Comparison & Optimization of Cutting Process Parameters in turning using Taguchi Method

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1 Volume-6, Issue-4, July-August 2016 International Journal of Engineering and Management Research Page Number: Comparison & Optimization of Process Parameters in turning using Taguchi Method Manish Kumar 1, Dr. (Mrs.) Shiena Shekhar 2 1,2 Department of Mechanical Engineering, INDIA ABSTRACT The aim of this work is to find the effect of tool rake angle experimentally on work piece material and tool material on the basis of main cutting force during a turning process of EN 31Steel, MS, Aluminium specimens have been used as work piece materials and carbide as a tool material. The experiments have been obtained with continuous cutting speed, feed rate and depth of cut with five different tool rake angles ( ). For the estimation of the material type effect on the primary cutting force, the no. of tests run has been performed with the same cutting conditions, tool characteristics on the three Specimen materials and tool material. The influence of the tool rake angle on the main cutting force will depend on the type of work piece material, i.e. for EN 31, M.S. specimens the main cutting force has a reducing pattern as the rake angle increases from 0 0 to 16 0 but for the Aluminium specimen the main cutting force was increases with the increasing the tool rake angle. The experimental results show that main cutting force gets the uppermost value at EN 31 specimen & average value at the MS specimen and the low value at the Aluminium specimen. In this thesis we are also finding optimal control parameters to achieve the minimum Surface roughness. It will investigate the process parameters, cutting speed, feed rate and depth of cut on cutting parameter during turning operation. Mainly surface roughness where investigated employing Taguchi s design of experiments with different turning process parameters of EN-31 Steel,MS,Aluminium in turning operation with rake angles of 4 0 and optimized by S/N ratio. Keywords Taguchi Method, turning, Orthogonal Array I. INTRODUCTION Turning is the removal of metal from the outer diameter of a rotating cylindrical work piece. Turning is used to reduce the diameter of the work piece, usually to a specified dimension, and to produce a smooth finish on the metal. Often the work piece will be turned so that adjacent sections have different diameters. Turning is the machining operation that produces cylindrical parts. In its basic form, it can be defined as the machining of an external surface: With the work piece rotating. With a single-point cutting tool, and With the cutting tool feeding parallel to the axis of the work piece and at a Distance that will remove the outer surface of the work. Turning is the removal of metal from the outer diameter of a rotating cylindrical work piece. Turning is used to reduce the diameter of the work piece, usually to a specified dimension, and to produce a smooth finish on the metal. Often the work piece will be turned so that adjacent sections have different diameters. II. METHODOLOGY Design of experiment In this study, three turning process parameters were adopted as control factors, and each parameter was designed to have three levels, denoted 1, 2, and 3. The experimental design was according to an L 9 array based on Taguchi method, while using the Taguchi orthogonal array would reduce the number of experiments. A set of experiments designed using the Taguchi method was conducted to investigate the relation between the process parameters and response factor. Minitab 17 software is used for optimization tool:- The tool is single point cutting tool made of carbide. It is grinded after each experiment and the same tool geometry is maintained by using the Bevel Protractor Combination Set. The tool used is of MIRANDA, S-400 as Work piece Material: - EN 31 steel, MS, Aluminum is used as the work piece material for carrying out the experimentation to optimize the surface roughness. Selection of Rake angle -Factors to consider: 450 Copyright Vandana Publications. All Rights Reserved.

2 Hardness of work material Type of cutting operation Material and shape of the cutting tool Strength of cutting edge parameters (a) speed - The speed of the work piece surface relative to the edge of the cutting tool during a cut. Measured in meter per minute. c) - The speed of the cutting tool's movement relative to the work piece as the tool makes a cut. The feed rate is measured in mm per revolution. d) Depth of cut - The depth of the tool along the radius of the work piece as it makes a cut, as in a turning or boring operation. A large depth of cut will require a low feed rate, or else it will result in a high load on the tool and reduce the tool life. Therefore, a feature is often machined in several steps as the tool moves over at the depth of cut. Procedure followed Tool used : Carbide Material used : En 31,MS,Aluminium Handysurf : for measuring surface roughness value. Parameters of the setting Control factor Symbol speed Factor A Factor B Depth of cut Factor C Table 1- Turning parameter of the setting III. PRIOR APPROACH Thamizhmanii et al. (2007) applied Taguchi method for finding out the optimal value of surface roughness under optimum cutting condition in turning SCM 440 alloy steel. The experiment was designed by using Taguchi method and experiments were conducted and results thereof were analyzed with the help of ANOVA (Analysis of Variance) method. The causes of poor surface finish as detected were machine tool vibrations, tool chattering whose effects were ignored for analyses. The authors concluded that the results obtained by this method would be useful to other researches for similar type of study on tool vibrations, cutting forces etc. The work concluded that depth of cut was the only significant factor which contributed to the surface roughness. Natarajan, C., et al. (2010)[2] designed an artificial neural network (ANN) to predict the surface roughness through back propagation network using Matlab 7 software. The cutting parameters evaluated were spindle speed, feed rate and depth of cut. The tests were performed in dry condition on C26000 metal in a CNC turning centre with a CNMG insert. A total of 36 specimens were experimented. The actual roughness values were matched with the predicted roughness values by using Matlab 7. The percentage of deviation between the roughness values was found to be 24.4%. The interactions between the parameters were also obtained through the model. It was found that the feed rate had huge effect on surface roughness then the other parameters Rodrigues, L.L.R., et al. (2012) [8] studied the effect of feed,speedand depth of cut on the surface roughness as well as cutting force in turning mild steel with HSS cutting tool. Experiments were carried out using high precision lathe machine. Full factorial design with two repetitions was used to find the optimal solution. Feed and the interaction between feed and speed were the main influencing factors in surface roughness whereas feed, depth of cut and the interaction between feed and depth of cut influenced the variance of cutting force significantly. They suggested that feed and depth of cut has significant effect on surface roughness and cutting force. Somashekara, H.M., and Swamy, N. L., et al. (2012) [10] obtained an optimal setting for turning Al6351-T6 alloy for optimal surface roughness. A model was generated for optimal surface roughness using regression technique. The turning parameters considered were speed, feed and depth of cut with three levels each. L9orthogonal array was implemented for the experiment. The roughness measure was done with three repetitions. The results found between regression model and experimental values were having error less than 2%. From ANOVA and S/N ratio, cutting speed was found to be highest significant parameter followed by feed and depth of cut. Quazi, T., and More, Pratik Gajanan(2014) [19]utilized Taguchi method to optimize the surface roughness in turning EN8, EM31 and mild steels. The three levels turning parameters considered were cutting speed and feed rate. The tool grades considered were TN60, TP0500 and TT8020. The experiments were carried on Supercut 5 turning machine. The roughness weremeasured by Wyko NT9100 Optical Profiling System. The Taguchi method was designed and analysed by Minitab statistical 16. L9orthogonal array was used for analysis of all the materials along with three cutting tools. It was 451 Copyright Vandana Publications. All Rights Reserved.

3 observed that feed rate has highest effect on surface roughness for all the three alloys. Satyanarayana.Kosaraju, VenuGopal. Anne and VenkateswaraRao.Ghanta Effect of Rake Angle and Feed Rate on Forces in an Orthogonal Turning Process The paper is about the effect of rake angle and feed rate on the cutting forces in an orthogonal turning process. A hollow cylindrical EN8 work piece was turned using HSS tools for 6 different rake angles (00, 40, 80, 120,160, 200) During the experimentation, the forces were measured Using a 4-component piezoelectric dynamometer. The experimental results show that the feed force (Fx) is greater than the tangential force (Fy) and the longitudinal force (Fz) is least in magnitude irrespective of the tool rake angle.[1] Α.Κ. Baldoukas, F.A. Soukatzidis, G.A. Demosthenous, A.E. Lontos experimental investigation of the effect of cutting depth,tool rake angle and workpiece material type on the main cutting force during a turning process. The paper is about experimentally the influence of cutting depth, tool rake angle and work piece material type on the main cutting force and chip morphology during a turning process. AISI 1020, Aluminum 2014 and UNS C23000 specimens were used as work piece materials.[2] Fata, B. Nikuei The Effect of the Tool Geometry and Conditions on the Tool Deflection and Forces The paper is about by measuring the cutting forces the effect of the tool shape and qualifications (sharp and worn cutting tools of both vee and knife edge profile) and cutting conditions (depth of cut and cutting speed) in the turning operation on the tool deflection and cutting force is investigated. The work piece material was mild steel and the cutting tool was made of high speed steel. forces were measured by a dynamometer. [4] Y. Zeng and J.W. suther land An orthogonal model based on finite deformation analysis and experimental verification The paper is about model based on finite deformation was developed to predict the forces in orthogonal turning operations. L. B. Abhang and M. Hameedullah, Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology The paper is about power consumption in turning EN-31 steel (a material that is most extensively used in automotive industry) with tungsten carbide tool under different cutting conditions was experimentally investigated Feng. Cang-Xue (Jack) [5] studied the impact of turning parameters on surface roughness. He studied the impact of Feed, Speed and Depth of Cut, Nose radius of tool and work material on the surface roughness of work material. He found that the feed have most significant impact on the observed surface roughness and also observed that there were strong interactions among different turning parameters. In 1950 (Gilbert, 1950) [2] presented a theoretical analysis of optimization of machining process and proposed an analytical procedure to determine the cutting speed for a single pass turning operation with fixed feed rate and depth of cut by using two different objectives maximum production rate and minimum machining cost. IV. OUR APPROACH Three input parameters selected for the experiment are cutting speed, and depth of cut The experiment is planned according to Taguchi s L9 orthogonal array. The three input parameters has three levels, hence L9 orthogonal array is selected for the experiment. The experiment work was carried out on Lathe Machine Turning, the main drive power is 7.5KW and the speed range was in the range rpm. (i) Work material was an Aluminium and Tool material is carbide. Table-2-Turning parameter of the setting Symbol Control Level Level Unit Level 1 Factor 2 3 V Speed m/min F Feed mm/rev D Depth Of Cut mm Copyright Vandana Publications. All Rights Reserved.

4 Level Speed(m/min ) (f) (mm/rev) Depth of cut (d) (mm Delta Rank Table 4- Response Table for Signal to Noise Ratios Level Speed(m/min ) (f) (mm/rev) Depth of cut (d) (mm Delta Rank Table 7- Response Table for Signal to Noise Ratios Mean of SN ratios Spped(m/min) 220 Depth Of Cut 264 Figure 1-Main effects plot for SN ratio (Ra) Signal-to-noise: Smaller is better Main Effects Plot for SN ratios Data Means 1.5 (ii) Work material was an MS and Tool material is carbide. Table-5- parameter of the setting Symbol Control Level Level Unit Level 1 Factor 2 3 V Speed m/min F Feed mm/rev D Depth Of Cut mm Feed Rate(mm/rev) able 7- Response Table for Signal to Noise Ratios Mean of SN ratios Depth Of Cut(mm) 225 Figure 2-Main effects plot for SN ratio (Ra) Signal-to-noise: Smaller is better Main Effects Plot for SN ratios Data Means Speed(m/min) 1.5 Figure 2-Main effects plot for SN ratio (Ra) (iii) Work material was an EN31 and Tool material is carbide. 0.1 Feed Rate(mm/rev) Table 6-The surface roughness plots for signal to noise ratios 453 Copyright Vandana Publications. All Rights Reserved.

5 Level Speed(m/min ) (f) (mm/rev) Depth of cut (d) (mm Delta Rank Table 10- Response Table for Signal to Noise Ratios (i)taguchi parameter design is an efficient and effective method for optimizing surface roughness in a turning operation.. (ii)the surface roughness Ra was measured using the input factors namely cutting speed, feed rate and depth of cut. The response, surface roughness was measured by varying the machining parameters and the corresponding the statistical analysis is done using MINITAB 17 software for obtaining the main effect, interaction effect and graphs. (iii)this was accomplished with a relatively small number of experimental runs, given the number of control and noise factors, suggesting that Taguchi parameter design is an efficient and effective method for optimizing surface roughness in a turning operation. REFERENCES Figure 3-Main effects plot for SN ratio (Ra) V. CONCLUSION The current study was done to study the effect of turning parameters on the surface roughness. The following conclusions are drawn from the study: [1] Satyanarayana.Kosaraju, VenuGopal. Anne and VenkateswaraRao.Ghanta Effect of Rake Angle and Feed Rate on Forces in an Orthogonal Turning Process International Conference on Trends in Mechanical and Industrial Engineering (ICTMIE'2011) Bangkok Dec., [2] Α.Κ. Baldoukas, F.A. Soukatzidis, G.A. Demosthenous, A.E. Lontos experimental investigation of the effect of cutting depth,tool rake angle and workpiece material type on the main cutting force during a turning process. Frederick University Cyprus (F.U.C.), Nicosia- Cyprus. [3] S.J. Ojolo and O.S. Ohunakin Study of Rake Face Action on Using Palm-Kernel Oil as Lubricant Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (1): [4] A. Fata, B. Nikuei The Effect of the Tool Geometry and Conditions on the Tool Deflection and Forces World Academy of Science, Engineering and Technology [5] Y. Zeng and J.W. suther land An orthogonal model based on finite deformation analysis and experimental verification Vol 10 ASME [6] L. B. Abhang and M. Hameedullah, Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology Journal of Engineering Science and Technology Review 3 (1) (2010) [7] Shane Y. Hong, Irel Markus, Woo-cheol Jeong New cooling approach and tool life improvement in cryogenic machining of titanium alloy Ti-6Al-4V.International Journal of Machine Tools & Manufacture 41 (2001) [8] Stoić, J. Kopač, T. Ergić, M. Duspar Turning conditions of Ck 45 steel with alternate hardness zones. published in revised form [9] M.A. Kamely, M.Y. Noordin, The Impact of Tool Materials on Force World Academy of Science, Engineering and Technology Copyright Vandana Publications. All Rights Reserved.

6 [10] Tugrul O zel, Taylan Altan Determination of workpiece flow stress and friction at the chip tool contact for high-speed cutting International Journal of Machine Tools & Manufacture 40 (2000) [11] H aouici,, M a yallese, Abelbah, Mfameur and M elbah Experimental investigation of cutting parameters influence on surface roughness and cutting forces in hard turning of X38CrMoV5-1 with CBN tool Indian Academy of Sciences. Sa dhana Vol. 38, Part 3, June 2013, pp [12] A. Pramanik, L.C. Zhang_, J.A. Arsecularatne Prediction of cutting forces in machining of metal matrix composites International Journal of Machine Tools & Manufacture 46 (2006) [13] S.S.K. Deepak Applications of Different Optimization Methods for Metal Operation A Review Research Journal of Engineering SciencesVol. 1(3), 52-58, Sept. (2012). [14] T. Tyan and we1 H. Yang analysis of orthogonal metal cutting processes international journal for numerical methods in engineering, vol. 34, (1992). [15] Yigit et al. (2011), Multi-Objective Optimization of the Forces in Turning Operations Using the Grey- Based Taguchi Method ISSN [16] Kamal, Anish and M.P.Garg (2012), Experimental investigation of Material removal rate in CNC turning using Taguchi method International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 2,Mar-Apr 2012, pp [17] Dhavamani and Alwarsamy, Optimization of Parameters of Composite Materials using Genetic Algorithm European Journal of Scientific Research ISSN X Vol.63 No.2 (2011), pp oct-2011 [18] Sijo M.T and Biju.N (2010), Taguchi Method for Optimization of Parameters in Turning Operations Proc. of. Int. Conf.on Advances in Mechanical Engineering [19] Rama and Padmanabhan. G (2012), Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp [20] Ballal, Inamdar and Patil P.V. (2012), Application Of Taguchi Method For Design Of Experiments In Turning Gray Cast Iron International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp [21] Ashish Yadav, Ajay Bangar, Rajan Sharma, Deepak Pal, Optimization of Turning Process Parameters for Their Effect on En 8 Material Work piece Hardness by Using Taguchi Parametric Optimization Method, International Journal of Mechanical and Industrial Engineering (IJMIE), ISSN No , Volume-1, Issue-3, [22] Puneet mangla, Nishant kr. Singh, Yashvir singh (2011) Study of effect of turning parameters on work piece hadness using Taguchi method. ICAM, pp [23] Rama Rao. S, Padmanabhan. G, Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites, International Journal of Engineering Research and Applications (IJERA) ISSN: Vol. 2, Issue 3, May-Jun 2012, pp Copyright Vandana Publications. All Rights Reserved.