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http://www.iaeme.com/ijmet/index.asp 203 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (...
Shrikant Borade and Dr.M.S.Kadam
http://www.iaeme.com/ijmet/index.asp 204 editor@iaeme.com
Cite this Article: Shrikant Bor...
Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
http://...
Shrikant Borade and Dr.M.S.Kadam
http://www.iaeme.com/ijmet/index.asp 206 editor@iaeme.com
2.5. Selection of process param...
Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
http://...
Shrikant Borade and Dr.M.S.Kadam
http://www.iaeme.com/ijmet/index.asp 208 editor@iaeme.com
Main Effect Plot for Signal to ...
Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
http://...
Shrikant Borade and Dr.M.S.Kadam
http://www.iaeme.com/ijmet/index.asp 210 editor@iaeme.com
Main Effect Plot for Signal to ...
Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
http://...
Shrikant Borade and Dr.M.S.Kadam
http://www.iaeme.com/ijmet/index.asp 212 editor@iaeme.com
6. ACKNOWLEDGEMENTS
The authors...
Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
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COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL2O3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE

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The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Application of cutting fluids changes the performance of machining operations because of their lubrication, cooling, and chip flushing functions. But the conventional cutting fluids are not that effective in such high production machining, particularly in continuous cutting of materials likes steels. So Nanofluids have novel properties that make them potentially useful in heat transfer medium in cutting zone and Minimum quantity lubrication (MQL) presents itself as a viable alternative for turning with respect to tool wear, heat dissipation, and machined surface quality.

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COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL2O3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE

  1. 1. http://www.iaeme.com/ijmet/index.asp 203 editor@iaeme.com International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 1, Jan-Feb 2016, pp. 203-213, Article ID: IJMET_07_01_021 Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=7&IType=1 Journal Impact Factor (2016): 9.2286 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL2O3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE Shrikant Borade Master of Manufacturing Engineering Student, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, 431001, India Dr.M.S.Kadam Professor and Head of Department, Mechanical Engineering, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, 431001, India ABSTRACT The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Application of cutting fluids changes the performance of machining operations because of their lubrication, cooling, and chip flushing functions. But the conventional cutting fluids are not that effective in such high production machining, particularly in continuous cutting of materials likes steels. So Nanofluids have novel properties that make them potentially useful in heat transfer medium in cutting zone and Minimum quantity lubrication (MQL) presents itself as a viable alternative for turning with respect to tool wear, heat dissipation, and machined surface quality. In this study Vegetable oil with MQL is compared with Al2O3 Nanfluids and MQL for the turning of EN353 steel based on experimental measurement of surface roughness and temperature. This study prepares the effect of MQL and Nano fluids with 3% volume Al2O3 on the machinability characteristics of EN353 steel mainly with respect to surface roughness and temperature dissipation. Experimental analysis for two different conditions – MQL + Vegetable Oil and MQL + Al2O3 Nanofluids were carried out. Key words: Al2O3 Nanofluids, MQL, CNC Machining, EN353 steel, Surface Roughness, Temperature.
  2. 2. Shrikant Borade and Dr.M.S.Kadam http://www.iaeme.com/ijmet/index.asp 204 editor@iaeme.com Cite this Article: Shrikant Borade and Dr.M.S.Kadam. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature. Dynamical Analysis of Silo Surface Cleaning Robot Using Finite Element Method, International Journal of Mechanical Engineering and Technology, 7(1), 2016, pp. 203-213. http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=1 1. INTRODUCTION Cutting fluids are employed in machining operations to improve the tribological processes, which occur when two surfaces, the tool and work-piece make contact. The cutting fluid improves the tool life, surface conditions of the work-piece and the process as a whole. It also helps in carrying away the heat and debris produced during machining. On the other hand, the cutting fluids have many detrimental effects. Machining experiences high temperatures due to friction between the tool and work piece, thus influencing the work piece dimensional accuracy and surface quality. Machining temperatures can be controlled by reducing the friction between tool–work piece and tool–chip interface with the help of effective lubrication. Cutting fluids are the conventional choice to act as both lubricants and coolants. But, their application has several adverse effects such as environmental pollution , dermatitis to operators, water pollution and oil contamination during disposal [1,2]. Further, the cutting fluids also incur a major portion of the total manufacturing cost. All these factors prompted investigations into the use of biodegradable coolants or coolant free machining. Hence, as an alternative to cutting fluids, researchers experimented with dry machining, coated tools, cryogenic cooling, minimum quantity lubrication (MQL) and solid lubricants. Dry machining, has been reported to be capable of eliminating cutting fluids with the advancement of the cutting tool materials [3]. Dry machining requires less power and produces smoother surface than wet machining at specified cutting conditions [4]. In tribological applications of hard protective coatings the toughness is as important as their hardness, since both properties have great influence on the wear resistance of coated tools [5]. Itakuraetal. [6] conducted dry turning experiments to identify the tool wear mechanisms with coated cemented carbide tool while machining in conel 718. During continuous cutting almost no rake wear but only flank wear appeared. The primary function of a cutting fluid in wet machining operations is to cool, to lubricate, and to remove the chips. Emulsions or straight oils are generally used depending on the manufacturing operation and machining task involved. Straight oil is a cutting fluid that is composed of mineral oil or vegetable oil and is mainly used as a lubricant. Research by Rao DN, Srikant RR (2006) showed straight oil is not intended to be mixed with water. Emulsions possess excellent heat transfer characteristics because of their high water content. Straight oils excel when a high degree of lubricity is required. Both media guarantee efficient chip transport, when compressed air is used instead of a cooling lubricant, the lubrication benefit of the fluid is lost. The coolant effect is much less pronounced than with water or oil. Water and oil are also superior to air in terms of chip transport characteristics.
  3. 3. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature http://www.iaeme.com/ijmet/index.asp 205 editor@iaeme.com 2. EXPERIMENTAL SETUP 2.1 Selection of work material The workpiece material used is EN353 steel in the form of round bars of 30 mm diameter and length of 80 mm. The composition of material is Table 1 Composition of EN353 Steel chemical composition of EN 353 Steel C Mn Cr Si Mo Ni 0.18% 0.93% 1.11% 0.26% 0.11% 1.34% EN353 steel is widely used for Machining components in various industries. This material has significant application in automotive industry. Typical applications of this material are crown wheel, crown pinion, bevel pinion, bevel wheel, timing gears, king pin, pinion shaft, differential turn ion etc. The gears especially crown wheel and pinion are one of the most stress prone parts of a vehicle, etc, which are made of EN353 steel. This material EN353 steel is used in heavy duty chains, conveyor chains. The workpiece of EN353 Steel was firstly hardened followed by oil quenching at a temperature of 850°C to achieve a hardness of 60 HRC throughout. A rough turning pass was conducted initially to eliminate the run out of the work piece, after that diameter obtained for experimentation is 60mm. 2.2. Selection of Insert Based on Literature survey, the Tool selected for this process was CBN CNMG (Cubic Boron Nitride) 7025 Manufactured by Sandvik Specifications as CNMG 120408. 2.3. Selection of lubricant Selection of cutting fluid is important in order to maintain better tool life, less cutting forces, lower power consumption, high machining accuracy and better surface integrity etc. Here Vegetable Oil {Max mix ST-2020}, is used as cutting fluid in MQL. ST-2020 is an environmentally acceptable vegetables oil based lubricant. ST- 2020 will yield the lowest net manufacturing costs of any fluid as found by JungSoo, et al. (2011.) 2.4. Preparation of Nano fluids For preparation of Al2O3 nanoparticles the Precipitation Method is used. Alumina prepared by this method is used for preparation of Nanofluids. In this work the alumina (Al2O3) Nanoparticles are mixed with vegetable oil, as a base fluid to make Al2O3 Nanofluid. Nano Al2O3 particles are selected due to their superior tribological and antitoxic properties based on study of Pil-Ho Lee, et al. (2012). The method used to make Nano fluid is given below. Take one gram of Al2O3 Nano particles and directly mix with 100 ml vegetable oil as a base fluid and prepare the sample. 1 Vol% of Al2O3 Nano fluid = 100 ml of vegetable oil + 1gm of Al2O3 Nano particles. 3Vol% of Al2O3 Nano fluid = 100 ml of vegetable oil + 3gm of Al2O3 Nano particles. The above composition has to be mixed continuously about 8 to 9 hours using Magnetic stirrer. The size of Al2O3 particles is about 5 Nm. Here after, Nanofluid means a mix of MQL and Al2O3 particles.
  4. 4. Shrikant Borade and Dr.M.S.Kadam http://www.iaeme.com/ijmet/index.asp 206 editor@iaeme.com 2.5. Selection of process parameter Input process parameters were selected after studying the literature on minimum quantity lubrication/near dry machining. Cutting speed (Vc), cutting feed (f) and Depth of cut (dc) were the input parameters chosen for present work. To investigate the effect of input parameters under the application of MQL on EN353 Steel machining technique. The output parameters chosen were Surface Roughness (Ra) and Temperature (T) at cutting zone to investigate the effect of input parameters under the application of MQL for turning on case hardened EN353 steel. The cutting insert used was CNMG7075 insert. 2.5.1. Machine Figure 1 CNC Lathe (HAAS – Make) A high speed precision CNC Lathe (HASS - MAKE) having maximum turning diameter 406mm, Height of centers over carriage 60 mm Maximum turning length: 762mm, Headstock A2-5 / CAMLOCK D1-3’’ Spindle speed 2,000 rev/min, AC motor drive: Power consumption 9kW, Transverse stroke X-axis 209mm, AC motor drive: intermittent torque 4 to 14 Nm Longitudinal stroke, Z-axis 762mm, Number of fixed tool stations/rotating tool stations. Weight of CNC Lathe 1860kgPower Requirement: 9KVA, 1-Phase:240V@40A, 3-Phase: 208V Table 2 Selected Parameter Sr. no. Parameter Selected Parameter 1 Machine tool: High speed precision CNC Lath (HASS – MAKE) 2 Work pieces EN353 Case Hardening material 3 Cutting tool inserts CBN CNMG 7075 4 Cutting speed 800 – 1200 rpm 5 Feed rate, f: 0.05 - 0.09 mm/rev 6 Depth of cut,(d) 0.05 - 0.09 mm 7 MQL supply: Air:4 bar, Lubricant: 50 ml/h 8 Environment: MQL with Vegetable oil and MQL with Nanofluids. 9 Measurement of surface roughness Surf Tester Least Count - 0.001 10 Measurement of cutting temperature Infrared thermometer Temperature Range- -40°C to 400°C Least Count - 1°C
  5. 5. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature http://www.iaeme.com/ijmet/index.asp 207 editor@iaeme.com 2.6. Experimental procedure The experiments were performed on the basis of Taguchi’s L9 array method. By performing One variable at time (OVAT) analysis and from graph it is found that cutting speed, feed rate, depth of cut are influencing parameters on Surface Roughness and Cutting temperature. According to OVAT analysis following input parameters namely cutting speed, feed rate and depth of cut are selected by keeping other process parameters constant at minimum level which is less influencing on Surface roughness and Cutting temperature [15]. On the basis of surface finish, the selected levels of input parameters which are as follows. Table 3 Selection of Levels from OVAT 3. RESULTS AND DISCUSSION 3.1. For Vegetable Oil with MQL- 3.1.1 For Surface Roughness Table no.5 and Figure no.2 indicates the signal to noise ratio. In this table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and then speed. Analysis of variance (ANOVA) Table 4 Analysis of Variance for Signal to Noise Ratios Level Speed Feed Depth of Cut 1 8.593 9.356 9.327 2 7.122 6.944 7.343 3 6.683 6.097 5.728 Delta 1.910 3.259 3.599 Rank 3 2 1 Response Table for Signal to Noise Ratios (Smaller is better) Table 5 Signal to Noise Ratios - Smaller is better Parameters Units Level 1 Level 2 Level 3 Speed rpm 800 1000 1200 Feed mm/rev 0.05 0.07 0.09 DOC mm 0.05 0.07 0.09 Source DF Seg SS Adj SS Adj MS F P S 2 6.007 6.007 3.003 7.30 0.121 F 2 17.158 17.158 8.579 20.85 0.046 D 2 19.495 19.495 9.747 23.68 0.041 Residual Error 2 0.823 0.823 0.411 Total 8 43.484 Source DF Seg SS Adj SS Adj MS F P S 2 6.007 6.007 3.003 7.30 0.121 F 2 17.158 17.158 8.579 20.85 0.046 D 2 19.495 19.495 9.747 23.68 0.041 Residual Error 2 0.823 0.823 0.411 Total 8 43.484
  6. 6. Shrikant Borade and Dr.M.S.Kadam http://www.iaeme.com/ijmet/index.asp 208 editor@iaeme.com Main Effect Plot for Signal to Noise Ratios MeanofSNratios 12001000800 9 8 7 6 0.090.070.05 0.090.070.05 9 8 7 6 S F D Main Effects Plot (data means) for SN ratios Signal-to-noise: Smaller is better Figure 2 Main Effect Plot for SIGNAL TO NOISE ratios 4.1.2 For Temperature (o C) Table no.7 and Figure no.2 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best temperature the optimum value levels are Speed 800 rpm, Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) – Table 6 Analysis of Variance for Signal to Noise Ratios Response Table for Signal to Noise Ratios (Smaller is better) Table 7 Signal to Noise Ratios - Smaller is better Level Speed Feed Depth of Cut 1 -32.45 -32.71 -32.04 2 -32.73 -32.66 -32.74 3 -32.85 -32.66 -33.25 Delta 0.39 0.05 1.21 Rank 2 3 1 Source DF Seg SS Adj SS Adj MS F P S 2 0.242 0.242 0.121 1.51 0.398 F 2 0.004 0.004 0.002 0.03 0.974 D 2 2.213 2.213 1.106 13.83 0.067 Residual Error 2 0.160 0.160 0.080 Total 8 2.620
  7. 7. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature http://www.iaeme.com/ijmet/index.asp 209 editor@iaeme.com Main Effect Plot for Signal to Noise Ratios MeanofSNratios 12001000800 -32.0 -32.4 -32.8 -33.2 0.090.070.05 0.090.070.05 -32.0 -32.4 -32.8 -33.2 Speed Feed Depth of Cut Main Effects Plot (data means) for SN ratios Signal-to-noise: Smaller is better Figure 3 Main Effect Plot for Signal to Noise Ratios 4.2. For Nanofluids with MQL 4.2.1. For Surface Roughness – Table no.8 and Figure no.4 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) Table 10 Analysis of Variance for Signal to Noise Ratios Response Table for signal to Noise Ratios (smaller is better) Table 11 Signal to Noise Ratios - Smaller is better Level Speed Feed Depth of Cut 1 9.242 9.284 1.0763 2 7.832 7.906 7.702 3 7.442 7.327 6.052 Delta 1.800 1.957 4.711 Rank 3 2 1 Source DF Seg SS Adj SS Adj MS F P S 2 5.380 5.380 2.690 9.690 0.094 F 2 6.061 6.061 3.030 10.92 0.084 D 2 34.289 34.289 17.144 61.75 0.016 Residual Error 2 0.555 0.555 0.277 Total 8 46.285
  8. 8. Shrikant Borade and Dr.M.S.Kadam http://www.iaeme.com/ijmet/index.asp 210 editor@iaeme.com Main Effect Plot for Signal to Noise Ratios MeanofSNratios 12001000800 10.8 9.6 8.4 7.2 6.0 0.090.070.05 0.090.070.05 10.8 9.6 8.4 7.2 6.0 Speed Feed Depth of Cut Main Effects Plot (data means) for SN ratios Signal-to-noise: Smaller is better Figure 5 Main Effect Plot 4.2.2. For Temperature Table no.13 and Figure no.6 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) Table 12 Analysis of Variance for Signal to Noise Ratios Response Table for Signal to Noise Ratios (Smaller is better) – Table 13 Signal to Noise Ratios - Smaller is better Source DF Seg SS Adj SS Adj MS F P S 2 0.893 0.893 0.446 12.73 0.073 F 2 0.064 0.064 0.032 0.92 0.521 D 2 1.294 1.294 0.647 18.46 0.051 Residual Error 2 0.070 0.070 0.035 Total 8 2.322 Level Speed Feed Depth of Cut 1 -31.04 -31.56 -30.96 2 -31.51 -31.36 -31.51 3 -31.80 -31.44 -31.88 Delta 0.76 0.21 0.92 Rank 2 3 1
  9. 9. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature http://www.iaeme.com/ijmet/index.asp 211 editor@iaeme.com 4.2. Comparison of Main Effect of Vegetable oil and Al2O3 Nanofluids used with MQL on Surface Roughness and Temperature – 4.2.1. Surface Roughness (µm) Figure 7 Comparison graph for surface roughness From the figure 7 it is observed that at each experiment the value of surface roughness when used with Al2O3 Nanofluids is comparatively low as compared with Vegetable oil. The smooth surface finish were obtained while using Al2O3 nanofluids with MQL. 4.2.2. Temperature (C) Figure 8 Comparison graph for surface roughness From the figure 8 it is observed that at each experiment the value of temperature when used with Al2O3 Nanofluids is comparative low as compared with Vegetable oil. The main problem with machining any component is heat generated while machining. To cool down the work piece litres of coolant were wasted. Hence the Al2O3 nanofluid with MQL is better option for flooded cooling system.
  10. 10. Shrikant Borade and Dr.M.S.Kadam http://www.iaeme.com/ijmet/index.asp 212 editor@iaeme.com 6. ACKNOWLEDGEMENTS The authors would like to thank Principal Dr. Sudhir Deshmukh MGM’s JNEC Aurangabad, for continuously assessing my work providing great guidance by timely suggestion and discussion at ever stages of this work. We would like to thanks all Mechanical Engineering Department MGM’s JNEC Aurangabad. The authors would also like to thank Indo German Tool Room, Aurangabad and Swajit Engineering Pvt.Ltd, Aurangabad for significant information and kind support for experimentation and making available their testing facility for Research Work. 6. CONCLUSIONS The present work has successfully demonstrated Al2 O3 Nano fluid applications in CNC turning on EN353 steel using Minimum Quantity Lubrication. The surface roughness (Ra) and Temperature (T) were measured under different cutting conditions for diverse combinations of machining parameters. The final conclusions arrived, at the end of this work are as follows:  These experiments show that Surface Roughness and Temperature reduced significantly by machining EN353 steel using Nanofluid (i.e. 3 Vol. % of Al2O3 Nano fluid) as compared to MQL Vegetable oil.  The percentage of error between the predicted and experimental values of the multiple performance characteristics during the confirmation experiments is less than 5 % as it is within limit.  The value of multiple performance characteristics obtained from confirmation experiment is within the 91% to 91.30% confidence interval of the predicted optimum condition. Hence it is concluded that Depth of Cut has significant effect on surface roughness and temperature when used with MQL + Nanofluids. Followed by Feed, Speed in Surface Roughness and Speed, Feed in Temperature. REFERENCES [1] Anselmo Eduardo Diniz, Ricardo Micaroni, (2002), Cutting conditions for finish turning process aiming: the use of dry Cutting, International Journal of Machine Tools & Manufacture 42,899-904 [2] Buongiorno, J. (March 2006), Convective Transport in Nanofluids, Journal of Heat Transfer (American Society of Mechanical Engineers) 128 (3): 240. Retrieved 27 March 2010. [3] Douglas C. Montgomery (2009), Design and Analysis of Experiments, 7th edition Wiley. [4] E.O. Ezugwua,R.B. Da Silvaa,J. Bonneya, Á.R.Machadob, (2005), International Journal of Machine Tools and Manufacture, Volume 45, Issue 9, Pages 1009– 1014. [5] EmelKuram, Babur Ozcelik and ErhanDemirbas (2013), Environmentally Friendly Machining: Vegetable Based Cutting Fluids, Green Manufacturing Processes and Systems, Materials Forming, Machining and Tribology, 23-29 [6] Eastman J A, Phillpot S R, Choi S U S, and Keblinski P, (2004), Thermal transport in nanofluids, A Rev. Mater. Res., 34, 219-230. [7] Funda Kahraman,(2009), The use of Response Surface Methodology for Prediction and Analysis of Surface Roughness of AISI 4140 Steel, Materiali in tehnologije / Materials and technology, 267–270.
  11. 11. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature http://www.iaeme.com/ijmet/index.asp 213 editor@iaeme.com [8] Jung Soo Nam, Pil-Ho Lee and Sang Won Lee (2011), A Study on Machining and Environmental Characteristics of Micro-Drilling Process Using Nanofluid Minimum Quantity Lubrication, ASME 2011 International Manufacturing Science and Engineering Conference, Volume 2 Corvallis, Oregon, USA, June 13–17. [9] M. M. A. Khan (2009), Effects of minimum quantity lubrication on turning AISI 9310 alloy steel using vegetable oil-based cutting fluid” Journal of Materials Processing Technology 209, 5573–5583. [10] Malkin, S. and Sridharan, U., (2009), Effect of minimum quantity lubrication with nanofluids on grinding behavior and thermal distortion, Trans. NAMRI/SME, 37, 629-636. [11] Pil-Ho Lee, Jung Soo Nam, Sang Won Lee, (2012), An Experimental Study on Micro Grinding Process with Nano Fluid Minimum Quantity Lubrication, International Journal of Precision Engineering and Manufacturing, 13(3), pp. 331-338. [12] Yigit Kazancoglu, Ugur Esme, Melih Bayramoglu, Multi-Objective Optimization of the Cutting Forces in Turning Operations Using the Grey-Based Taguchi Method, UDK 621.9.025.5:669.14 ISSN 1580-2949, Original scientific article, MTAEC9, 45(2)105(2011) [13] Nikhil Aggarwal and Sushil Kumar Sharma, Optimization of Machining Parameters in End Milling of AISI H11 Steel Alloy by Taguchi based Grey Relational Analysis, International Journal of Current Engineering and Technology E-ISSN 2277 – 4106, P-ISSN 2347 - 5161 ©2014 INPRESSCO [14] K.C Chang and M-F, Yeh, Grey-relational analysis based approach for data clustering, IEEE Proc,_Vis. Image Signal Process., 152(2), April 2005, pp165- 172. [15] Rao DN, Srikant RR (2006) Influence of emulsifier content on cutting fluid properties. ProcInstMechEng, Part B: EngManuf 220:1803–1806 [16] Rahman, MM, Khan, M M A, and Dhar, N R, (2009), An experimental investigation into the effect of minimum quantity lubrication on cutting temperature for machinability of AISI 9310 steel alloy, Eur. J Sci. Res.,29(4), 502-508. [17] S. Khandekar, M. Ravi Sankar, V. Agnihotri, J Ramkumar (2012), Nano Cutting fluid for enhancement of Metal Cutting Performance, Material and Manufacturing Processses, 27:9, 963- 967 [18] Saidur R, Leong K Y, and Mohammad H A, (2011), A review on application and challenges of nanofluids, Renewable and Sustinable energy Rev., 15(3), 1646- 1668. [19] Mathematical Modelling and Analysis of Machining Parameters in WEDM for WC-10%Co Sintered Composite in the International Journal of Scientific & Engineering Research, 4(8), August-2013 811 ISSN 2229-5518 IJSER © 2013 http://www.ijser.org. [20] S. Bhanuteja and D.Azad. Thermal Performance and Flow Analysis of Nanofluids in A Shell and Tube Heat Exchanger, International Journal of Mechanical Engineering and Technology, 4(5), 2013, pp. 164-172.

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