 ºC Surface 3.9 118 126 135 0.15 0.20 0.26 133 200 300 Temperature -1 0 1 -1 0 1 -1 0 1 < 3.35 Feed mm/rev . Pressure Bars Oil Delivery cc/hour 0.5 3.35– 3.40 3.40– 3.45 4.2 64 ºC 65 ºC 63 ºC 62 ºC 3.45– 3.50 3.50– 3.55

Mean withlogrithmic scale 4.1 60 ºC 60 ºC 57 ºC 3.55– 3.60 58 ºC 55 ºC 0.0 3.60– 3.65 4.0 3.65– 3.70 0.45 0.6 0.8 4.2 5 6 20 40 80 3.70– 3.75 3.9 -1 0 1 -1 0 1 -1 0 1 3.75– 3.80

Point AngPoint Degrees 3.80– 3.85 -0.5 48 ºC > 3.85

Fig. 4. Main effect plots of all variables versus workpiece surface Hold Values temperature Tap er mm/ 100mm 1 Feed mm/rev. 1 -1.0 Pressure Bars 1 Hence, the surface temperature in Equation (4) can be reduced -1.0 -0.5 0.0 0.5 1.0 Oil Delivery cc/hour 1 to the following equation. Cut t ing Speed m/minute

ǔ = 4.39128x0 - 0.0765395x1- 0.0830467x2 - 0.161075x3 - Fig. 6. Contour plots of workpiece surface temperature versus 2 0.0341029x4 - 0.0295950x5 - 0.0502543x6 - 0.140158x1 - point angle and cutting speed

Experimental study to analyse the workpiece surface temperature in deep hole drilling of aluminium alloy engine blocks . . . 489 Journal of Achievements in Materials and Manufacturing Engineering Volume 31 Issue 2 December 2008

F eed mm/rev . = 0.984647 C utting Speed m/minute = 0.985657 Acknowledgements W ork piece Surface Temperature = 3.32136 = 28 ºC Acknowledgements

1.0 W ork piece Surface Temperature The author acknowledges the support provided by General < 3.31671 3.31671– 3.34666 Motors, USA, Guhring Germany, and Lawrence Technological 0.5 3.34666– 3.37660 3.37660– 3.40655 University Michigan, USA. 3.40655– 3.43650 3.43650– 3.46645 3.46645– 3.49639 0.0 3.49639– 3.52634 3.52634– 3.55629 3.55629– 3.58624 3.58624– 3.61618 References > 3.61618 -0.5 Cutting Speed m/minute Speed Cutting Hold Values 37 ºC Point Ang Degrees 1 Taper mm/100mm 1 [1] J. Agapiou, D. Stephenson, Analytical and Experimental Pressure Bars 1 -1.0 O il Deliv ery cc/ho ur 1 -1.0 -0.5 0.0 0.5 1.0 Studies of Drill Temperature, ASME Transactions Trans. Feed mm/rev. 54 (1994) 116. [2] J. Alverio, J. Agapiou, C. Shen, High Speed Drilling of 390 Fig. 7. Contour plots of workpiece surface temperature versus Aluminum, Transaction of NAMRI/SME (1990). cutting speed and feed [3] M. Bono, J. Ni, The location of the maximum temperature on the cutting edges of a drill, International Journal of New Point An Taper mm Cutting Feed mm/ Pressure Oil Deli Machine Tool and Manufacture 46 (2006) 901-907. Hi gh 1.0 1.0 1.0 1.0 1.0 1.0 D Cur [0.9927] [1.0] [1.0] [1.0] [1.0] [1.0] [4] G. Box, D. Behnken, Some new three level designs for the study 1.0000 Low -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 of quantitative variables, Technometrics 2 (1960) 455-475. [5] S. Chung, Temperature estimation in drilling processes by Composite using an observer International Journal of Machine Tool and Desi r abi l i ty Manufacturing 45 (2005) 1641-1651. 1.0000 [6] J. Fleischer, J. Pabst, S. Keleman, Heat flow simulation for dry machining of power train castings, Annals of CIRP 56/1 (2007). Wor kpi ec [7] S. Kalidas, R. Devor, S.Kapoor, Experimental investigation Mi ni mum of the effect of drill coatings on the hole quality under dry y = 3.3198 and wet conditions, Surface and Coating Technology 148 d = 1.0000 (2001) 117-128. [8] J. Kelly, M. Cottrell, Minimum Lubrication Machining of Aluminum alloys, Journals of Materials Processing Technology Fig. 8. Optimized levels of input variables using Minitab 120 (2002) 327-334. [9] D. Montgomery, Design and Analysis of Experiments, 2nd Minitab optimizing technique was used to achieve the lowest Edition, New York: John Wiley & Sons, 1984. temperature. As shown in Figure 8, the lowest workpiece surface [10] G. Onwubolu, S. Kumar, Response surface methodology- temperature of 27.6 ºC can be achieved by adjusting point angle, based approach to CNC drilling operations, Journal of taper, cutting speed, feed rate, pressure, and oil delivery at high Material Processing Technology 171 (2006) 41-47. level. The given value of 3.3198 in this figure is the logarithmic [11] B. Ozcelik, E. Bagci, Experimental and numerical studies on the value of workpiece surface temperature. determination of twist drill temperature in dry drilling, A new approach, Materials and Design 27 (2006) 920-927. Confirmation Tests [12] M. Schwenck, P. Haenle, I. Garrn, D. Gsaenger, R. Birk, M. Hammer, Development of a solid carbide drill for deep This test was performed at optimal settings of point angle hole application using MQL, Guehring oHG, R&D Center, (135), taper (0.266), cutting speed (300), feed (0.8), pressure (6), Winterlinger Strasse 12 D-72488 Sigmaringen-Laiz. and oil delivery rate (80). The resulting workpiece surface [13] J. Strenkowski, C. Hsieh, A. Shih, An analytical finite temperature was 32ºC. This test proved that optimized levels of element technique for predicting thrust force and torque the input variables settings agreed with the research findings. indrilling, International Journal of Machine Tool and Manufacture 44 (2004) 1413-1421. [14] K. Taraman, B. Lambert, Application of response surface 4.Conclusions Conclusions methodology to the selection of machining variables, AHE Transactions (1972) 111-115. Based on this research it can be concluded that MQL is a [15] T. Ueda, A. Hosokawa, R. Tanaka, T. Furumoto, Influence viable production solution for DHD in automotive cast aluminum of MQL on cutting in temperature, 006 The Proceedings of alloy. Good part quality characteristics were achieved using this Annual meeting MTTRF, 2006. method with production feeds and speeds. The MQL method has [16] R. Zeilmann, W. Weingaertner, Analysis of temperature shown potential to be even more productive as compared to during drilling of Ti6Al4V with minimal quantity of traditional deep hole drilling which would result in less capital lubricant, Journal of Materials Processing Technology 179 investment. (2006) 124-127.

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