Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 97 (2014) 338 – 345
12th GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014
Effect of Machining Parameters on Tool Wear in Hard Turning of AISI D3 Steel a*,c b
Varaprasad.Bha*, Srinivasa Rao.Chb, P.V. Vinayc
GVP College for Degree and P.G. courses (Technical Campus), Rushikonda, Visakhapatnam, India.
Mechanical Engineering Department, Andhra University, College of Engineering (A), Visakhapatnam, India.
Abstract Present day metal cutting industry has to meet the challenges of quality and productivity of the machined parts during turning economically. In the present work, an attempt has been made to develop a model and predict tool flank wear of hard turned AISI D3 hardened steel using Response Surface Methodology (RSM). The combined effects of cutting speed, feed rate and depth of cut are investigated using contour plots and surface plots. RSM based Central Composite Design (CCD) is applied as an experimental design. Al2O3/TiC mixed ceramic tool with corner radius 0.8 mm is employed to accomplish 20 tests with six centre points. The adequacy of the developed models is checked using Analysis of Variance (ANOVA). Main and interaction plots are drawn to study the effect of process parameters on output responses. © by Elsevier Ltd. This an open access ©2014 2014Published The Authors. Published byisElsevier Ltd. article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Organizing Committee of GCMM 2014. Selection and peer-review under responsibility of the Organizing Committee of GCMM 2014
Keywords: Hard turning; Tool Flank Wear; AISI D3 ; RSM.
* Corresponding author. Tel.: +91-9894666288; E-mail address:
[email protected]
1877-7058 © 2014 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Organizing Committee of GCMM 2014
doi:10.1016/j.proeng.2014.12.257
339
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
1. Introduction Hard turning is the process of machining hardened ferrous material with a hardness value more than 45HRC in order to obtain finished workpieces directly from hardened parts. The growth of hard turning process is indebted to the advent of new advanced tools such as Cubic Boron Nitride (CBN), Polycrystalline Cubic Boron Nitride (PCBN), Chemical vapor deposition (CVD), Physical Vapor Deposition (PVD) and Ceramic tools since 1970. Reduction in machining costs, elimination of cutting fluids, increase in the flexibility and efficiency, parthandling costs and finally decrease in the set-up times when compared to grinding process [1-3].The great advantage of hard turning is its dry environment, it is mostly carried out in the absence of lubricants. The amount of heat generated depends on input parameters especially cutting speed which is most influencing factor and the type of material being machined [4]. Heating action caused by machining leads many of the economic and technical problems. In actual practice, there are many factors which change these performance measures, i.e. tool variables, workpiece variables and cutting conditions. Excessive temperatures directly influence the temperatures of tool face and tool flank and this leads to thermal damage of the machined surface [5]. Earlier researchers published experimental based works to study the effect of cutting parameters on surface roughness [6, 7]. Jarah A.G. et al. [8] investigated under dry machining conditions on machinability of FCD 500 ductile cast iron using coated carbide tool. Lalwani D.I. et al. [9] discussed effect of input parameters experimentally on cutting forces and surface roughness of MDN250 steel. Kaladhar et al. [10] presented optimized Material Removal Rate (MRR) by using CVD coated cutting insert while machining AISI 304 austenitic stainless steel. To improve cutting efficiency one has to select the most appropriate machining settings. Design of Experiments (DOE) and statistical or mathematical models are used quite extensively to select the optimum input parameters from experimented data. Statistical design of experiments refers to the process of planning the experiments so that the appropriate data can be analyzed by statistical methods resulting in valid and objective conclusions [11]. Davim J.P. and Figueira L. [12] conducted experiments on AISI D2 cold working steel with ceramic tool and investigated the machinability evaluation in hard turning using statistical methods. Cutting velocity was influences tool wear to a higher extent and cutting time by a smaller extent. The feed rate strongly influences the specific cutting pressure. In the present study, an attempt has been made to investigate the effect of process parameters (cutting speed, feed rate and depth of cut) on the tool wear in finish hard turning of AISI D3 steel hardened at 62HRC with Ceramic tool. The combined effects of the process parameters on performance characteristic are investigated while employing the ANOVA. The relationship between process parameters and performance characteristic through the RSM are modelled. 2. Experimentation The work piece material used for experiments is AISI D3 steel. A bar of diameter 68 mm x 360 mm long is prepared. Test sample is trued, centred and cleaned by removing a 2 mm depth of cut from the outside surface, prior to actual machining tests. The chemical composition of the work piece material is given in Table.1.The workpiece is oil-quenched from 9800C (18000F), hardened followed by tempering at 2000C to attain 62HRc. Table.1 Chemical composition of AISI D3 (wt%) C Si Mn P S Cr 2.06
0.55
0.449
0.036
0.056
11.09
Ni
Mo
Al
Cu
Zn
Fe
0.277
0.207
0.0034
0.13
0.27
84.8716
The lathe used for machining operations is Kirloskar make model Turn Master-35, spindle power 6.6KW. Tool maker’s micro scope is used for measuring tool flank wear. The cutting insert used is a mixed ceramic removable, of square form with eight cutting edges and having designation SNGA 120408 T01020 Sandvik make CC6050 is a mixed ceramic grade based on alumina with an addition of titanium carbide. The inserts are mounted on a commercial tool holder of designation PSBNR 2525 M
340
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
12 (ISO) with the geometry of active part characterized by the following angles: χ = 75°; α = 6°; γ = −6°; λ = −6°. Three levels are defined for each cutting variable as given in Table.2. The variable levels are chosen within the intervals as recommended by the cutting tool manufacturer. Three cutting variables at three levels led to a total of 20 tests. Table.2. Process parameters and their levels Parameters Levels -1 0 +1 Speed(m/min) 145 155 165 Feed(mm/rev) 0.05 0.075 0.1 Depth of cut(mm) 0.3 0.6 0.9 2.1 Measurement of tool flank wear During the course of experimentation the tool flank wear of worn out inserts are measured with the help of a Tool maker’s microscope. 3. ANALYSIS OF RESULTS Table.3 presented experimental results of Tool flank wear (V b) for various combinations of cutting conditions (cutting speed, feed rate and depth of cut) as per the design matrix. Table.3.Experimental results for Tool wear Un Coded Form
Tool Flank wear
Experiment. No. Speed (m/min)
Feed (mm/rev)
Depth of Cut (mm)
Vb(mm)
1
145
0.05
0.3
0.1740
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
165 145 165 145 165 145 165 145 165 155 155 155 155 155 155 155 155 155 155
0.05 0.1 0.1 0.05 0.05. 0.1 0.1 0.075 0.075 0.05 0.1 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075
0.3 0.3 0.3 0.9 0.9 0.9 0.9 0.6 0.6 0.6 0.6 0.3 0.9 0.6 0.6 0.6 0.6 0.6 0.6
0.1480 0.1560 0.1630 0.1730 0.1840 0.1720 0.2230 0.1750 0.1775 0.1680 0.1700 0.1630 0.1860 0.1780 0.1710 0.1780 0.1700 0.1690 0.1720
The raw data presented in table.3 for which analysis has to be graphically represented. The following sections present the detailed discussion.
341
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
3.1 Statistical Analysis In RSM, the quantitative form of the relationship between the desired response and independent input process parameters can be represented by [13] (1) Y = f (Vc, f, d) where Y is the desired response and f is the response function. In the present investigation, the RSM-based mathematical models for tool wear has been developed with cutting speed ‘Vc’, feed rate ‘f’ and depth of cut ‘d’ as the process parameters. The response surface equation for three factors is given by [13]. (2) Y = a0 + a1Vc + a2f + a3d + a12Vcf + a13Vcd + a23fd+ a11v2 + a22f 2 + a33 d2 where Y is desired response and ao,a1,_ _ _ _ _ a33 regression coefficients to be determined for each response. The regression coefficients of linear, quadratic, and interaction terms of RSM-based mathematical models are determined by [13]. Table.4 shows estimated regression co-efficient for tool wear (Vb) after removing the insignificant terms. Table.5 presented the ANOVA test which is performed to evaluate the statistical significance of the fitted regression model and factors involved therein for the response factor tool flank wear (Vb) ANOVA table is used to summarize the test for significance of regression model, test for significance for individual model coefficient. Table.4 Estimated Regression Coefficients for Tool wear Vb (mm) after removing the insignificant terms. Term Constant
Coef 0.172945
SE Coef 0.001289
T 134.136
P 0.000
Speed (m/min) Feed (mm/rev) Depth of cut (mm) Speed (m/min) x Feed (mm/rev)
0.004550 0.003700 0.013400 0.009125
0.001186 0.001186 0.001186 0.001326
3.836 3.120 11.298 6.882
0.003 0.011 0.000 0.000
Speed (m/min) x Depth of cut (mm) 0.010125 Feed (mm/rev) x Depth of cut (mm) 0.005125 S = 0.003750 R-Sq = 96.5% R-Sq (adj) = 93.4%
0.001326 0.001326
7.636 3.865
0.000 0.003
Table.5 Analysis of Variance for Tool wears Vb (mm) Source
DF
Seq SS
Adj SS
Adj MS
F
P
Regression Linear Square
9 3 3
0.003898 0.002140 0.000062
0.003898 0.002140 0.000062
0.000433 0.000713 0.000021
30.79 50.70 1.47
0.000 0.000 0.280
Interaction Residual Error Lack-of-Fit Pure Error Total
3 10 5 5 19
0.001696 0.000141 0.000061 0.000080 0.004039
0.001696 0.000141 0.000061 0.000080
0.000565 0.000014 0.000012 0.000016
40.20
0.000
0.76
0.616
x
Vb= 0.172945 + 0.004550 x Vc + 0.003700 x f + 0.013400 x d + 0.009125 Vc x f+0.010125Vc x d+ 0.005125 f x d (3)
342
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
3.2 Main Effect Plots and Interaction plots for Tool Flank Wear . Main Effects Plot (data means) for Tool Flank wearVb(mm) Speed(m/min)
Mean of Tool Flank wearVb(mm)
0.19
Feed(mm/rev)
0.18 0.17 0.16 -1
0
1
-1
0
1
Depth of cut(mm)
0.19 0.18 0.17 0.16 -1
0
1
Figure 4.shows main effects for tool flank wear are plotted. Interaction Plot (data means) for Tool Flank wearVb(mm) -1
0
1
Speed(m/min) -1 0 1
0.200
0.175
Speed( m/min)
0.150 0.200
0.175
Feed( mm/r ev)
Feed(mm/rev ) -1 0 1
0.150 0.200
0.175
Depth of cut( mm)
Depth of cut(mm) -1 0 1
0.150 -1
0
1
-1
0
1
Fig5.Interaction plot for mean Tool flank wear. It is clearly observed that the depth of cut strongly changes flank wear. Speed and Feed rate has also an increasing effect. For the depth of cut, influence value is that highest and it has much higher levels of contribution. However, low depth of cut should be used in order to reduce the tendency to chatter. Therefore, if the tool work
343
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
system is not very rigid, such as in cutting slender parts, very fine depth of cut should be employed to avoid chatter. Figure 5.shows interaction of tool flank wear and parameters. From the above figure flank wear is decreasing trend at low speed and feed and low speed and depth of cut. At high levels of speed and feed as well as speed and depth of cut flank wear is high. The combined effect of feed and depth of cut is also following the same trend as above. The combined effect of depth of cut on flank wear is more even small changes in speed or feed. 3.3 Contour Plots for tool flank wear Vs Speed, Feed and Doc . Contour plots play a very important role in the study of the response surface. By creating contour plots using Minitab software for response surface analysis, the optimum is located by characterising the shape of the surface. Circular shaped contour represents the independence of factor effects and elliptical contours may indicate factor interaction. The contours of the responses are shown in figure 6a, 6b and 6c for flank wear is low at low levels of speed and feed, low at depth of cut and feed. But, it is very sensitive against depth of cut and speed, flank wear is drastically increases at even at low values. 1.0
1.0 0.170
0.165
0.185
0.180
0.190
0.175
0.185
0.5
Depth of cut(mm)
Feed(mm/rev)
0.5
0.0
0.175
-0.5
-1.0 -1.0
0.0 0.165
0.170
-0.5
0.170
0.160
0.0 Speed(m/min)
0.5
0.160
-1.0 -1.0
0.165
-0.5
0.180
1.0
Fig.6a contour plot for flank wear Vs Depth of cut and Feed
-0.5
0.0 Feed(mm/rev)
0.5
1.0
Fig.6b contour plot for flank wear Vs Feed and Speed 1.0 0.19
0.20
Depth of cut(mm)
0.5
0.18
0.0 0.17
-0.5
-1.0 -1.0
0.16
-0.5
0.0 Speed(m/min)
0.5
1.0
Fig.6c contour plot for flank wear Vs Depth of cut and Speed. 3.4 3 D Surface plots 3D Surface plots of Tool flank wear vs. different combinations of cutting parameters are shown below. These figures are obtained using RSM figure 7a presents the influence of depth of cut and feed rate on the tool flank wear, while the speed is kept at the middle level. Figure.7b shows the estimated response surface in relation to the
344
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345
0.19
Flank Wear Vb (mm)
Flank Wear Vb (mm)
depth of cut and cutting speed while feed is kept at the middle level.Figure.7c shows surface plot of speed and feed while the depth of cut is kept at the middle level. For each plot, the variables not represented are held at a constant value (the middle level).These 3D plots confirm the nodes observed during the principal effects plots analysis.
0.18 m)
0.20
m) 0.18
0.17 0.16
1 0
-1 -
0 Feed(mm/r ev)
1
Depth of cut(mm) De
1
0.16
0
-1 -
-1
0 Speed(m/min)
Flank Wear Vb (mm)
Fig.7a Surface plot for Flank wear Vs Depth of cut and Feed
1
-1
Depth of cut(mm) De
Fig.7b Surface plot for Flank wear Vs Depth of cut and Speed
0.19 0.18 m) 0.17 1 0.16
0
--1
0 S d( / i ) Speed(m/min)
1
-1
Feeed(mm/rev)
Fig.7c Surface plot for Flank wear Vs Feed and Speed. 4. Conclusions In this work, tool wear analyzed to study the effects of cutting speed, feed rate and depth of cut in hard turning of AISI D3 cold work tool steel using CC6050 ceramic inserts. The conclusions are as follows. 1. The RSM based DOE is found to be an effective way in determining the optimal cutting parameters to be speed of 165m/min, feed rate of 0.05mm/rev and depth of cut of 0.3mm to achieve a low tool wear of 0.148mm. 2. The significant parameter for tool flank wear is Depth of cut. The speed and feed have little influence on the total variation. 3. The relationship between performance characteristic and cutting parameters is expressed by a multiple regression equation that can be used to estimate the expressed values of the performance level for any parameter levels. 5. References [1]Özel, T., and Karpat, Y.,2005, “Predictive Modeling of Surfsce Roughness and Tool Wear in Hard Turning Using Regression and Neural Networks”, International Journal of Machine Tools and Manufacture, Vol.45, pp.467-479. [2]Mohammadi,A. and Zarepour,H., 2008, “Statistical Analysis Of Hard turning Of AISI 4340 Steel on surface finish And Cutting Region Temperature” ,M0048010,Bahrain ,Manama, AMPT 2008. [3]Kountanya, R.K., 2008, “Optimizing PCBN cutting tool performance in hard turning” ,Proceeding of the Institution of Mechanical Engineers, part B:Journal of Engineering manufacture, Vol. 222, pp. 969-980. [4]Hamdan A., Sarhan Ahmed A.D., and Hamdi M.A.,2012, “Optimization method of the machining parameters in high speed machining of stainless steel using coated carbide tool for best surface finish”, International Journal Adv Manufacturing Technology, Vol.58,pp.81-91.
Varaprasad. Bh et al. / Procedia Engineering 97 (2014) 338 – 345 [5]Shaw, M.C., Metal cutting principles, Oxford University Press, 2005; New York. [6]Khidhir, B.A., and Mohamed B., 2011, “Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel based hastelloy-276”. IOP Conf. Series: Materials Science and Engineering, Vol.17 pp.1-10. [7] Singh H., Khanna R., and Garg M.P.,2011, “Effect of Cutting Parameters on MRR and Surface Roughness in Turning EN-8”, International Journal of Current Engineering and Technology, Vol. 1(1),pp.100-104. [8]Jaharah A.G., Rodzi Mohd Nor A.M., Rahman A.A., Rahman Mohd Nizam A., and Hassan C.H.C., 2009, “Machinability of FCD 500 ductile cast iron using coated carbide tool in dry machining condition”, International Journal of Mechanical and Materials Engineering, Vol.4(3),pp. 279284. [9]Lalwani D.I., Mehta N.K., and Jain P.K.,2008, “Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel”, Journal of Materials Processing Technology, Vol.206,pp.167-179. [10]Kaladhar M., Subbaiah K.V., and Rao Ch. S., 2012, “Parametric optimization during machining of AISI 304 Austenitic Stainless Steel using CVD coated DURATOMICTM cutting insert”, Int. J. of Industrial Engineering Computations, Vol.3,PP. 577–586.155 [11]Montgomery D.C. Design and analysis of experiments.John Wiley & Sons, 1997; New york [12]Davim J.P., and Figueira L., 2007, “Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques”, Journal of Materials and Design, Vol.28,pp.1186–1191. [13]Kondapalli Siva Prasad, Srinivasa Rao. Ch., Nageswara Rao D.,2012, “Application of design of experiments to plasma arc welding process: a review”, J. of the Braz. Soc. of Mech. Sci. & Eng., Vol.XXXIV,pp.75–81.
345
