identifing the effect of cutting parameters on surface roughness using

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IDENTIFING THE EFFECT OF CUTTING PARAMETERS ON SURFACE ROUGHNESS USING TAGUCHI METHOD IN CNC HORIZONTAL FACE MILLING OF AUTOMOBILE PART SURFACES Ibrahim Sevima, Veysel Karab, S. Cinar Cagana and Mustafa Ugurlua a Mersin

University, Engineering Faculty, Department of Mechanical Engineering, 33343 Ciftlikkoy, Mersin / TURKEY, E-Mail: b CIMSATAS. Mersin-Tarsus Yolu 11. Km. 33004 Mersin/TURKEY, E-Mail:

Abstract In this study, the cutting parameters are optimized for surface roughness as performance criteria in CNC horizontal face milling operation of an automobile part made from 24Mn5 steel. The cutting speed, depth of cut, feed rate and the number of inserts are selected as variable parameter factors. These factors are placed in an L9 (34) array and an experiment plan is determined. In the experimental process, the surface roughness was measured. The signal to noise (S/N) ratio is estimated according to Taguchi method. The optimization is performed according to minimization of the performance criteria in order to determine the optimal cutting parameters and the minimum surface roughness. The effect levels of factors are determined using the variance analysis and the results are verified through experiments. Keywords: Surface roughness, Taguchi method, variance analysis, milling, automobile part surfaces

1. Introduction Main goals of the modern manufacturing industry is to achieve the desired surface quality, increase chip removal rate, decrease time-consuming and machining costs. Chip removal in milling operations, the minimization of surface roughness can be achieved by using of optimum values of the process parameters, such as, cutting speed, depth of cut, feed rate and the number of cutters [1-6]. The surface roughness is the most important attribute for product quality in modern manufacturing industry [7]. The prediction of surface roughness of the machined part is difficult because it is necessary to develop a model or optimization parameter that includes the influence of cutting conditions and the properties of work piece material and cutting tools. Many surface roughness modeling and optimization systems have been studied in recent years. For example, Benardos and Vosniakos [8, 9] aimed prediction of surface roughness in CNC face milling and in machining using . Baek et al. [10] carried out optimization of feed rate in a face milling operation using a surface roughness model. Axinte and Dewes [11] studied surface integrity of hot work tool steel after high speed milling-experimental data and empirical models. Franco et al. [12] focused on a numerical model for predicting the surface profile and surface roughness as a function of feed, cutting tool geometry and tool errors factors. Wang et al. [13] analyzed the influence of cutting condition and tool geometry on surface roughness when slot end milling AL2014-T6.

Ozcelik et al. [14] determined to enable minimum surface roughness under the constraints of roughness and material removal rate, optimum cutting parameters of Inconel 718. Peigne et al. [15] focused on both properties of the cutting vibratory phenomena and their impacts on the roughness of the machined surface. Ozcelik and Bayramoglu [16] presented the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions. Zhang et al. [17] presented a study of the Taguchi design application to optimize surface quality in a CNC face milling operation. Cui et al. [18] investigated the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra-high speed face milling of AISI H13 steel. Lou et al. [19] developed surface roughness prediction technique for CNC end-milling. Yang and Chen [20] demonstrated a systematic approach for identifying optimum surface roughness performance in end-milling operations. Lin [21] proposed an optimization technique for face milling stainless steel based on the Taguchi method. Shetty et al. [22] used metal matrix composites. Bagci and Aykut [23] developed a study of Taguchi optimization method for low surface roughness value in terms of cutting parameters when face milling of the cobalt-based alloy material. Dabade et. al. [24] presented analysis of surface roughness and chip cross-sectional area while machining with self-propelled round inserts milling cutter. Ghani et al. [25] outlined application of Taguchi method in the optimization of end milling parameters. Korkut and Donertas [26] investigated the influence of feed rate and speed on the cutting forces, surface roughness and tool-chip contact length during face milling. In the literature, there are numerously traditional experiment design procedures and the number of the process parameters for prediction of quality surface of the machined surface. However, they are not simplified and suit to use in experimental studies. To overcome this problem, Taguchi methods have been widely applied for optimizations process with a limited number of experiments [27, 33]. The main goal of this investigation is to obtain the lowest surface roughness using optimal milling parameters, while milling 24Mn5 steel with TiC-coated inserts. Taguchi robust design methods for optimizing the process parameters have been used in this study. Additionally, a statistical analysis of variance (ANOVA) was performed to examine the influence of process parameters.

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2. Surface roughness and mechanics of cutting

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If the cutter has z teeth, then during one revolution of the cutter, each tooth travels a distance S z, which is called the feed per tooth:

2.1. Surface roughness Surface roughness is usually known by two methods. The arithmetic mean value (Ra) is based on the schematic illustration of a rough surface, as shown in Fig. 1. It is defined as;

Ra

a b c ... n

(1)

n where all number of measurements. The root-mean square roughness (Rq, formerly identified as RMS) is defined as

a 2 b2

Rq

c 2 ... n

(2)

The datum line AB in Fig. 1 is located so that the sum of the areas above the line is equal to the sum of the areas below the line [1]. 2.1. Mechanics of cutting In milling, the surface roughness is affected by many parameters such as cutting speed, feed rate and depth of cut [15]. The speed of primary motion is the peripheral speed of rotary motion of the cutter, vm, is

.Dm .nR 1000

vm

(m/min)

(3)

where Dm is the outer diameter of the milling cutter in mm and nR is the rotation rate of the cutter in rev/min. Forrectilinear feed motion, its speed is called the feed rate and is denoted by st in mm/min. The feed per revolution, s, is

s

st nR

(mm/tooth)

sz

s z

st nR . z

(mm/tooth)

(5)

3. Experimental work 3.1. Workpiece Material, Cutting Tools and Equipment In this study, to achieve the desired surface quality, decrease time-consuming and manufacturing costs, an automobile work piece had been milled. Work piece materials was 24Mn5 steel. This material is widely used in automobile manufacturing industry. The chemical composition of this work piece is given in Table 1. The steel was manufactured by casting and heat treatment. The steel was tempered at 985oC for 3,5 hours and left in air for normalization heat treatment to remove residual stresses. The mechanical properties of the steel are given in Table 2. Symmetric milling and the milled surfaces are shown in Fig. 2. The properties of the milling machine is given in Table 3. In the experimental studies, 345-063Q22-13M cutter body, 4, 5 and 6 inserts milling apparatus mounted with 345R-1305M-KH hard metal inserts. Geometries and dimensions of cutter body and inserts are given in Table 4. The experiments were conducted under dry cutting conditions. In order to eliminate wear effects, unused cutters are employed for each experiment. The experiments were carried out on OKUMA MA-500HB SPACE CENTER brand milling machine as seen in Fig. 3. Mitutoyo SJ-201 surface roughness measurement apparatus was used for surface roughness measurement as seen in seen Fig. 4. Initial cutting parameter values were selected from the Sandvik Coromant tool catalog.

(4)

. Figure. 1 Schematic illustration of surface roughness profile

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(a)

(b) Figure 2. Milling operation of work piece: a) Symmetric milling, b) The milled surfaces Material 24Mn5

C 0.21-27

Mn 1.10-1.40

Table 1. Chemical composition of material (wt. %) S P Si Ni Cr Mo V 0.05 0.55 0.30-0.50 0.30 0.20 0.15 0.02

Cu 0.35

Al 0.08

Pb 0.02

Sn 0.03

Table 2. Mechanical properties of material Material

Yield strength, (MPa)

Ultimate tensile strength,(MPa)

Relative elongation,(%)

Charpy Impact Strength (Joule)

Brinell Hardness (HB)

24Mn5

350

560

22

22

165-195

Table 3. The properties of the milling machine Model Table Travels (X-Y-Z) Spindle speed Tool storage Motor (VAC) Floor space

OKUMA SPACE CENTER MA-500HB 500 mm x 500 mm 700 mm x 900 mm x 780 mm 6,000 min-1 40 tools 30/22 kW 3,080 mm x5,970 mm

Table 4 Geometries and dimensions of the tool holder and insert [Sandvik Coromant catalog] Tool Holder Standard 345-063Q22-13M Parameter Value Dc 63 mm Dc2 77,08 mm apmax 6 mm dmm 22 mm l1 45 mm K(Tool cutting edge) Insert Standard

45 o

345R-1305M-KH Parameter la apmax s re

Value 13 mm 8.8 mm 6 mm 5,6 mm 0,8 mm

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Figure 3. Photo of the experimental set up

Figure. 4. Surface roughness measurement on work piece

4. The Experimental design 4.1. The Taguchi Method. The achievement of a desirable quality of surfaces roughness is hardly possible by theoretical analysis [1-2]. Due to this difficulty, the operators use trial and error method to determine the operation conditions of the milling machines. This trial and error method requires numerous experiments to achieve the desired surface quality. The trial and error method is time-consuming and increases the manufacturing costs [3-4]. To achieve this problem, an experiment design method has been developed by Dr. Genichi Taguchi. The basic principle of Taguchi method is to use orthogonal arrays to study a large number of variables with a limited number of experiments [27, 33]. Taguchi method (orthogonal array) is widely used in solving engineering problems to significantly decrease the number experiments. The steps adopted Taguchi optimization to obtain the optimum chip removal against conditions minimum surface roughness in this study is shown in Fig. 5 [27, 33, 34]. 4.2. Selection of Orthogonal Array The first step of Taguchi method is the selection of a proper orthogonal array. The arrays are selected according to the number of levels and the total degree of freedom. In this study, the L9 (34) array given in Table 5 is selected for 4 parameters and 3 levels. In general, 3 4=81 experiments are required for this selection. However, the orthogonality of the selected array reduces the number of experiments to 9. Table 6 shows the experiment variables and the L9 (34) orthogonal array

A B C D

Figure.5. The steps for Taguchi optimization Table 5. Factors and levels used in the experiment Level Factors 1 2 3 Cutting speed (m/min) 120 160 200 Feed Rate, (mm/min) 200 250 300 Cut Depth (mm.) 0,3 0,5 0,8 Number of inserts 4 5 6 Table. 6. An L9 (34) Taguchi Orthogonal Array

Factors Cutting Feed Cut Number speed Rate, Depth of (m/min) (mm/min) (mm.) inserts (A) (B) (C) (D) 1 1 1 1 1 2 1 2 2 2 3 1 3 3 3 4 2 1 2 3 5 2 2 3 1 6 2 3 1 2 7 3 1 3 2 8 3 2 1 3 9 3 3 2 1 The experimental results are then transformed into a signal to noise (S/N) using orthogonal arrays in the Taguchi robust design methods. Taguchi used the statistical arrays of signal to noise ratio (S/N) used in experiment design performance criteria in order to reduce the variables. S/N is defined as follows: Experiment Number

S

N

10 log

1 N

n i 1

Yi 2

(6)

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where Yi is the surface roughness for the ith test, n the number of tests and N the total number of data points.

S

N

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(2) equations. The measured and calculated values are given in Table 7. These values are transferred to Table 8 5.1. Variance (ANOVA) Analysis

S

N

array is selected for

determining the optimal cutting parameter levels in chipping process against minimum surface roughness.

5. Analyzing Experiments

and

Evaluating

of

the

Nine experiments are prepared. Surface roughness of each part is measured three times at input and output parts as seen in Fig. 2. The arithmetic mean of three measurements is used in S/N calculations using (1) and

In this study, a statistical analysis of variance (ANOVA) was performed to examine the influence of process parameters. A statistical analysis of variance (ANOVA) is given in Table 9. For a reliability range of 95%, the standard F value is F 0,05;2;18 = 3,55 at degrees of freedom 2 between 27 groups [13-15]. P (%) value indicates influence of process parameters on the surface roughness. Cutting speed is the primary factor affecting the surface roughness (67, 34%). The feed rate is the second factor highly effecting the surface roughness (28,2%). Other parameters effects surface roughness less (Fig. 6.).

Table 7. S/N (dB) ratios and mean surface roughness (Ra), Exp. Input Surface Output Surface In-Out Mean Surf. Repeat Roughness (Ra) Roughness (Ra) Rough. Ra No 1 1,99 3,02 2,505 2 2,83 2,94 2,885 3 2,62 2,19 2,405 1 2,72 1,95 2,335 2 2,83 2,06 2,445 3 2,38 2,02 2,200 1 3,87 2,73 3,300 2 2,63 1,13 1,880 3 3,45 2,32 2,885 1 3,08 2,78 2,930 2 2,51 2,74 2,625 3 2,12 0,92 1,520 1 3,14 3,28 3,210 2 2,84 2,82 2,830 3 2,80 2,60 2,700 1 2,76 2,39 2,575 2 3,43 3,33 3,380 3 2,46 2,89 2,675 1 2,67 2,35 2,510 2 1,36 1,31 1,335 3 1,97 1,24 1,605 1 2,48 1,48 1,980 2 2,10 1,17 1,635 3 1,94 0,63 1,285 1 3,46 2,04 2,750 2 2,63 1,47 2,050 3 2,20 1,36 1,780

Exp. No 1 2

3 4

5 6

7 8 9

Mean Surf. Roughness Ra 2,598 2,327

2,658 2,358

2,387 2,877

1,817

1,633 2,193

S/N Ratio (dB) -8,321 -7,343

-8,798 -7,730

-9,312 -9,245

-5,506 -4,391 -6,970

2,320 S/N Total S/N Mean

Levels 1 2 3 Max Min

Ta Cutting speed (m/min) (A) -8,154 -8,19 -5,622 2,571

Feed Rate (mm/min)( B ) -7,186 -6,446 -8,338 1,892

-67,615 -7,513 S/N Ratio (dB) Dept of Cut (mm)( C ) -7,319 -7,348 -7,303 0,045

Number of inserts (D) -7,632 -7,364 -6,973 0,659

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Variation source A Cutting speed (m/min) B Feed Rate, (mm/min) C Cut Depth (mm.) D Number of inserts Error Total

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Table 9. ANOVA Variance Analysis Results Degree of Sum of Freedom squares Mean square 2 13,0205 6,51025 2 5,45285 2,72642 2 0,00308 0,00154 2 0,65983 0,32992 18 0,1990 0,0110 26 19,3352 9,5681

(

F ratio 5%) 564,57 247,68 0,28 59,98 18,09

Percent P (%) 67,34 28,20 0,02 3,44 1,00 100

Figure. 6. Effect of factors on surface roughness and corresponding S/N ratios 5.2. Verification experiment The test results were analyzed by ANOVA method, and the selected optimum values for the factors are defined as follows (Table 10). Table 10. Optimum Levels of Factors selected according to Surface Roughness Ra values A3 B2 C3 D3 200 250 0,8 6 With these values, the verification test conducted and the results were compared. A verification experiment was done with the three levels of optimal cutting parameters and the results of the experiments are given in Table 11. The verification experiment shows that the minimum surface roughness is achieved by choose of optimal parameter values (Table 12). Each measurement was repeated at least three times.

Table 11. Verification experiment Level Input Output Surface Surface Roughness Roughness, (Ra (Ra 1 1,63 1,23 2 1,75 1,70 3 1,65 1,55

Mean Surface Roughness, (Ra

Standard Deviation S/N

1,430 1,725 1,600 1,585 0,14807 -4,03

Table 12. Surface roughness value obtained from the validation experiments Experiment Optimum Surface Number Levels Roughness (Ra), 10

A3B2C3D3

1,585

As a result of experiments, the factor-level combination (A2B3C3D1) and the average surface roughness value

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obtained from the validation experiments, the best performance characteristics for the desired state value was reached. 5.3. Predicting the Minimum Surface Roughness Using Optimum Parameters The minimum surface roughness using optimum parameters in Table 8 can be predicted by using following Equations (7) and (8). Minimum surface roughness predicted Mean () = A2 + B3 + C 3 + D1 -4x

Yi

(7)

Similarly, the maximum S/N ratio is computed to identify whether the minimum surface roughness can be accepted. Additionally, the maximum S/N ratio alters from the minimum=-10dB to

Predicted Maximum S/N Ratio C1

D3

3x

(8)

=-1,88-7,02-7,32-6,97+3x7,51 =-0,66 dB where S/N

Yi

[1] Topal, E.S., The role of the step over ratio in prediction of surface roughness in flat end milling, International Journal of Mechanical Sciences, vol. 51, 782 789, 2009.

[3] Feng, C.X. and Wang, X., Development of empirical models for surface roughness prediction in finish turning, Int. J. Adv. Manuf. Technol., vol. 20, 348 356, 2002. [4] Davim, P.J., Design of optimization cutting parameters for turning metal matrix composites based on the orthogonal arrays. Journal of Mater. Process. Technol., vol. 132, 340 344, 2003,

The S/N ratio can be predicted as;

B1

7. References

[2] Gologlu, C. and Sakarya, N., The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on taguchi method, J. Mater. Process. Technol. vol. 206, 7-15, 2008.

=-5,622-6,446-7,303-6,973-4x(-7,323)

A3

Chip depth has no important effect on surface roughness. Chip depth has the least effect on surface roughness. The tooth number of cutter has an effect on the surface roughness. However the effect comes after cutting and feed speeds (3,44 %).

is average value of surface roughness or

ratio

According to this prediction, it can be concluded that the

[5] Kopac, J. Bahor, M. and Sokovic, M., Optimal machining parameters for achieving the desired surface roughness in fine tuning of cold preformed steel workpieces, Int. J. Mach. Tools. Manuf., vol. 42, 707 716, 2002. [6] cutting parameters on surface roughness in hard turning using the Taguchi method, Measurement, vol. 44, 1697 1704, 2011.

in experimental

[7] Mandal, N, Doloi, B., Mondal, B. and Das, R., Optimization of flank wear using Zirkoni Toughened Alumina (ZTA) cutting tool: taguchi Method and Regression analysis, Measurement, vol. 44, 2149 2155, 2011.

The surface roughness is determined in milling operation using S/N ratio approach and Pareto ANOVA method. The important conclusions are given in the following:

[8] Benardos, P.G. and Vosniakos, G.C., Prediction of surface roughness in CNC face milling using neural networks and Taguchi and Computer Integrated Manufacturing, vol. 18 343 354, 2002.

calculated of Ra is the smallest value measurements.

6. Conclusions

Experiments showed that the cutting speed and the surface roughness are inversely proportional. The cutting speed is the primary factor affecting the surface roughness (67, 34 %). As cutting speed increases, surface roughness decreases. The high feed speed results in a high surface roughness. Surface quality decreases under high feed speeds. The feed speed is the second factor highly effecting the surface roughness (28,2 %). Mean surface roughness and chip depth are proportional.

[9] Benardos, P.G. and Vosniakos, G.C., Predicting surface roughness in machining: a review, International Journal of Machine Tools & Manufacture, vol. 43, 833 844. 2003. [10] Baek, D.K., Ko, T.J. and Kim, H.S., Optimization of feed rate in a face milling operation using a surface roughness model, International Journal of Machine Tools and Manufacture, vol. 41, 451 462, 2001. [11] Axinte, D.A. and Dewes, R.C., Surface integrity of hot work tool steel after high speed milling experimental data and empirical models, Journal of Materials Processing Technology, vol. 127, 325 335, 2002.

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[12] Franco, P., Estrems, M., and Fuara, F., Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools, International Journal of Machine Tools & Manufacture, vol. 44, 1555 1565, 2004. [13] Wang, M.Y. and Chang, H.Y., Experimental Study of Surface Roughness in slot end milling Al 2014-T6, International Journal of Machine Tools & Manufacture, vol. 44, 51 57, 2004. [14] Ozcelik, B., Oktem, H. and Kurtaran, H., Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm, International Journal Advanced Manufacturing Technologies, vol. 27, 234 241, 2005. [15] Peigne, G., Paris, H., Brissaud, D., Gouskov, A., Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness, International Journal of Machine Tools & Manufacture, vol. 44, 1133 1142, 2004. [16] Ozcelik, B. and Bayramoglu, M., The statistical modeling of surface roughness in high-speed flat end milling, International Journal of Machine Tools & Manufacture, vol. 46, 1395 1402, 2006. [17] Zhang, J.Z. Chen, J.C. and Kirby, E.D., Surface Roughness Optimization An End-Milling Operation Using The Taguchi Design Method, Journal of Materials Processing Technology, vol. 184, 223-239, 2007. [18] Cui, X., Zhao, J., Jia, C. and Zhou, Y., Surface roughness and chip formation in high-speed face milling AISI H13 steel, International Journal Advanced Manufacturing Technologies, vol. 61, 1-13, 2012. [19] Lou, M.S., Chen, C.J. and Li, C.M., Surface roughness prediction technique for CNC end-milling, Journal of Industrial Technology, vol. 15, 1-6, 1999. [20] Yang, J.L. and Chen, J.C., A Systematic approach for identifying optimum surface roughness performance in end-milling operations. Journal of Industrial Technology, vol.17, 1, 1-8, 2001. [21] Lin, T.R., Optimization technique for face milling stainless steel with multiple performance characteristics, Journal Advanced Manufacturing Technologies., vol. 19, 330 335, 2002. [22] Shetty, R., Pai, R.B., Rao, S.S and Nayak, R., composites, J. Braz. Soc. Mech. Sci. Eng., vol. 31, 1, 12 20, 2009. [23] optimization method for identifying optimum surface roughness in CNC face milling of Cobalt-based alloy (stellite 6), Int. J. Adv. Manuf. Technol., vol. 29, 940 947, 2006. [24] Dabade, A.U., Joshi, S.S and Ramakrishnan, N., Analysis of surface roughness and chip cross-sectional area while machining with self-propelled round inserts

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milling cutter. J. Mater. Process. Technol., vol. 132, 305 312, 2003. [25] Ghani, A.J. Choudhury, A.I. and Hassan, H.H., Application of Taguchi method in the optimization of end milling parameters, J. Mater. Process. Technol., vol. 145, 84 92, 2004. [26] Korkut, I. and Donertas, M.A. The Influence of feed rate and speed on the cutting forces, surface roughness and tool-chip contact length during face milling, Materials and Design, vol. 28, 308-312, 2007. [27] Khoei, A.R, Masters, I. and Gethin, D.T., Design optimization of aluminium recycling processes using Taguchi technique, Journal of Materials Processing Technology, vol. 127, 96 106, 2002. [28] Cutting Tools- Main Catalog, Sandvik Coromant, 2006. [29] Gadelmawle, E.S., Koura, M.M., Maksoud, T.M.A., Elewa, I.M. and Soliman, H.H., Roughness Parameters, Journal of Materials Processing. Technology, vol. 123, 133 145, 2002. [30] Karayel, D., Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Technol., vol. 209, 3125 3137, 2009. [31] Prakasvudhisarn, C., Kunnapapdeelert, S. and Yenradee, P., Optimal cutting condition determination for desired surface roughness in end milling. Int. J. Adv. Manuf. Technol., vol. 41, 440 451, 2009. [32] Yang, W.H. and Tarng, Y.S., Desing optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology, vol. 84, 122 129, 1998. [33] Oktem, H. Erzurumlu, T. and Col, M., A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces. Int. J. Adv. Manuf. Technol., vol. 28, 694 700, 2006. [34] Abou-El-Hossein K.A., Kadirgama, K. Hamdi M. and Benyounis K.Y., Prediction of cutting force in end-milling operation of modified AISI P20 tool steel, J. Mater. Process. Technol., vol. 182, 241 247, 2007.