Effect of Cutting Parameter for Turning En-31 Material using RSM

Transcription

1 Effect of Cutting Parameter for Turning En-31 Material using RSM Mahesh k. sharma 1, Sanjay Singh 2, Rakesh kumar 3 1 M.Tech Research Scholar, Mechanical Engineering Department, Jagannath University, Rajasthan, India 2,3 Assistant Professor, Mechanical Engineering Department, Jagannath University, Rajasthan, India Abstract - This paper presents the effect of process parameter in turning operation to predict surface roughness and material removal rate (MRR). The turning process by using bank a 34 lathe machine. The parameters that affect the turning operation are vibration, tool wear, surface roughness, material removal rate etc. Among this surface roughness and material removal rate are an important factor that affects the quality in manufacturing process. The main objective of this paper is to predict the surface roughness and material removal rate on EN -31 alloy steel by using coated carbide tool. A mathematical model is developed using regression technique and optimization is carried out using Box- Behnken of response surface methodology. Therefore, this study attempts the application of response surface methodology to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness and maximum MRR. Keywords - Turning process, Surface roughness, MRR, Response Surface method, DOE, ANOVA I. INTRODUCTION Turning is a machining process for generating external surface of revolution. It is the removal of unwanted material for the outer diameter of a rotating cylindrical work-piece, and used to reduced the diameter of the work-piece, usually to specified dimension. In turning surface roughness and material removal rate are important task in determine how a real entity will intermingle with the environment. Decreasing the roughness of the the work piece usually exponentially increase its manufacturing cost and develop of quality of product. Surface roughness are influences the wear rate, fatigue strength,coefficient of friction and corrosion resistance of the machined components. In actual practice surface roughness are affect in many factor such as cutting tool variable, cutting conditions of work-piece and work-piece variables. Tool variable includes tool material, nose radius, rake angle, nose radius etc. Cutting condition are affect speed, feed and depth of cut. Work-piece variables are affect which type of material is using, mechanical & chemical properties of material. Carbide cutting tools are very popular in metal cutting industry for the cutting of various hard materials such as, alloy steels, die steels, high speed steels, bearing steels, white cast iron and graphite cast iron. In the past few decades there had been great advancement in the development of these cutting tools. To improve the surface condition,tool wear and hardness of cutting tool are coated by different material such as Titanium carbon nitride, Titanium nitride, Titanium aluminum nitride and Titanium carbide by the process of chemical vapour deposition. Coating is also used on cutting tools to provide improved lubrication at the tool/chip and tool/work piece interfaces and to reduce friction, and to reduce the temperatures at the cutting edge. II. EXPERIMENTION DETAILS A. Work piece Material The work material selected for the study is EN-31 Alloy steel. This steel is basically known bearing steel and used in bearing production such as ball & roller bearing. Application of EN-31 material with its properties are used to make camshafts, Punches and dies, spinning tool, axels, gears, driving pinion and link components for transportation and energy products as well as many applications in general mechanical engineering. The chemical composition, mechanical properties and heat treatment properties of the EN-31 materials are shown in table 1, table 2 and table 3 Table 1: Chemical Composition Elemen Compositio t n (%) C % Si % Mn % S % P % Ni % Cr % Table 2: Mechanical Properties Property Tensile strength Yield stress Modulus of elasticity Value 750 N/mm N/mm N/mm 2 Density 7.8 kg/m 3 Hardness HRC ISSN: Page 166

2 Table 3: Heat Treatment of EN-31 Element Hardening Temperature Quenching Medium Tempering Temperature Brinell-Rockwell Hardness Objective C Oil C B. Cutting Insert The tool insert for experimentation was a coated carbide tool whose specifications are shown table 4. Coated carbide tools better performance than uncoated carbide cutting tool. Carbide is a important cutting tool for machining of alloy steel and casting. Catalog Number ISO SNMG Table 4: Specification of Cutting Tool Catalog Number ANSI SNMG 432MS Grade KCU 25 Dimensions D S mm in mm in Fig 2: Banka 34 lathe machine D. Roughness Measurement Surface roughness has been precisely measured with the help of a portable stylus-type profile-meter, Talysurf (Taylor Hobson, Surtronic 3+, UK). Measurements were taken at different locations of job and the average was reported for each run. The chosen insert was a square type negative insert meaning that it was rotatable and reversible so that a total number of 8 cutting edges can be generated. The coating on the insert is TiAlN (Titanium Aluminium Nitride). Fig 3:Taylor hobson for measurement of Surface Roughness Fig 1: Selected cutting tool insert C. Experiment Machine The turning of work piece in dry turning conditions were conducted on lathe BANKA 34 having following specifications: Power required : 2 HP, Spindle speed: rpm, Spindle nose loo type, spindle bore 38 mm, Swing over cross slide 190 mm, Swing over bed 350 mm, main moter 1.5kw,weight kg. E. Process variables and their level The working ranges of parameters for subsequent design of experiment based on Response Surface Methodology have been selected. In the present experimental work, spindle speed, feed rate and depth of cut have been considered as main process variables. The process variables with their units are listed in Table 5 Table 5: Factors and their levels Factor Level-1 Level-2 Level-3 Cutting Speed (m/min) Feed (mm/rev) Depth of cut (mm) ISSN: Page 167

3 III. RESULTS AND DISCUSSION Table 6: Results of main experiments for MRR & average surface roughness value Ra Factor I Factor II Factor III Response I Response II Std B:Feed C:Depth of Ra(µm) MRR(mm 3 /sec) Run A:Speed cut ANOVA For Ra ANOVA for response Ra is given in Table 7 Source Total sum of DF Mean Total F p-value Remarks squares square Value prob>f Model significant A-speed < B-feed C-doc AB AC BC A < B E E C Residual Lack of Fit not significant Pure Error Cor Total Std. Dev R-Squared Mean 1.56 Adj R- Squared C.V. % 9.98 Pred R- Squared PRESS 1.93 Adeq Precision ANOVA For MRR ANOVA for response MRR is given in Table 8 ISSN: Page 168

4 Table 8: ANOVA For MRR Source Total sum of DF Mean Total F p-value Remarks squares square Value prob>f Model significant A-speed < B-feed C-doc AB AC BC Residual Lack of Fit not significant Pure Error Cor Total Std. Dev R-Squared Mean Adj R- Squared C.V. % Pred R- Squared PRESS Adeq Precision Regression models:- The regression equations for the response characteristics as a function of input process parameters are given below in both coaded and actual factor. The insignificant coeffiecients (investigated from ANOVA) are omitted from the total equations. The developed statistical model for surface roughness and material removal rate are Roughness == *A+0.15*B+0.21*C *AB-0.050*AC-0.27*BC+0.70*A *B *C 2 Roughness= *speed * feed * doc * speed* feed E-003* speed * doc * feed * doc e-004*speed * feed * doc 2 MRR= *19.28*B+53.27*C *AB+31.88*AC+4.68*BC MRR= *speed *feed *doc *speed*feed * speed*doc *feed*doc Discussions of Results for Main Experiment Single Factor Effect on R a : Fig. 4 show the effect of three process inputs i.e., speed, feed and depth of cut of turning on average surface roughness value. Effect of speed: From the main effect plots based on the fig. 4(a) it has been observed that whenever spindle speed is increased from 90 RPM to 152RPM the value of Ra is decrease and when again speed increased from 152RPM to 202 RPM, the value of Ra is increased. Effect of feed: From the main effect plots based on the fig. 4(b) it has been observed that when feed rate is increased from 0.06mm/min to 0.16 mm/min, the increased of Ra. So result shows that feed gives the main effect on Ra. Effect of depth of cut: From the main effect plots based on the fig. 4(c), it has been observed that whenever depth of cut is increased from 0.1mm to 0.3mm the value of Ra is very slightly increase. Single Factor Effect on MRR: Fig. 5 show the effect of three process inputs i.e., speed, feed and depth of cut of turning on material removal rate. Effect of speed: From the main effect plots based on the fig. 5(a), it has been observed that whenever spindle speed is increased from 90 RPM to 202 RPM the value of MRR is increased. Effect of feed: From the main effect plots based on the fig. 5(b), it has been observed that when feed rate is increased from 0.06 mm/min to 0.16 mm/min, the increased of MRR. Effect of depth of cut: From the main effect plots based on the fig. 5(c), it has been observed that whenever depth of cut is increased from 0.1mm to 0.3mm the value of MRR is very slightly increase. ISSN: Page 169

5 Figure 5: Effect of (A) Speed (B) Feed (c) depth of cut on MRR Figure 4: Effect of (A) Speed (B) Feed (c) depth of cut on Ra Figure 6: a-b-c shows the contour plot, 3D response surface and Interaction Graph for the response Ra in terms of speed and depth of cut at a feed of 0.11mm.Contour plot plays a very important role in the study of response surface method with generating contour plot using Design of expert software for the response surface analysis, it is simple to characterize the shape of surface and locate the optimum with reasonable precision. By the examination of the contour plot and response surface, it is observed that SR decrease from 2.2 to 1.6 with increase in speed from 90 RPM to 202 RPM with increase of depth of cut from 0.1mm to 0.3 mm at a feed 0.11mm. Figure 6: d-e-f displays the normal probability plot of residuals, predicted versus actual plots and residual Vs run for Ra. It is observed that the residuals generally fall on the straight line implying that errors are normal distributed. The outlier points are then verified by checking for any points lying outside the red lines. It is evident from the fig. 6(f), all points lie inside the red lines, which indicates that the model fit well Figure 7: a-b-c shows the contour plot, 3D response surface and Interaction Graph for the response MRR in terms of speed and feed at a doc = 0.2 mm. Contour plot plays a very important role in the study of response MRR with generating contour plot by Design of expert software for the response surface method. By the examination of the contour plot and response surface, it is observed that MRR increases from 200 mm 3 /sec to 250 mm 3 /sec with increase in speed from 146 RPM to 202 RPM with increase of depth of cut from 0.1mm to 0.3 mm at a feed 0.11mm. Figure 7: d-e-f displays the normal probability plot of residuals,predicted versus actual and residual Vs run plots for MRR. It is observed that the residuals generally fall on the straight line implying that errors are normal distributed. The outlier points are then verified by checking for any points lying outside the red lines. It is evident from the fig. 7(f), all points lie ISSN: Page 170

6 inside the red lines, which ensures that the model fit well. (a) (f) Figure 6:Ra for (a)contour plot(b) response surface(c)interaction plot at feed of 0.11(d) Normal probability plot of residuals(e)actual Vs predicted values(f) residual Vs run (b) (a) (c) (b) (d) (c) (e) (d) ISSN: Page 171

7 (e) (f) Figure 7: MRR for (a)contour plot(b) response surface(c)interaction plot at feed of 0.11(d) Normal probability plot of residuals(e)actual Vs predicted values(f)residual Vs run Table 9: Solutions for optimum of process input for confirmation experiment Exp no. Spee d Feed Doc Ra MRR Desirabili ty Figure 9:Contour plot for results of Ra and MRR(at speed=146 rpm, feed rate=0.16 mm/rev,depth of cut=0.3mm) Once the optimal level of the process inputs is selected, the final step is to predict and verifying the improvement of the performance characteristics using the optimal level of the machining parameters. Experiments performed to machine and verify the Turning at the above optimal input parametric setting for MRR and surface roughness were compared with optimal response values. The observed MRR and surface roughness of the experimental results are mm 3 /sec and µm respectively A:speed = C:doc = B:feed = roughness = Desirability = Table 10 shows the error percentage for experimental validation of the developed models for the responses with optimal parametric setting during Turning of EN-31. From the analysis of Table10, it can be observed that the calculated error is small. The error between experimental and predicted values for surface roughness and MRR lies within 0.70% and 4.18% respectively. Obviously, this confirms the excellent reproducibility for the experimental conclusions MRR = Figure 8: Multi response optimization results for maximum MRR and minimum Ra with ramp diagrams. Table 10: main Experimental validation of developed models with optimal parameter settings. Responses Predicted Experimental Error Surface roughness % MRR % ISSN: Page 172

8 IV. CONCLUSION In this study, the surface roughness and MRR in the surface finishing process of EN-31 alloy steel were modeled and analyzed through RSM. Spindle speed, feed and depth of cut have been employed to carry out the experimental study. Summarizing the main features, the following conclusion can be drawn. [1]Analysied with ANOVA the experimental results showed that the speed (the most significant factor) contributed %, where as the depth of cut and feed rate contribution were 7.12 % and 3.56 % for Ra. [2]The experimental results with ANOVA analysis showed that the speed (the most significant factor) contributed 47 %, where as the depth of cut and feed rate contribution was 25 % and 3.26 % for MRR. [3]The predicted values of R 2 are for surface roughness and for MRR are reasonably well. Its value greater than 60% and closest to one is the best value for fit the model. [4]The error between experimental and predicted values at the optimal combination of parameter setting for Ra and MRR lie with in 0.70 % and 4.18 % respectively. Obviosly,this confirms excellent reproducibility of the experimental conclusions. [5]From the multi response optimization, we obtain the optimal combination of parameters settings are speed of 146 rpm, feed rate 0.16 mm/rev. and depth of cut 0.3 mm for achieving the required minimum surface roughness and maximum MRR. [10] Frauko puh,toni segota,zaran jurkovic, Optimization of hard turning process parameter with pcbn tool based on response surface methodology,2012 [11] A.D.Bagawade,P.G.Ramdasi,R.S.Pawade,P.k.Bramhan kar, The effects of cutting condition on Chip area ratio and surface roughness in turning of AISI steel,2012 [12] Basha N.Z., Vivek S. Optimization of CNC Turning Process Parameters on Aluminium 6061 Using response surface methodology, Engineering Science and Technology An International Journal (ESTIJ),2013 [13] Dr. C. J. Rao, Dr. D. Nageswara Rao, P. Srihari, Influence of cutting parameters On cutting force and surface finish in turning operation,2013 [14] Vikas B. Magdum and Vinayak R. Naik, Evaluation and Optimization of machining parameter for turning of EN-8 steel, International Journal of Engineering Trends and Technology, volume 4,pp ,may 2013 [15] Ranganath M S and Vipin, Effect of Machining Parameters on Surface Roughness with Turning Process- Literature Review, International Journal of Advance Research and Innovation, 2014 [16] Ranganath. M. S., Vipin, Nand Kumar, Rakesh Kumar, Experimental Analysis of Surface Roughness in CNC Turning of Aluminium Using Response Surface Methodology,2015 [17] Dharindon Sonowal, Thuleswar Nath and Dhrupad A review on optimization of cutting parameters on turning International Journal of Engineering Trends and Technology, volume 28,pp 54-60,oct 15 REFERENCES [1] M.Y.Noordin, Venkatesh VC, Sharif S, Elting S, Abdullah, Application of response Surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. Journal of Materials Processing Technology, [2] Dilbag singh and P.venkateswara rao, A surface roughness prediction modal for hard Turning process,2005 [3] Kandananond.K, The Determination Of Empirical Model For Surface Roughness In Turning Process Using Design Of Experiment, vol., 8,issue. 10, 2009, [4] Hwang. Y. K and Lee. C. M, Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments, Journal of Mechanical Science and Technology, vol. 24,issue.8,2010,pp. [5] Sahoo P., Optimization Of Turning Parameters For Surface Roughness Using RSM and GA, Advances in Production Engineering & Management vol.6,2011,pp [6] Alexandra Stanimir. A, Regressions Modeling Of Surface Roughness In Finish Turning Of Hardened 205cr115 Steel Using Factorial Design Methodology, Fiability& Durability No 1(7),2011 [7] Aruna. M and Dhanalaksmi. V, Design Optimization of Cutting Parameters when Turning Inconel 718 with Cermet Inserts, World Academy of Science, Engineering and Technology,vol. 61, [8] Wasif M. G., Safiulla. M, Evaluation Of Optimal Machining Parameters Of Nicrofer C263Alloy Using Response Surface Methodology While Turning On Cnc Lathe Machine, International Journal of Mechanical and Industrial Engineering (IJMIE), Vol-2,Iss-4,2012 [9] Dr.G.Harinath Gowd,M.Gunasekhar ready and Bathinpua sreenivasnlu, Empirical Modeling of Hard Turning Process Using response surface methodology,2012. ISSN: Page 173