Surface roughness model in turning hardened hot work steel using mixed ceramic tool/ Pavirsiaus siurkscio nustatymo modelis tekinant sukietinta, karsciui atsparu pliena mineralu keramikos irankiu.

Fnides, B. ; Yallese, M.A. ; Mabrouki, T. 等

Nomenclature

[a.sub.p]--depth of cut, mm; f--feed rate, mm/rev; HRC--Rockwell hardness; [R.sub.2]--coefficient of determination; Ra--arithmetic mean roughness, [micro]m; Rt--total roughness, [micro]m; Rz--mean depth of roughness, [micro]m; [r.sub.[epsilon]--tool nose radius, mm; [V.sub.c]--cutting speed, m/min; [alpha]--relief angle, degree; [gamma]--rake angle, degree; [lambda]--inclination angle, degree; X--major cutting edge angle, degree.

1. Introduction

Hard turning is a cutting process defined as turning materials with hardness higher than 45 HRC with appropriate cutting tools and under high cutting speed. Machining of hard steel using advanced tool materials, such as cubic boron nitride and mixed ceramic, has more advantages than grinding or polishing, such as short cycle time, process flexibility, compatible surface roughness, higher material removal rate and less environment problems as there is no use of cutting fluid. This process has become a normal practice in industry because it increased productivity and reduced energy consumption [1-3].

Alumina ([Al.sub.2][O.sub.3]) based ceramics are considered to be one of the most suitable tool materials for machining hardened steels because of their high hot hardness, wear resistance and chemical inertness [4].

Surface roughness is classified among the most important technological parameters in machining process. It is in relation to many properties of machine elements such as wear resistance, the capacity of fit and sealing. Theoretical surface roughness achievable based on tool geometry and feed rate is given approximately by the formula: Ra = 0.032 [f.sup.2] / [r.sub.[epsilon]. In hard turning, surface finish has been found to be influenced by a number of factors such as feed rate, cutting speed, tool nose radius and tool geometry, cutting time, workpiece hardness, stability of the machine tool and the workpiece set up, etc [5-6].

In order to know surface quality values in advance, it is necessary to employ empirical models making it feasible to do predictions in a function of operation conditions. To calculate constants and coefficients of these models, we used software Minitab characterized by Analysis of Variance: ANOVA, multiple regression and Response Surface Methodology (RSM).

2. Experimental procedure

The material used for experiments is X38CrMoV5-1, hot work steel which is popular for the use in hot form pressing. Its resistance to high temperature and its aptitude for polishing enable it to meet the most severe requests in hot dieing and moulds under pressure [7]. Its chemical composition is as follows: 0.35% C; 5.26% Cr; 1.19% Mo; 0.5% V; 1.01% Si; 0.32% Mn; 0.002% S; 0.016% P; 1.042% other components and 90.31% Fe. The workpiece is of 270 mm length and 75 mm in diameter and it is machined under dry condition. It is hardened to 50 HRC. Its hardness was measured by a digital durometer DM2D. The lathe used for machining operations is TOS TRENCIN; model SN40C, spindle power 6.6KW. A roughness meter (2d) Surftest 201 Mitutoyo was selected to measure different criteria of surface roughness (Ra, Rt and Rz) as shown in Fig. 1. Roughness values were obtained without disassembling the workpiece in order to reduce uncertainties due to resumption operations.

[FIGURE 1 OMITTED]

The cutting insert used is a mixed ceramic (CC650), removable, of square form with eight cutting edges and having designation SNGA 120408 T01020. The insert is mounted on a commercial toolholder of designation PSBN[R.sup.2]525M12 with the geometry of active part characterized by the following angles: x = 75[degrees]; a = 6[degrees]; y = = -6[degrees]; X = -6[degrees] [8]. Three levels were defined for each cutting variable as given in Table 1. The variable levels were chosen within the intervals recommended by the cutting tool manufacturer. Three cutting variables at three levels led to a total of 27 tests.

3. Results and discussion

Table 2 presents experimental results of surface roughness criteria (Ra, Rt and Rz) for various combinations of cutting regime elements (cutting speed, feed rate and depth of cut) according to [3.sup.3] full factorial design. Minimal values of surface roughness criteria (Ra, Rt and Rz) were obtained at [V.sup.c] = 180 m/min, f = 0.08 mm/rev and [a.sub.p] = = 0.15 mm (test number 19). That means increasing of cutting speed with the lowest feed rate and depth of the cut lead to decreasing of surface roughness.

Maximal values of surface roughness criteria (Ra, Rt and Rz) were registered at [V.sub.c] = 90 m/min and f = = 0.16 mm/rev and [a.sub.p] = 0.45mm (test number 9). In order to achieve better surface finish, the highest level of cutting speed, 180 m/min, the lowest level of feed rate, 0.08 mm/rev, should be recommended.

3.1. ANOVA for Ra

The results of analysis of variance (ANOVA) for surface roughness Ra are shown in Table 3. This table also shows the degrees of freedom (DF), sum of squares (SS), mean square (MS), F-values (F-VAL.) and probability (PVAL.) in addition to the percentage contribution (Contr. %) of each factor and different interactions.

A low P-value indicates statistical significance for the source on the corresponding response [9-10].

It is clear from the results of ANOVA that the feed rate is the dominant factor affecting surface finish Ra. Its contribution is 77.61%. The second factor influencing Ra is cutting speed. Its contribution is 18.05%. As for the depth of cut, its contribution is 2.48%. The interactions [V.sub.c]xf and [V.sub.c]xap are significant but interaction [a.sub.p]xf is not significant. Respectively, their contributions are (1.63; 0.17 and 0.03) %.

To understand the hard turning process in terms of surface roughness Ra, mathematical model was developed using multiple regression method.

Ra model is given by equation (1). Its coefficient of correlation [R.sup.2] is 96.24%.

Ra = 0.19254--0.00075[V.sub.c] + 3.54167f + 0.11667[a.sub.p] - - 0.00417[V.sub.c]xf + 0.00019[V.sub.c]x[a.sub.p] (1)

3. 2. 3D Surface plots of Ra

3D Surface plots of Ra vs. different combinations of cutting regime elements are shown in Figs. 2, 3 and 4. These figures were obtained using response surface methodology (RSM).

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

3. 3. Effect graphs of the main cutting regime on Ra

Fig. 5 gives the main factor plots for Ra. Surface roughness Ra appears to be a decreasing function of [V.sub.c]. This figure also indicates that Ra is an almost linear increasing function of f. But the depth of cut [a.sub.p] has a little effect on Ra.

3. 4. ANOVA for Rt

Table 4 presents ANOVA results for Rt. It can be seen that the feed rate is the most important factor affecting surface finish Rt. Its contribution is 63.03%.

The second factor influencing Rt is cutting speed. Its contribution is 31.73%. As for the depth of cut, its effect is not significant because its contribution is 0.55%. The interactions [V.sub.c]xf, [V.sub.c]xap and [a.sub.p]xf are not significant. Respectively, their contributions are (0.61; 0.58 and 1.46) %. Rt model is given by Eq. (2). Its coefficient of correlation [R.sup.2] is 89.42%.

Rt = 2.9681--0.0069 [V.sub.c] + 12.2917f + 0.2444a (2)

3. 5. 3D Surface plots of Rt

Figs. 6, 7 and 8 illustrate 3D surface plots of Rt according to the response surface methodology.

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

[FIGURE 8 OMITTED]

3. 6. Effect graphs of the main cutting regime on Rt

[FIGURE 9 OMITTED]

Fig. 9 shows the main factor plots for Rt. Surface roughness Rt appears to be a decreasing function of [V.sub.c].

This figure also indicates that Rt is an almost linear increasing function of f But the depth of cut [a.sub.p] has not an effect on Rt.

3. 7. ANOVA for Rz

ANOVA results for Rz are indicated in Table 5. It can be noted that the feed rate affects Rz in a considerable way. Its contribution is 91.14%. The second factor influencing Rz is cutting speed. Its contribution is 7.52%. As for the depth of cut, its effect is not significant because its contribution is 0.18%. The interaction [V.sub.c]xf is also significant. Its contribution is 0.80%. The interactions [V.sub.c]x[a.sub.p] and [a.sub.p]xf are not significant. Respectively, their contributions are (0.03 and 0.12) %. Rz model is given by equation (3). Its coefficient of correlation [R.sup.2] is 98.69%.

[FIGURE 10 OMITTED]

[FIGURE 11 OMITTED]

[FIGURE 12 OMITTED]

Figs. 10, 11 and 12 show 3D surface plots for Rz. These figures were obtained by the response surface methodology for different combinations of cutting regime elements.

3. 9. Effect graphs of the main cutting regime on Rz

Fig. 13 highlights the main factor plots for Rz. Surface roughness Rz appears to be an almost linear decreasing function of [V.sub.c]. This figure also indicates that Rz is an almost linear increasing function of f. But the depth of cut [a.sub.p] has not an effect on Rz.

[FIGURE 13 OMITTED]

4. Conclusion

The tests of straight turning carried out on grade X38CrMoV5-1 steel treated at 50 HRC, machined by a mixed ceramic tool (insert CC650) enabled us to develop statistical models of surface roughness criteria. These models were obtained by the software Minitab using multiple regression method.

The results revealed that feed rate seems to influence surface roughness more significantly than cutting speed. However, the depth of cut is not significant. Thus, if we want to get good surface finish and much removed amount of chip, we must use the highest level of cutting speed, 180 m/min, the lowest level of feed rate, 0.08 mm/rev and the highest level of depth of cut, 0.45 mm.

Statistical models deduced defined the degree of influence of each cutting regime element on surface roughness criteria. They can also be used for the optimization of hard cutting process.

This study confirms that in dry hard turning of this steel and for all cutting conditions tested, the found roughness criteria are close to those obtained in grinding (Ra < 0.73 [micro]m).

Acknowledgements

This work was completed in the laboratory LMS (University of Guelma, Algeria) in collaboration with LaMCos (CNRS, INSA-Lyon, France). The authors would like to thank the Algerian Ministry of Higher Education and Scientific Research (MESRS) and the Delegated Ministry for Scientific Research (MDRS) for granting financial support for CNEPRU Research Project--LMS: J2401/03/80/06 (University of Guelma).

Received March 10, 2009

Accepted May 11, 2009

References

[1.] Chen, W. Cutting forces and surface finish when machining medium hardness steel using CBN tools. -International Journal of Machine Tools & Manufacture, 2000, 40, p.455-466.

[2.] Singh, D., Rao, P.V. A surface roughness prediction model for hard turning process. -Int. J. Adv. Manuf. Technol., 2007, 32, p.1115-1124.

[3.] Fnides, B., Yallese, M.A., Aouici, H. Hard turning of hot work steel AISI H11: Evaluation of cutting pressures, resulting force and temperature. -Mechanika. -Kaunas: Technologija, 2008, Nr.4(72), p.59-63.

[4.] Dewes, R.C., Aspinwall, D. K. A review of ultra high speed milling of hardened steels. -Journal of Materials Processing Technology, 1997, 69, p.1-17.

[5.] Fnides, B., Aouici, H., Yallese, M.A. Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic. -Mechanika. -Kaunas: Technologija, 2008, Nr.2(70), p.73-78.

[6.] Ozel, T., Hsu, T.K., Zeren, E. Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel. -Int J Adv Manuf Technol, 2005, 25, p.262-269.

[7.] Site internet: http: //www. Buderus-steel.com.

[8.] SANDVIK Coromant, Catalogue General, Outils de coupe Sandvik Coromant, Tournage--Fraisage--Percage--Alesage--Attachements, 2009.

[9.] Paulo Davim, J., Figueira, L. Machinability evaluation in hard turning of cold work tool steel (D2) with ceramic tools using statistical techniques. -Journal of Materials & Design, 2007, 28, p.1186-1191.

[10.] Sahin, Y., Motorcu, A. R. Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. -International Journal of Refractory Metals & Hard Materials, 2008, 26, p.84-90.

B. Fnides*, M. A. Yallese*, T. Mabrouki**, J-F. Rigal**

* Mechanics and Structures Laboratory (LMS), Department of Mechanical Engineering, May 08th 1945 University, Guelma 24000, Algeria, E-mail: [email protected]

** Laboratoire de Mecanique des Contacts et des Solides (LaMCoS), INSA de Lyon, 20 Avenue Albert Einstein, 69 621 Villeurbanne Cedex, France, E-mail: [email protected]

Table 1

Assignment of the levels to the variables

Level      [V.sub.c], m/min   f, mm/rev   [a.sub.p], mm

1(low)             90            0.08          0.15
2(medium)         120            0.12          0.30
3(high)           180            0.16          0.45

Table 2

Design layout and experimental results for surface roughness criteria

Tests No.   Coded factors                       Actual factors
            [X.sub.1]   [X.sub.2]   [X.sub.3]   [V.sub.c], m/min

1           -1             -1          -1              90
2           -1             -1           0              90
3           -1             -1           1              90
4           -1              0          -1              90
5           -1              0                          90
6           -1              0           1              90
7           -1              1          -1              90
8           -1              1                          90
9           -1              1           1              90
10           0             -1          -1             120
11           0             -1                         120
12           0             -1           1             120
13           0              0          -1             120
14           0              0                         120
15           0              0           1             120
16           0              1          -1             120
17           0              1                         120
18           0              1           1             120
19           1             -1          -1             180
20           1             -1                         180
21           1             -1           1             180
22           1              0          -1             180
23           1              0                         180
24           1              0           1             180
25           1              1          -1             180
26           1              1           0             180
27           1              1           1             180

Tests No.   Actual factors              Performance measures
            f, mm/rev   [a.sub.p], mm   Ra, [micro]m   Rt, [micro]m

1             0.08          0.15            0.41            3.44
2             0.08          0.30            0.43            3.47
3             0.08          0.45            0.44            3.48
4             0.12          0.15            0.53            3.95
5             0.12          0.30            0.55            3.99
6             0.12          0.45            0.56            4.02
7             0.16          0.15            0.69            4.50
8             0.16          0.30            0.71            4.56
9             0.16          0.45            0.72            4.59
10            0.08          0.15            0.35            3.32
11            0.08          0.30            0.40            2.67
12            0.08          0.45            0.41            3.07
13            0.12          0.15            0.46            3.54
14            0.12          0.30            0.49            3.59
15            0.12          0.45            0.51            3.60
16            0.16          0.15            0.56            3.75
17            0.16          0.30            0.59            3.97
18            0.16          0.45            0.62            4.16
19            0.08          0.15            0.30            2.80
20            0.08          0.30            0.33            2.82
21            0.08          0.45            0.34            2.85
22            0.12          0.15            0.43            3.36
23            0.12          0.30            0.46            3.40
24            0.12          0.45            0.47            3.41
25            0.16          0.15            0.54            3.67
26            0.16          0.30            0.56            3.76
27            0.16          0.45            0.58            3.81

Tests No.   Performance measures
            [R.sb.z], [micro]m

1                 2.36
2                 2.39
3                 2.40
4                 3.11
5                 3.15
6                 3.16
7                 3.81
8                 3.84
9                 3.88
10                2.19
11                2.44
12                2.33
13                2.95
14                2.97
15                2.99
16                3.50
17                3.45
18                3.55
19                2.10
20                2.12
21                2.15
22                2.73
23                2.76
24                2.78
25                3.37
26                3.36
27                3.38

Table 3

ANOVA for Ra

Source           DF      SS         MS      F-VAL.    P-VAL.

[V.sub.c]         2   0.060289   0.030144   2170.40   <0.001
f                 2   0.259267   0.129633   9333.60   <0.001
[a.sub.p]         2   0.008289   0.004144    298.40   <0.001
[V.sub.c] * f     4   0.005444   0.001361     98.00   <0.001
[V.sub.c] x ap    4   0.000556   0.000139     10.00    0.003
[a.sub.p] X f     4   0.000111   0.000028      2.00    0.187
Error             8   0.000111   0.000014
Total            26   0.334067

Source           Contr. %

[V.sub.c]         18.05
f                 77.61
[a.sub.p]          2.48
[V.sub.c] * f      1.63
[V.sub.c] x ap     0.17
[a.sub.p] X f      0.03
Error              0.03
Total              100

Table 4

ANOVA for Rt

Source          DF   SS        MS        F-VAL.   P-VAL.   Contr. %

[V.sub.c]        2   2.20027   1.10014    61.80   <0.001   31.73
f                2   4.37090   2.18545   122.77   <0.001   63.03
ap               2   0.03790   0.01895     1.06    0.389    0.55
[V.sub.c] X f    4   0.04246   0.01061     0.60    0.676    0.61
[V.sub.c] x      4   0.04019   0.01005     0.56    0.696    0.58
[a.sub.p]
[a.sub.p] x f    4   0.10104   0.02526     1.42    0.311    1.46
Error            8   0.14241   0.01780                      2.05
Total           26   6.93516                                 100

Table 5

ANOVA for Rz

Source           DF   SS        MS        F-VAL.    P-VAL.   Contr. %

[V.sub.c]        2    0.62370   0.31185    145.36   <0.001    7.52
f                2    7.55932   3.77966   1761.77   <0.001   91.14
[a.sub.p]        2    0.01479   0.00739      3.45    0.083    0.18
[V.sub.c] x f    4    0.06677   0.01669      7.78    0.007    0.80
[V.sub.c] x      4    0.00290   0.00073      0.34    0.845    0.03
[a.sub.p]
[a.sub.p] x f    4    0.00981   0.00245      1.14    0.402    0.12
Error            8    0.01716   0.00215                       0.21
Total           26    8.29445                                  100