Arab J Sci Eng (2015) 40:1151–1164 DOI 10.1007/s13369-015-1578-0
RESEARCH ARTICLE - MECHANICAL ENGINEERING
Effect of Tool Material Properties and Cutting Conditions on Machinability of AISI D6 Steel During Hard Turning Manoj Nayak · Rakesh Sehgal
Received: 16 August 2014 / Accepted: 19 January 2015 / Published online: 6 February 2015 © King Fahd University of Petroleum and Minerals 2015
Abstract Hard turning offers numerous advantages to grinding operation; however, there is a critical need for research to clarify issues related to high cutting forces, high temperatures, and surface roughness to meet the challenges it can offer as an alternate to grinding process. Mathematical models are generated for each response variable (main cutting force, thrust force, cutting temperature, and surface roughness) in terms of actual values of the factors (cutting speed, feed, and tool material) to establish relationships using design expert software for statistical investigation. A 33 full-factorial design with a total of 27 experiments was obtained for parametric analysis and investigation of machinability of AISI D6 tool steel using three different grades of low-content cubic boron nitride (CBN-L) inserts. The parametric analysis study shows that the main cutting force, thrust force, and surface roughness increase with feed. The thrust force and cutting temperature get influenced by tool material properties. It was established that grain size, CBN content, edge geometry, and hardness of the tools affected all the output characteristics. Scanned electron microscopy and energydispersive X-ray of the worn tools showed crater wear, chipping, and fracture of cutting edges, while abrasion and diffusion/dissolutions in CBN tools were the wear mechanisms affirmed in this study. M. Nayak National Institute of Technology, Hamirpur 177005, India M. Nayak (B) Department of Mechanical Engineering, FET, MRIU, Faridabad 121006, India e-mail:
[email protected] R. Sehgal Department of Mechanical Engineering, National Institute of Technology, Hamirpur 177005, India e-mail:
[email protected]
Keywords Hard turning · Cubic boron nitride · Thrust force · Surface roughness
1 Introduction Cubic boron nitride (CBN) has the highest hardness and thermal conductivity among all materials except diamond, popularly noted for its ability to perform efficient machining and offers low reactivity to ferrous material. In response to the increasing concern for the reduction in grinding sludge, there is paradigm shift from grinding to hard turning. But several issues need to be addressed such as typical failures occurring at the cutting edges during machining of hard materials. Hard materials are considered as difficult-to-machine materials mainly due to high chip–tool interface temperatures and cutting forces generated during the cutting process. Performance of several cutting tools like ceramics and carbides has been investigated for machinability characteristics of hard materials in previous studies [1–3]. Hard turning as a finishing process requires the generation of the machined surface by pure plastic deformation. Therefore, not only the stress, strain, and temperature distribution in the cutting zone are of interest, but the study of the influence of tool material properties on the cutting force and surface roughness is also significant. With the help of developed tool materials like ceramics and CBN, hard turning has become a well-established and profitable alternative to finish grinding operation [4]. To fill this demand, various CBN grades are being developed as they provide a reliable tool life. Hard turning process differs from conventional turning due to relatively low depth of cut, low feeds [5]. Moreover, due to high brittleness of CBN tools along with high strength and high hardness of the work material, these inserts are normally provided with a defined edge geometry. They provide
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strength to the tool nozzle and also prevent chipping and improve wear resistance. The chamfer provides a large negative rake angle resulting in large plastic strains and hence higher cutting temperature, which adversely affects the tool’s performance and the hone exerts high cutting forces. A proper selection of tool geometry along with the cutting conditions for better surface quality of hard materials is suggested [6,7] Tool steels used for hot forging dies, press work, extrusion, and form tools work under extreme condition in a tribological systems. These steels are considered as difficultto-machine steels, and more particularly, it has been reported that the machinability of tool steel is extremely poor. During machining, these steels generate high chip–tool interface temperatures and high cutting forces [8]. The thrust force is highest in hard turning because of low feed rate and low depth of cut, which is much smaller than the nose radius (0.8 mm). Such a large value of thrust force becomes a serious threat to the dimensional accuracies of the component due to greater radial deflection of the workpiece, and it also causes chattering if the dynamic loop stiffness of the machining system is low [9]. Surface integrity such as surface roughness plays an important role in the functionality of the machined component. Surface roughness is worse for high CBN content tool than the low CBN content tools. The reason is plucking out of CBN grains from the sintered tool body by the hard carbide particles of the workpiece, which then produce abrasive effect on the workpiece [10]. Cutting forces data are essential while selecting work material and tool material. It is also required while selecting an appropriate machine tool with adequate power. To avoid excessive distortion of the workpiece and rapid wear of cutting tool and fixtures, the knowledge of cutting forces is must [11]. A study by Huang et al. [12] proposed a cutting force model to consider the tool thermal properties. The model was applied to hard turning of AISI H13 tool steel using CBN tools of varying CBN content, i.e., CBN-L and CBN-H inserts. The model predicted low major cutting forces and low thrust forces, but high chip–tool interface temperatures for CBN-L tools, while high major cutting force and high thrust forces with low chip–tool interface temperatures were predicted for CBN-H tools. This clearly suggests that the cutting forces and the chip–tool temperature are greatly influenced by CBN content, thermal, physical, and mechanical properties of the CBN tools. Tool wear also affects the cutting zone temperature and generates residual stresses on the machined surface [13]. The difference in behavior by the
Table 1 Chemical composition of AISI D6 tool steel
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different grades of CBN inserts is due to the transformation of boron nitride from hexagonal to cubic form, grain size, shape, porosity, defects, inclusions, and binder material [14]. CBN tools are classified as high CBN content tools (CBNH) constituting more than 70 % CBN with a metallic binder like cobalt, medium CBN tools with 50–70 % CBN content with tin as a binder, and low CBN tools (CBN-L) with less than 50 % CBN content along with a ceramic binder like TiN [15]. RSM-based mathematical models have been proposed for predicting the output characteristic along with parametric analysis on machinability investigation of AISI D2 cold work tool steel [16]. Optimization techniques such as RSM and firefly algorithm [17], genetic algorithm [18], and other statistical investigations [19] for machining study have been reported in the literature. In the present work, an attempt has been made to develop mathematical models based on statistical methodology for each tool grade and to study the effect of cutting parameters and tool material influences on machinability characteristics of AISI D6 steel during hard turning. 2 Experimental Details 2.1 Work Material The work material used is AISI D6 tool steel in hardened condition (54±2HRC). The chemical composition of the materials is given in Table 1. The microstructure observed in the hardened steel is of spheroidal carbides in tempered martensite as shown in Fig. 1, and dispersion of small and large particles of metal carbides in the ferrite matrix is clearly noted. 2.2 Selection of Tool Material CBN compact is classified into two categories: In CBN-L, the compact has the CBN particles bound on contact with a ceramic binder material (TiN, TiC, TiAlN, TiCN), and CBNH compact is formed by binding together the CBN particles using a small amount of metallic binder (Co, Sn). For industrial applications, the former type tools are recommended for machining of hardened steel due to their excellent wear resistance. The later is used for machining cast iron, heat-resistant alloys, and powder metallurgy parts due to their excellent heat resistance and toughness [20]. Most of the researchers have carried out their study using CBN-H tools for machining hardened steel, which is uneconomical in real applications.
Elements
C
Si
Mn
Cr
Ni
Mo
V
Cu
Al
Co
W
Weight %
2.130
0.238
0.240
11.10
0.090
0.032
0.130
0.03
0.06
0.025
0.620
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Fig. 1 Dispersion of small and large particles of carbides in the ferrite matrix
Fig. 2 Machining setup
The three tools used in the present work are CBN-L types and are designated as CBN-1, CBN-2, and CBN-3 based on tool substrate property, thermal, and mechanical properties as given in Table 2. The nose radius of all the tools was 0.8 mm, and an approach angle of 45o was kept throughout the machining.
possible experiment errors. Force measuring dynamometer (make TeLC Germany) was used to measure the main cutting force (Fc ) and the thrust force (Fp ). Cutting temperature of the tool tip was measured with the help of InGaAs radiation sensor (Impact electronic series 300, 24VDC, 4– 20 mA; measuring range 300–800 ◦ C). Software XKM 2000 was used in the dynamometer for cutting force and temperature measurements. A surface roughness tester (SJ-301 Mitotoyo, Japan), X -axis (drive units) with measuring range of 12.5 µm, was used to measure the surface roughness Ra during the experiments. Surface roughness was measured off-line with the profilometer by taking the measurements across the lay. Three measurements (λc = 0.8 mm; N = 5) were taken along the feed length for each sample length machined, and measurements were taken about 120◦ apart; the average Ra value was used in the analysis. Workpiece of AISI D6 tool steel of Ø54.5 and Ø59.5 mm was prepared after machining 1 mm thickness of the top cylindrical sur-
2.3 Experimental Procedure A three-factor three-level full-factorial design was used to determine the effects of tool material properties and cutting conditions on machinability of AISI D6 tool steel. Based on 33 full-factorial designs, a total of 27 experiments were carried out on an all geared DRO Lathe (Model: Bajaj-Pioneer-175 Geared Headed), 8-spindle cutting speeds (8–1,200 rpm), and 24 number of feeds as shown in Fig. 2. Randomization is strictly maintained as per the run order suggested by the DOE, and 14 experiments were repeated to minimize the Table 2 Thermal and mechanical properties of tool materials [9,13]
Tool type
CBN-1
CBN-2
CBN-3
Make
Kyocera
SumiBoron
SumiBoron
Grade
KBN 25M
BNX 10
BNX 20
Binder
TiC
TiCN
TiN
Grain size (µm)
0.5–1
3
3
CBN (%)
45
40–45
55–60
Base material hardness (HV)
2,500
2,800–3,000
3,200–3,400
Transverse rupture strength (Gpa)
1,250
800–900
1,000–1,100
Thermal conductivity (Watt/mK)
44
Unknown
54
Edge geometry
25◦ × 0.12 mm
30◦ × 0.1 mm
30◦ × 0.1 mm
(Chamfered + Honed)
(Chamfered)
(Chamfered)
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Table 3 Factors and factor levels Symbol
Factor
Units
Level-1
Level-2
Level-3
Vc
Cutting speed
m/min
54.03
93.62
132.92
f
Feed
mm/rev
0.08
0.133
0.21
CBN
CBN Grades
Categorical
CBN-1
CBN-2
CBN-3
face, in order to eliminate any surface defects and wobbling, then centered, and faced. Corresponding cutting speeds were equalized at different workpiece diameters to the extent that rotational speeds ratio permit. Short-duration tests were performed (machined length of 20 mm) without coolant. The test conditions in Table 3 span the range of recommended values from tool supplier and are comparable to cutting parameters in previous studies of hard turning. Three CBN grades from two different manufacturers were used, so that a particular tool manufacturer is not endorsed. This cutting condition represents machining at low and medium speeds to ensure that
the CBN tool wear is not rapid and to obtain surface roughness value close to those obtained in grinding. Moreover, constant depth of cut 0.15 mm was used as a usual value of material stock removal in grinding operation. Each test was realized with fresh cutting edge. The obtained experimental data are summarized in Table 4.
3 Results and Discussions Analysis of the results of these trials was done using standard statistical methodology [21] using Design Expert Software (version 7) as per the full-factorial DOE model as shown in Table 5. All responses were analyzed using 2FI polynomial design model with ANOVA standard deviation. Graphical analyses are explained with the help of 3D response surface graphs obtained during the regression analysis. The statistical methodology consists of first assessing statistical normality of the data and, wherever necessary, applying appro-
Table 4 Experimental data along with standard order and run as per DOE matrix Stand. order
Run order
CBN grades
Cutting speed (m/min)
Feed (mm/rev)
Main cutting force (Fc )
Thrust force (Fp )
Cutting temperature (T)
Surface roughness (Ra )
1
1
CBN-1
54.03
0.080
67
184
482
0.47
2
10
CBN-1
54.03
0.133
72
187
484
0.71
3
26
CBN-1
54.03
0.210
88
148
479
1.75
4
2
CBN-1
93.62
0.080
68
201
551
0.79
5
13
CBN-1
93.62
0.133
86
223
549
0.81
6
14
CBN-1
93.62
0.210
93
183
534
1.83
7
4
CBN-1
132.92
0.080
45
172
511
0.56
8
20
CBN-1
132.92
0.133
67
192
515
0.77
9
24
CBN-1
132.92
0.210
83
128
490
1.81
10
19
CBN-2
54.03
0.080
64
158
458
0.58
11
23
CBN-2
54.03
0.133
75
156
483
0.83
12
12
CBN-2
54.03
0.210
92
177
481
1.65
13
25
CBN-2
93.62
0.080
62
178
537
0.53
14
18
CBN-2
93.62
0.133
79
178
554
0.93
15
17
CBN-2
93.62
0.210
99
198
536
1.72
16
3
CBN-2
132.92
0.080
57
160
515
0.48
17
9
CBN-2
132.92
0.133
66
158
509
0.89
18
16
CBN-2
132.92
0.210
88
165
504
1.66
19
8
CBN-3
54.03
0.080
62
165
429
0.54
20
11
CBN-3
54.03
0.133
90
215
478
0.92
21
5
CBN-3
54.03
0.210
97
196
463
2.14
22
15
CBN-3
93.62
0.080
71
195
530
0.57
23
27
CBN-3
93.62
0.133
82
244
535
0.92
24
22
CBN-3
93.62
0.210
98
205
498
2.39
25
7
CBN-3
132.92
0.080
69
188
515
0.54
26
6
CBN-3
132.92
0.133
76
190
512
0.84
27
21
CBN-3
132.92
0.210
85
195
524
2.53
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Table 5 Full-factorial DOE model Study type
Factorial
Experiment
27
Initial design
Full factorial
Blocks
No blocks
Design model
2FI-analyzed as a polynomial using ANOVA standard deviation
Response
Name
Units
Obsr.
Min.
Max.
Model
Fc
Major cutting force
N
27
45
99
RQuadratic
Fp
Thrust force
N
27
128
244
RQuadratic
T
Temperatures
◦C
27
429
554
RQuadratic
Ra
Surface roughness
µm
27
0.47
2.53
RQuadratic
Table 6 ANOVA table (partial sum of squares) for reduced quadratic model (response Fc ) Source Model A-Cutting speed
Sum of squares
DF
Mean squares
4,496.99
5
899.40
280.06
1
280.06
3,659.95
1
3,659.95
C-CBN grades
229.41
2
114.70
A2
327.57
1
13.63
Residuals
504.87
21
24.04
Cor Total
5,001.85
26
B-Feed
SD Mean C.V.% PRESS
4.90
F value
Prob > F
37.41
< 0.0001
11.65
0.0026
6.9
152.24
0.0001
90.26
0.0196
2.83
4.77
R-squared
0.8991
Adj R-squared
0.8750
6.36
Pred R-squared
0.8342
829.13
Adeq Precision
20.105
3.1 Major Cutting Force Models Statistical analysis of Fc was made with response surface reduced (backward transformation) quadratic regression model
Remarks Significant
0.0014
77.07
priate mathematical transformations to ensure normality of that data. These normalized data were used to produce a series of “best fit” regression equations. The ANOVA table of each output characteristic contains the usual sum of squares, degree of freedom, mean squares, and the test statistics “F.” Values of “Prob > F” less than 0.050 indicate that the model terms are significant. In addition to the basic analysis of variance, the diagnostic checking is done by the software, and the program displays some useful information like “R-squared (R 2 ), adjusted R-squared (Adj R 2 ), prediction R-squared (Pred. R 2 ), adequate precision (Adeq. R 2 ), standard deviation (SD), mean, degree of freedom (DF) percentage contribution (% Contr.), coefficient of variation (C.V.%), and prediction error sum of squares (PRESS) values. The mathematical regression equations can be used to predict the main cutting force, thrust force, cutting temperature, and surface roughness during hard turning of AISI D6 steel using CBN inserts.
% Cont.
by selecting the backward elimination procedure to automatically reduce the insignificant terms. The resulting ANOVA table for the reduced quadratic model for Fc is shown in Table 6. The quadratic model with P value > 0.05 suggests that the model is significant. Various R 2 statistics of Fc are given in Table 6, R 2 = 0.8991 indicates that 89.91 % of the total variations are explained in the model. Adjusted R 2 = 0.8750 indicates that 87.50 % of the total variability is explained by the model after considering the significant factors. The predicted R 2 = 0.8342 is in reasonable agreement with the adjusted R 2 of 0.8750, and the model would be expected to explain 83.42 % of the variability in new data. Lower value of C.V. = 6.36 % indicates improved precision and reliability of the conducted experiments. The P value indicates the probability that the factor or interaction is significant or insignificant. The result indicates that A, B, C, A2 are significant model terms. Examination of the P value shows the feed to be dominant factor with 90.26 % contribution to Fc followed by cutting speed and CBN grades. The mathematical models in the form of regression equations to predict the main cutting force (Fc ) with cutting speed (Vc ), feed rate (f) for three different tools are given below:
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Fig. 5 3D surface plots of Fc for CBN-3
Fig. 3 3D surface plots of Fc for CBN-1
Fig. 4 3D surface plots of Fc for CBN-2
FcCBN−1 = 16.354 + 0.788Vc + 218.14 f −4.749 × 10−3 Vc2
(1)
FcCBN−2 = 17.81 + 0.788Vc + 218.14 f −4.749 × 10−3 Vc2
(2)
FcCBN−3 = 23.132 + 0.788Vc + 218.14 f −4.749 × 10−3 Vc2
(3)
Equations (1), (2), and (3) are valid for finish hard turning in continuous cutting of AISI D6 tool steel using CBN-1, CBN-2, CBN-3, respectively, under the conditions 54.03 ≤ Vc ≤ 132.92 m/min; 0.08 ≤ f ≤ 0.21 mm/rev; and constant depth of cut = 0.15 mm. From the ANOVA table of Fc , it is observed that the feed has the most significant effect on it, which is in agreement with the study presented by Thiele [22]; Quin and Hosson [23]. The regression equations of Fc suggest that Fc CBN−3 > Fc CBN−2 > Fc CBN−1 , clearly indicating the influences of tool material properties. In Figs. 3, 4, 5, the change in major cutting force can be studied de-
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pending on feed rate and cutting speed for different CBN tools. It can be seen that the major cutting force increases in parallel with the increase in feed rate compared to a curvilinear profile (quadratic effect) with the increase in cutting speed (Vc2 ). The curvilinear profile in the figures is in accordance with the quadratic model fitted. The quadratic effect of the model term Vc2 can be explained for the single bend of the curve, i.e., the curvilinear profile, and is concave due to negative signs of the coefficients (Eqs. 1–3). This can be explained in a simple way; as the cutting speed increases, the friction between the workpiece and the tool cutting edge increases, and hence, the temperature at the cutting zone increases. This induces a thermal softening effect, and workpiece gets softened, which then requires less cutting force. The other reason is at a high cutting speed, situation improves the formation and removal of chips at the cutting region. Also, as the feed rate increases, the undeformed chip thickness increases, and since the cutting force is directly proportional to undeformed chip thickness, more forces is required for chip formation; therefore, the feed rate becomes a major contributory to the Fc component of the cutting forces in accordance with a previous study [22]. Studies also suggest that there is no appreciable change in magnitude of the main cutting force and feed force (axial force) during hard turning, when the cutting speed was increased from 140 to 240 m/min [23,24]. Therefore, to minimize Fc , it is necessary to select low feed and high cutting speed. The other observation that can be made from these graphs is that higher Fc is encountered by CBN-3, possibly because of softening effect. Since the hardness of CBN-1 < CBN-2 < CBN-3, the hardness differences between the workpiece and the tool are less in case of CBN-3, thereby requiring more of this force than the other two tools CBN-1 and CBN-2. Therefore, tool with low CBN content along with low hardness should be selected for minimizing Fc .
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Table 7 ANOVA table (partial sum of squares) for reduced quadratic model (response: Fp ) Source
Sum of squares
DF
Mean squares
F value
Prob > F
% Contr.
12,429.55
8
1,553.69
8.77
< 0.0001
A-Cutting speed
80.22
1
80.22
0.45
0.5096
0.875
B-Feed
31.24
1
31.24
0.18
0.6796
0.35
C-CBN grades
4,035.19
2
2,017.59
11.38
0.0006
22.12
BC
2,558.88
2
1,279.44
7.22
0.0050
13.99
A2
4,195.85
1
4,195.85
23.67
0.0001
46.017
B2
1,528.17
1
1,528.17
8.62
0.0038
16.75
Residuals
3,190.30
18
Cor total
15,619.85
26
Model
SD Mean C.V.% PRESS
13.31 182.93
Remarks Significant
177.24
R-squared
0.7958
Adj R-squared
0.7050
7.28
Pred R-squared
0.5546
6,956.79
Adeq Precision
10.586
3.2 Thrust Force Models Statistical analysis of Fp was made with response to surface reduced (backward transformation) quadratic regression model. Proceeding with the backward elimination procedure to remove the insignificant terms, the resulting ANOVA table for the reduced quadratic model for thrust force is shown in Table 7. The quadratic model with P value > 0.05 suggests that the model is significant. The predicted R 2 value is 0.5546 and is in reasonable agreement with the adjusted R 2 value of 0.7050. Both predicted R 2 and adjusted R 2 are within the range of 0.20 of each other as obtained in this case. The adequate precision is 10.586 as desired and indicates an adequate model discrimination. This justifies the accuracy of the predicted model. The result indicates in this case that C, BC, A2 , and B2 are all significant model terms. The quadratic effect of cutting speed is the most significant term with 46.017 % contribution. The effect of CBN grades alone is a significant factor with 22.12 % and at the same time, it is interesting to note that the interaction of feed with CBN grades is also significant. It implies that feed as a secondary factor affects the thrust force. Cutting speed and feed as a lone factor has little influence on the thrust force. It is reported that feed alone has no influence on thrust force. The equations for predicting thrust force using three different tools CBN-1, CBN-2, CBN-3 are as follows: Fp CBN−1 = 19.535 + 3.124Vc + 877.732 f − 0.017Vc2 −3,932.755 f 2 Fp CBN−2 = −46.51 + 3.124Vc + 1,275.190 f −3,932.755 f 2
(4) − 0.017Vc2 (5)
Fp CBN−3 = −13.960 + 3.124 Vc + 1,253.190 f − 0.017Vc2 −3,932.755 f 2
(6)
Fig. 6 3D surface plots of FP for CBN-1
In conventional turning of soft annealed steel, the major cutting force is highest among the three cutting forces (the other two being thrust force and feed force). This is due to the fact that turning is done with relatively high depth of cut and larger nose radius. However, the thrust force is highest in hard turning because of small nose radius and feed rate. The cutting action in hard turning occurs on a very small area of the tool nose often at the chamfer resulting in high thrust force. Response surface graphs show the evolution of the thrust force with increase in cutting speed and feed rates for all the three CBN tools as shown in Figs. 6, 7, 8. It can be seen from the graphs that the thrust force follows a curvilinear profile with the increase in cutting speed as well as the feed. Again, the curvilinear profile along the feed and cutting speed is in accordance with the quadratic model fitted. At low speed and low feed, the thrust force in case of CBN-2 and CBN-3 is low but increases as the feed and cutting speed
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increase and then further decrease with further increase in feed and cutting speed. The thrust force on CBN-1 is high at low feed because of the presence of the chamfer and hone but decreases as the feed rate increases. This finding is consistent with the previous research for inserts with chamfer angles and hones [25,26]. The large negative effective angle formed due to the chamfer results in high thrust force initially and further decreases due to thermal softening. The initial thrust force is highest for CBN-1 (KBN 25M) tool because of geometry effect, i.e., availability of the chamfered and honed edge, whereas CBN-2 and CBN-3 had less initial thrust force due to non-availability of the hone at the cutting edge.
3.3 Cutting Temperature Models
Fig. 7 3D surface plots of FP for CBN-2
Statistical analysis of “T” was made with response surface reduced (backward transformation) quadratic regression model. Table 8 shows the ANOVA table with model F value of 16.84, which implies that the model is significant. In this case, A and A2 are significant model terms. R 2 = 0.8821 indicates that 88.21 % of the total variations are explained in the model. Adjusted R 2 = 0.8297 indicates that 82.97 % of the total variability is explained by the model after considering the significant factors. The predicted R 2 = 0.7282 is in reasonable agreement with the adjusted R 2 of 0.8297, and the model would be expected to explain 72.82 % of the variability in new data. Lower value of C.V. = 2.53 % indicates the improved precision and reliability of the conducted experiment. After removing the insignificant terms, the equations for predicting the cutting temperature (T) with cutting speed (Vc ), feed rate (f) as the process parameters are obtained as follows:
Fig. 8 3D surface plots of FP for CBN-3
Table 8 ANOVA table (partial sum of squares) for reduced quadratic model (response: T) Source
Sum of squares
Model A-Cutting speed B-Feed
DF
Mean squares
F value
Prob > F
% Contr.
22,060.56
8
2,757.57
16.84
TCBN−2 > TCBN−3 . The variation in temperatures is due to variation in thermal conductivity of the tools. The tool material CBN grades have very little influence on the cutting temperature with 1.86 % contribution as per the ANOVA. It can be seen from the response surface graphs, i.e., Figs. 9, 10, 11, that increase in temperature occurs due to increase in cutting speed for all the three tools, and increase in feed has little influence on the cutting temperature. This may be due to increase in friction at higher cutting speeds, which then induces a temperature rise in the cutting zone. At higher cutting speed situations, the formation and removal of chip improves in the cutting region, thereby reducing the cutting temperature at the cutting zone. The cutting temperature generated also depends on the thermal conductivity of the tools. Since there is a slight difference in thermal conductivity of the three tools owing to their CBN content, the slight variation observed in the response surface graph Fig. 11 is obvious. Higher the CBN content, higher is the thermal conductivity, and lower the CBN content higher is the initial cutting temperature of the cutting zone [24]. The amount of heat transferred to the workpiece by all the three tools depends on the thermal conductivity of tool material. The thermal
Fig. 11 3D surface plots of “T” for CBN-3
conductivity of CBN-1 is low as compared to CBN-2 and CBN-3, and therefore, the amount of heat transferred to the workpiece by CBN-1 is high and the material gets softened due to high heat content of the workpiece. The main requirement of a cutting tool for machining hard materials is thermal conductivity and hardness [27]. Therefore, CBN-3 is the best tool in terms of thermal conductivity and hardness, and the sharpness of the cutting edge is retained for a longer time. From the response surface analysis Figs. 9, 10, 11, the lowest cutting temperatures are observed during machining using CBN-3, which agrees well with the model equations. ANOVA indicates that the CBN grades are not significant nor do have any secondary effect on temperature within the cutting speed range of 54.03 ≤ V ≤ 132.92 m/min. But at higher cutting speeds, the effect will be prominent. 3.4 Surface Roughness Models Statistical analysis of “Ra ” was made with response to surface reduced (backward transformation) quadratic regres-
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Table 9 ANOVA table (partial sum of squares) for reduced quadratic model (response Ra ) Source
Sum of squares
Model
10.41
DF
Prob > F
Mean squares
F value
% Contr.
120.62