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IRJET-Optimization of Machining Parameters Affecting Metal Removal Rate of Aluminium Alloy 6082 in Dry End Milling Operation on VMC
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IRJET-Optimization of Machining Parameters Affecting Metal Removal Rate of Aluminium Alloy 6082 in Dry End Milling Operation on VMC
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1799 OPTIMIZATION OF MACHINING PARAMETERS AFFECTING METAL REMOVAL RATE OF ALUMINIUM ALLOY 6082 IN DRY END MILLING OPERATION ON VMC Akhilesh Chaudhary1, Jitendra Kumar Verma2 1Faculty, Dept. of Mechanical Engineering, Subharti Polytechnic College, Meerut, UP, India 2Assistant Professor, Dept. of Mechanical Engineering, Institute of Tech. and Management, Meerut, UP, India ------------------------------------------------------------------------***------------------------------------------------------------------------ Abstract - Every manufacturing industry wants to reduce the manufacturing cost and increase the production rate of a product. The aim of this project work is to optimize the selected control factors in order to increase the metal removal rate in dry end milling operation. The machining parameters are spindle speed, feed rate and depth of cut. These parameters are investigated at three different levels and 27 experiments were performed on the basis of L27 orthogonal array of Taguchi method. The machining operation performed on vertical machining centre in dry condition. These experimental data were analyzed using Minitab software to identify the most significant factor. The material used in this project is Al6082 Aluminium alloy. Keywords: Surface Roughness, Taguchi, ANOVA, Minitab, End mill. 1. INTRODUCTION In current age due to lot of competition every industry wants to produce a good quality of product at higher production rate. In machining process production rate is mainly affected by metal removal rate. The metal removal rate is depending upon machining parameter. The metal removal rate is selected such that they not affect too much the other properties like surface roughness, power consumption and strength etc. 1.1 Metal Removal Rate (MRR) Metal removal rate is defined as the volume of metal remove in unit time during machining operation. The formula for calculating MRR in end milling is - ( ) (mm3/sec) Where f = feed rate (mm per min) W = width (mm) d = depth of cut (mm) 2. LITERATURE REVIEW The many researchers have research on material Al6082, but very few researchers has research work on End milling operation on Al6082. In this present work we are selected the material Al6082. The objective of present work is to optimize the cutting parameter of Al6082 in dry end milling operation. Kurt et al. [1] (2008) were performed an experiment on the role of different coating, point angle, cutting speed and feed rate on the hole quality (hole size, surface roughness, roundness and radial deviation of produced hole) in drilling of Al 2024 alloy. They conclude that, for low cutting speed and feed rate and the best hole quality obtained from near the bottom of the produced hole. Ali Riza Motorcu et al. [2] (2010) is conducted an experiment to find the optimum machining parameter by Taguchi method. The material AISI 8660 hardened alloy steel is used in this experiment. The cutting speed, feed, depth of cut and nose radius are the machining parameter for optimization surface roughness. The ceramic based cutting tool is used in this experiment. The result shows that the feed rate is the most dominant factor for surface roughness followed by depth of cut and nose radius. B Fnide et al. [3] (2011) were conducted an experiment by the application of response surface methodology for evaluating the cutting forces in hard turning of AISI H11 hot work tool steel. Machining was done under dry conditions by a mixed ceramic tool (insert CC650 of chemical composition 70%Al2O3+30%TiC. They found that the depth of cut was the dominant factor affecting cutting force components. The tangential cutting force was more affected rather than radial and axial forces. The radial force was more affected by cutting speed rather than tangential and axial forces. Ilhan Asilturk ET al. [4] (2011) were conducted the experiment for optimization of cutting parameters for minimum surface roughness (Ra & Rz). Nine experiments had been carried out using L9 orthogonal array in CNC turning. Dry turning tests were carried out on hardened AISI 4140 with coated carbide tools and result observed that feed rate were the most dominated parameter for surface roughness.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1800 M. Kaladhar et al. [5] (2011) were conducted an experiment on AISI 304 for surface finish and metal removal rate and investigate the effects of process parameters on it. For this experiment they used ANOVA software for analysis. They found that the feed and nose radius were the most significant parameters for surface roughness and for metal removal rate, depth of cut and feed are the significant factors. Aruna et al. [6] (2012) were conducted an experiment for optimizing cutting parameters with respect to the data obtained from high speed lathe machining of INCONEL 718 material, a nickel based super alloy by Taguchi and RSM optimisation method. The cutting speed was the most dominated affected parameter for surface roughness and tool wear. Mahesh Babu et al. [7] (2012) were studied the various characteristics of the surface quality on machining of hybrid (Al–SiC–B4C) composites. They found that the feed rate was the most dominant parameter followed by cutting speed, which affected the surface roughness. V. S. Thanhgarasu et al. [8] (2012) were conducted an experiment to find the optimum cutting parameters for high speed CNC milling, in which they used Taguchi, based Box-Behnken RSM (Response Surface Methodology) to develop prediction formula and Multi Objective Genetic Algorithm (MOGA). The Result was found that, the Ra was more affected by the upward variation of the depth of cut and feed rate than that of the spindle speed but had a significant influence over the responses. P. Raveendran et al. [9] (2016) were conducted an experiment to find the optimum cutting parameters for Glass fiber reinforced polymers (GFRP) material for good surface finish by using Response Surface Methodology (RSM). The result shows that depth of cut was the main influencing factor on the surface roughness, followed by the feed rate and cutting speed. H R Ghan et al. [10] (2017) were provides literature review on machining parameters, such as cutting speed (Vc), feed (f) and depth of cut (t), of different material. They found that parameters play a very vital role for the machining and utilized in the industries. Mayur N. Trimbakwade et al. [11] (2017) were conducted an experiment to find the optimum cutting parameters for CNC face milling for surface roughness and metal removal rate for Inconel 718 by Using Taguchi Method. Inconel 718 was mostly used nickel based super alloy. The result was analysed in ANOVA and they found the set of optimum value for minimum surface roughness. 3. EXPERIMENTAL DESIGN 3.1 Taguchi Method The Taguchi Methods was developed by was Genichi Taguchi. He was a Japanese engineer who began working for the telecommunications company, Electrical Communications Lab, a part of NT&T, in 1950’s. Taguchi method is used evaluating and implementing improvements in the products, optimization of the objectives function. Optimization means determination of best levels of control factors that maximize the signal- to – noise ratio. 3.2 Signal- to- Noise Ratio Signal-to-noise ratio is very important and useful parameter in taking into account of goal and variation in comparing two sets of samples. Signal–to- noise ratio is the log functions of a given output. It is used for optimization of objectives function. It is also help in data analysis and prediction of results. Signal to noise ratio formula are as follow- Smaller the better ( ∑ ) Nominal the best (̅ ) Larger the better ( ∑ ) 4. EXPERIMENTAL WORK 4.1 Workpiece Material The workpiece material used in this experiment is Aluminium Alloy 6082. It is a medium strength alloy. It is mostly used in high stress application, trusses, bridges, cranes and transport application etc. The material properties are given below. Table -1: Material Properties Density 2.71 g/cm3 Young modulus 71 GPa UTS 140 to 330 MPa
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1801 Yield strength 90 to 280 MPa Hardness Brinell 91 HB Material composition Al- 95.2 to 98.3% Cr- 0.25% max Cu- 0.1% max Fe- 0.5% max Mg- 0.6 to 1.2% Mn- 0.4 to 1% Si- 0.7 to 1.3% max Ti- 0.1% max Zn- 0.2% max Fig -1: Workpiece 4.2 CNC Vertical machining Centre CNC Vertical Machining Centre is an automatic machine tool. It is controlled by computer executes pre- programmed commands. The machine table is moved along x and y axis and machine spindle move along z axis. The G and M codes are based on the three dimensional Cartesian coordinate system and controlled the machine. In CNC vertical machining centre the cutting tool is hold on a vertical spindle and feed is provided by the machine table. Fig -2: Vertical Machining Centre Table -2: VMC Specification Machine Specification M/C model no. TURBO-450-R40 Serial no. 6225/1215/66844 Capacity 450 W/hr Spindle speed 6000 rpm Temperature range 100 to 450 C Frequency 50 HZ Coolant System Tank capacity 450 litter Type of coolant Servo Cut-S Coolant/water ratio 1:20 Coolant around spindle 50 lpm @ 1.8 bar Coolant wash 60 lpm @ 4 bar Lubrication System Tank capacity 3 litter Type of oil Servo way- H68 Max. working pressure 15 bar
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1802 4.3 Cutting Tool or End Mill In this operation multipoint end milling cutter is used for machining. The material of cutting tool is carbide. The diameter of the cutting tool is 10 mm. Fig -3: End Mill 4.5 Machining Parameter and Levels of Experiment Table -3: Levels of Experiment Parameter Levels speed(N) in rpm Feed(f) in mm/ min. Depth of Cut(d) in mm Level 1 800 100 0.5 Level 2 1200 150 1.0 Level 3 1600 200 1.5 In this experiment spindle speed, depth of cut and feed rate are taken as the machining parameters. Also we select the three levels of experiment for optimize these parameters.. 4.6 Experimental Data Now with the help of Minitab software orthogonal array is prepared and with the help of this array conduct the experiment. Then this experimental data are analysed in Minitab software and we get S/N ratios. The following experimental data are given below. 5. RESULT ANALYSIS The experimental data are analysed in Minitab software we get the following result. Table -5: Response Table for Signal to Noise Ratios Level N F D 1 26.78418 23.60289 21.5966 2 26.78418 27.12588 27.61749 3 26.78418 29.62494 31.13961 Delta 0 6.02205 9.54301 Rank 3 2 1 Table -4: Experimental Data Exp. no. Speed (N) Feed (f) Depth of cut (d) Cutter dia. (D) Cutting Speed (V) MRR S/N rpm mm/ min. mm mm m/min. mm 3 / sec Db 1 800 100 0.5 10 25.132 8.33 18.413 2 800 100 1 10 25.132 16.7 24.433 3 800 100 1.5 10 25.132 25 27.959 4 800 150 0.5 10 25.132 12.5 21.938 5 800 150 1 10 25.132 25 27.959 6 800 150 1.5 10 25.132 37.5 31.481 7 800 200 0.5 10 25.132 16.7 24.439 8 800 200 1 10 25.132 33.3 30.457 9 800 200 1.5 10 25.132 50 33.979 10 1200 100 0.5 10 37.698 8.33 18.413 11 1200 100 1 10 37.698 16.7 24.439 12 1200 100 1.5 10 37.698 25 27.959 13 1200 150 0.5 10 37.698 12.5 21.938 14 1200 150 1 10 37.698 25 27.959 15 1200 150 1.5 10 37.698 37.5 31.481 16 1200 200 0.5 10 37.698 16.7 24.439 17 1200 200 1 10 37.698 33.3 30.457 18 1200 200 1.5 10 37.698 50 33.979 19 1600 100 0.5 10 50.264 8.33 18.413 20 1600 100 1 10 50.264 16.7 24.439 21 1600 100 1.5 10 50.264 25 27.959 22 1600 150 0.5 10 50.264 12.5 21.938 23 1600 150 1 10 50.264 25 27.959 24 1600 150 1.5 10 50.264 37.5 31.481 25 1600 200 0.5 10 50.264 16.7 24.439 26 1600 200 1 10 50.264 33.3 30.457 27 1600 200 1.5 10 50.264 50 33.979
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1803 Table -6: Response Table for Average MRR Level N F D 1 25 16.66556 12.5 2 25 25 24.9989 3 25 33.3333 37.5 Delta 0 16.66774 25 Rank 3 2 1 It is clear that from both the table, the rank of depth of cut and feed are 1 and 2 respectively. It means that the MRR is maximum affected by depth of cut follow by feed. Also there is no any effect of speed on MRR. 5.1 Effect of Machining Parameters on MRR and S/N Ratio Fig -4: Effects of machining parameter on MRR Fig -5: Effects of machining parameter on S/N Ratio It is clear from figure -4 MRR increases with increasing feed and depth of cut. Also there is no effect of spindle speed on MRR. Also from figure-5 it is clear that S/N ratio increases with increasing feed and depth of cut. Also there is no effect of spindle speed on S/N ratio. 6. CONCLUSIONS In this research work we are selected the material Aluminium alloy 6082 for optimization of machining parameters spindle speed, feed and depth of cut for metal removal rate in dry end milling operation. Taguchi method is used to optimize these parameters. According to L27 orthogonal array 27 experiments are performed. The experimental data is analysed in Minitab software and we get the following result. Factor Rank Depth of cut 1 feed 2 Spindle speed 3 Depth of cut, feed and spindle speed have rank 1, 2 and 3 respectively. It means that the MRR is maximum affected by depth of cut followed by feed. Also there is no effect of spindle speed on MRR The MRR is increases with increasing depth of cut and feed rate. The optimum values of machining parameter for maximum MRR in dry end milling operation are feed 200 mm per min. and depth of cut 1.5 mm. REFERENCES [1] Kurt, M., Bagci, E. and Kaynak, Y., Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes. International Journal of Advance Manufacturing Technology (IJAMT), 40, 2008, 458–469. [2] Ali Riza Motorcu, The Optimization of Machining Parameters Using the Taguchi Method for Surface Roughness of AISI 8660 Hardened Alloy Steel, Strojniški vestnik - Journal of Mechanical Engineering 56, 2010, 6, 391-401. [3] B Fnides, M A Yallese, T Mabrouki, J F Rigal, Application of response surface methodology for determining cutting force model in turning hardened AISI H11 hot work tool steel, Sadhana, Vol. 36, Part 1, 2011, pp.109–123. [4] Asiltürk, Ilhan, Akkus, Harun, Dtermining the effect of cutting parameters on surface roughness in hard turning using the Taguchi
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1804 method, Measurement, Vol. 44, 2011, pp. 1697– 1704. [5] M. Kaladhar, K. Venkata Subbaiah, Ch. Srinivasa Rao., Determination of Optimum Process Parameters during turning of AISI304 Austenitic Stainless Steels using Taguchi method and ANOVA, International Journal of Lean Thinking (IJLT), Vol. 3, Issue 1, 2012, pp.1-19. [6] M. Aruna and V. Dhanalakshmi, Optimisation of turning parameters of Inconel 718 alloy using RSM, International Journal of Manufacturing Technology and Management (IJMTM), Vol. 25, Nos. 1/2/3, 2012, pp.113–134. [7] Mahesh Babu, M.S. Aldrin Sugin, N. Muthukrishnan, Investigation on the characteristics of surface quality on machining hybrid metal matrix composite (Al–SiC B4C), Procedia Engineering 38, 2012, 2617–2624. [8] V. S. Thangarasu, G. Devaraj, R. Sivasubramanian, High speed CNC machining of AISI 304 stainless steel; Optimization of process parameters by MOGA, International Journal of Engineering, Science and Technology (IJEST),Vol. 4, No. 3, 2012, pp. 66-77 [9] P. Raveendran and P. Marimuthu, optimization of machining parameters for minimizing surface roughness in turning operations of gfrp rod based on response surface methodology, International Journal of Advanced Engineering Technology (IJAET) Vol.7, Issue 2,2016, 1126- 1129. [10] H R Ghan, A A Hashmi and M M Dhobe, International Journal of Advance Research, Ideas and Innovations in Technology (IJARIIT)Vol. 3, Issue 2, 2017, 2454-132X. [11] Mayur N. Trimbakwade and Manish T. Shete, International Research Journal of Engineering and Technology (IRJET) Vol. 4, Issue 4, 2017, 2395-0072. [12] M. D. Selvam, A. K. S Dawood , G. Karuppusami., Optimization of Machining Parameters For Face Milling Operation In A Vertical CNC Milling Machine Using Genetic Algorithm, Engineering Science and Technology: An International Journal (ESTIJ), Vol.2, No. 4, 2012, 2250-3498. [13] Avinash A thakre, Optimization of Milling Parameters for Minimizing Surface Roughness Using Taguchi‘s Approach, International Journal of Emerging Technology and Advanced Engineering (IJETAE, )Vol. 3, Issue 6, 2013, pp- 226-230. BIOGRAPHIES Faculty, Dept. of Mechanical Engineering, Subharti Polytechnic College, Meerut, UP, India Assistant Professor, Dept. of Mechanical Engineering, Institute of Tech. and Management, Meerut, UP, India
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