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Optimization of machining parameters for turning super duplex stainless steel using taguchi technique.

INTRODUCTION

Super Duplex is an austenitic ferritic Iron Chromium-Nickel alloy with Molybdenum addition. It has good resistance to pitting, a high tensile strength and higher resistance to stress corrosion cracking at moderate temperatures to that of conventional austenitic stainless steels. Super Duplex is a material having an approximate equal amount of austenite and ferrite. These combine excellent corrosion resistance with high strength. Mechanical properties are approximately double those of singular austenitic steel and resistance to stress corrosion cracking is superior to type 316 stainless steel in chloride solutions. Super Duplex material has a ductile / brittle transition at approximately -50[degrees]C. High temperature use is usually restricted to a maximum temperature of 300[degrees]C for indefinite use due to embrittlement.

In a turning operation, it is very important to select the machining parameters for achieving high machining performance. In general, the desired machining parameters are determined based on experience or by use of a handbook. However, this does not ensure that the selected machining parameters have optimal or near optimal machining performance for a particular machine and environment. To select the machining parameters properly, several mathematical models based on statistical regression techniques or neural computing have been constructed to establish the relationship between the machining performance and the machining parameters.

Then, an objective function with constraints is formulated to solve the optimal machining parameters using optimization techniques. Therefore, considerable knowledge and experience are required for using this modern approach. Furthermore, a large number cutting experiments has to be performed and analyzed in order to build the mathematical models. Thus the required model buildings are very costly in terms of time and materials. In this paper, an alternate approach based on taguchi technique is used to determine the desired machining parameters more effectively.

Machining advanced engineering materials is usually associated with high machining costs and low productivity. This is due to the excessive generation of heat at the cutting zone and difficulties in heat dissipation due to relatively low heat conductivity of these materials. High material hardness and strength together with high temperatures at the cutting zone could result in excessive tool wear and thus short tool life and poor surface quality. Most components produced from advanced materials such as super duplex stainless steel and other nickel based alloys have high buy-to-fly ratio as most of them are made to be used in marine, undersea applications, heat exchangers, and engine and gas turbine industries. Difficult- to-machine materials are referred to the materials which during machining operations produce excessive tool wear, heat and/or cutting forces, difficulties in chip formation and/or poor surface quality. One of the important phenomena in machining difficult-to-machine materials is excessive generation of heat at the cutting zone resulting in very high cutting temperature.

Difficult-to-Machine Ferrous alloys Iron based difficult-to-machine materials have been classified into three categories, low carbon ductile steels, stainless steels and hardened steels. While in general the machining of steels is considered easy to moderate, some steel alloys like super duplex stainless steel-exhibit characteristics which make them difficult to machine. These steels are widely used in marine, chemical, aerospace, automotive and food processing industries. This is due to high strength, high fracture toughness, high fatigue and corrosion resistivity as compared to plain carbon steels. Low thermal conductivity together with high strength and high heat capacity has made stainless steel a difficult-to-machine material. High material strength requires high cutting energy resulting in high heat generation during machining. Similar to titanium and nickel alloys, in machining super duplex stainless steels the generated heat cannot effectively be transferred into the workpiece and chips due to low thermal conductivity of the material. Thus, the generated heat during machining operation is concentrated at the cutting zone and produce high cutting zone temperatures. High temperatures at the cutting zone increase the thermally induced tool wear such as diffusion and chemical reaction between the tool and workpiece materials. In addition, stainless steels tend to adhere to the cutting tools and form BUE. This increases the machining instability and thus chipping on the cutting edge. The work hardening capability of stainless steel together with its mentioned mechanical and thermal properties results in severe tool wear and low surface quality of the machined surface [38-43]. Generally the difficulties in machining stainless steel are attributed to low thermal conductivity, work hardening and poor chip breakability, which characterise the machining of stainless steels together with short tool life and poor surface quality, resulting in low productivity and high machining costs.

2. Literature Review:

The cutting speed, feed rate and depth of cut have the most influential effect on the surface roughness [1]. Increasing the feed rate will increase the surface roughness significantly and also the depth of cut using Taguchi method [2]. ]. Akkus et al. found that the feed rate is the significant factor which contributes to the surface roughness using ANOVA and regression [3].Chowdhury et al. noticed that the rate of growth of flank wear increases irrespective of feed with the increase of speed under minimum quality lubrication and dry condition respectively [4]. According to Grzesik and Wanat the results show that keeping equivalent feed rates, 0.1 mm/rev for conventional and 0.2 mm/rev for wiper inserts, the obtained surfaces have similar roughness parameters and comparable values of Skewand Kurtosis [5]. With wiper inserts and high feed rate is possible to obtain machined surfaces with <0.8 [micro]m of Racompared to conventional insets which present high values of surface roughness [6]. Kushnaw et.al observed that the main factor affecting the inclination angle is the diameter of periphery and machined diameters which are depend upon change of depth of cut, the cutting condition [7]. Flank wear, nose wear, crater wear, notch wear, edge chippings or combination of these is the performance measure of tool but out of these flank wear has significant effect on tool wear. The required response is minimum flank wear selected for better tool performance. It is very necessary to predict tool wear during hard turning to determine the optimum cutting conditions. 3

3. Design of Experiment:

In this paper, three machining parameters were selected as control factors, and each parameter was designed to have three levels, denoted 1, 2 and 3(Table 1). The experimental design was according to a L18 array based on taguchi method, while using the Taguchi orthogonal array would markedly reduce the number of experiments. A set of experiments designed using the Taguchi method was conducted to investigate the relation between the process parameters and response factor. Minitab 15 software is used to optimization and graphical analysis of obtained data

3.1 Experimental Details:

The work material used as test specimen was super duplex stainless steel SAF 2507. A cylindrical bar of test specimen 55mm diameter and 450mm length were used for the turning tests. Initially, it is plain turned in a rigid and powerful HMT Lathe by using uncoated carbide insert at industrial speed feed combinations under dry conditions. Uncoated Cemented carbide cutting tool inserts(CNMG 120408-QM, grade H13A) were used for turning tests. The inserts were rigidly attached to a tool holder (PCLNR25M12). The combination of the insert and the tool holder provided approach angle = 95[degrees], rake angle = - 6[degrees], angle of inclination = -6[degrees] and 0[degrees] of clearance angle on the major cutting edge. The chemical composition of super duplex stainless steel SAF2507 can be seen in the tables 2. The turning tests were carried out to determine the surface roughness under various turning parameters. In order to obtain a good surface finish, the cutting oil is used during turning. The cutting oil named MAK Sherol B is mixed with water in the ratio 1:8. The cutting oil provides the function of coolant as well as lubricant during machining. The ranges of cutting velocity, feed rate and depth of cut were selected based on the tool maker's recommendations and industrial practices. The uncoated inserts were replaced after each trial and the tool wear is measured using tool makers microscope. At the end of full cut, the cutting inserts were also inspected under scanning electron microscope (JOEL Model 6390).

RESULTS AND DISCUSSION

4.1 Experimental results and Taguchi analysis:

Generally, in turning operation, the surface roughness & tool wear are the important criteria. The purpose of analysis of variance (ANOVA) is to investigate which machining parameter significantly affects the surface roughness& tool wear. Based on ANOVA, the relative importance of the machining parameters with respect to surface roughness and tool wear was investigated to determine the optimum combination of machining parameters.

A series of turning tests are conducted to assess the effect of turning parameters on surface roughness and tool wear in turning of super duplex stainless steel. Experimental results of the surface roughness and tool wear for turning of super duplex stainless steel SAF 2507 with different turning parameters are shown in Table 3. Table 3 also gives S/N ratio for surface roughness and tool wear. The S/N ratio for each experiment of L'18 was calculated using smaller the better approach. The objective of using the S/N ratio as a performance measurement is developing products and process insensitive to noise factor.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

From the ANOVA table it is clear that maximum contribution factor is Cutting speed having percentage contribution up to 47%. After that second main contribution is of depth of cut, which is up to 32%.. Hence the individual ranking of these three parameters on the average value of means of Surface roughness.

5. Predictive Equation and Verification:

The predicted values of surface roughness at the optimum levels is calculated by using the relation

[??] = nm + [0.summation over (i=1)] (nim - nm)

Where [??] is the predicted value of the surface roughness after optimization, nm is the total mean value of surface roughness for every parameter, nim is the mean surface roughness at optimum level of each parameter and o is the number of main machining parameters that affects the response parameter. By applying this relation, the predicted value of surface roughness at optimum conditions is obtained:

[??] Ra = 1.393 + [(1.182-1.393) + (1.292-1.393) + (1.284-1.393)]

[??] Ra = 0.972 [micro]m.

The effectiveness of this parameter optimization is verified experimentally. This requires the confirmation run at the predicted optimum conditions. The experiment is conducted at the predicted optimum conditions and the average of the response is comes out to be 1.01 [micro]m. The error in the predicted and experimental value is only 3.7 %, so good agreement between the experimental and predicted value of response is obtained. Because the percentage error is less than 5%, it confirms that the results have excellent reproducibility. The results show that using the optimal parameter setting (V3F1D1) the lower surface roughness is achieved. Table 6 shown below shows that optimal values of surface roughness lie between the optimal ranges.

6. Results:

The effect of three machining parameters i.e. the cutting speed, feed and depth of cut and their interactions are evaluated using ANOVA and with the help of MINITAB 15 statistical software. The purpose of ANOVA in this paper is to identify the important turning parameters in prediction of surface roughness. Some important results come from ANOVA and plots are given above.

6.1 Surface Roughness:

It has been found that cutting speed is the most significant factor for surface roughness and its contribution is 47%. The best results for surface roughness would be achieved when Super duplex stainless steel SAF 2507 is machined at Cutting speed of 120m/min, feed rate of 0.06 mm/rev and depth of cut of 0.5 mm. with confidence interval, cutting force affects the surface roughness most significantly.

7. SEM analysis:

The turning operation is carried out at the optimization values of machining parameters i.e. V3 F1 D1. The experimental value of surface roughness is compared with the predicted values of taguchi method. The error in the predicted and experimental value is only 3.7 %, so good agreement between the experimental and predicted value of response is obtained. Because the percentage error is less than 5%, it confirms that the results have excellent reproducibility.

The chips produced when turning at optimized value were analyzed by scanning electron microscope (JOEL Model 6390). The sharper edges of the chip produced were considerably reduced and refined chips were produced when machined at the optimized value. The chips slightly affect the tool work interface while comparing with the other trials. The SEM images of the chips produced when turned at the optimized values in various magnification levels were given below.

[FIGURE 4 OMITTED]

Conclusion:

The current study was done to study the effect of machining parameters on the surface roughness. The following conclusions are drawn from the study:

1. The Surface roughness is mainly affected by the cutting speed. With the increase in cutting speed, the surface roughness decreases considerably.

2. From ANOVA analysis, parameter making significant effect on surface roughness is the cutting speed. Secondly, the depth of cut has the significant effect on the value of surface roughness. At last, the feed rate has the less significant effect on the surface roughness.

3. The parameters taken in the experiments are optimized to obtain the minimum surface roughness possible. The optimum setting of cutting parameters for high quality turned parts is as:

i) Cutting speed i.e. 120 m/min.

ii) Feed rate i.e. 0.06 mm/rev.

iii) Depth of cut should be 0.5 mm.

Scope for future work:

(1) The same experiment can be conducted in other methods of cooling like MQL, Cryogenic cooling etc. so that we can expect more surface finish in such case.

(2) For the same single pass during turning, three methods namely dry machining, wet or MQL machining and cryogenic cooling can be applied and the performance can be compared with the existing work.

(3) Similar to surface roughness, other outputs like tool wear, cutting zone temperature, force acted, vibration etc can be analyzed for the experiment.

(4) With the same work material i.e. super duplex stainless steel, apart from turning the other operations like drilling, milling can be done.

REFERENCES

[1.] Abhang, L.B., M. Hameedullah, 2011. Modeling and Analysis for Surface roughness in Machining EN-31 steel using Response Surface Methodology, Journal of Applied Research, 1: 33-38.

[2.] Akkus, Harun, 2011. Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method, Response, 44: 1697-1704.

[3.] Akkus, Harun., Asilturk Ilhan., 2011. Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models, Scientific Research and Essays, 6(13): 2729-2736.

[4.] Chowdhury Nusrat T., N.R. Dhar, 2011. Experimental Analysis and Modeling of Tool Wear and Surface Roughness in Hard Turning under Minimum Quantity Lubricant Environment, Operations Management, 68: 326-330.

[5.] Grzesik, W., T. Wanat, 2006. Surface finish generated in hard turning of quenched alloy steel parts using conventional and wiper ceramic inserts, Int. J. Mach. Tools Manuf., 46(15): 1988-1995.

[6.] Correia A Esteves., Davim J Paulo., 2011. Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts, Measurement, 44(5): 1000-1005.

[7.] Kushnaw. Farhad Mohammad., Khleif Ali Abbar., 2011. Theoretical and Experimental Investigation of Tool Inclination Angle in Turning Operation, Materials Engineering, 29(1): 116-128.

[8.] Gopalsamy, B.M., B. Mondal and S. Ghosh, 2009. "Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel", Journal of Scientific and Industrial Research, 68: 686-695.

[9.] Selvaraj, D. Philip and P. Chandarmohan, 2010. "optimization of surface roughness of AISI 304 austenitic stainless steel in dry turning operation using Taguchi design method", Journal of Engineering Science and Technology, 5(3): 293-301.

[10.] Upinder Kumar Yadav et al, 2012. "Experimental Investigation And Optimization Of Machining Parameters For Surface Roughness In CNC Turning By Taguchi Method", / International Journal of Engineering Research and Applications (IJERA), 2(4): 2060-2065.

[11.] Thamizhmanii, S., S. Saparudin and S. Hasan, 2007. "Analysis of Surface Roughness by Using Taguchi Method", Achievements in Materials and Manufacturing Engineering, 20(1-2): 503- 505.

[12.] Villalobos. Albiter. D and Maldonado, 2009. "Microstructural changes in SAF 2507 super duplex stainless steel produced by thermal cycle", Revista Materia, 14(3): 1061-1069.

[13.] Thirumalai, R and J.S. Senthilkumaar, 2011. "Intelligent selection of optimum machining parameters in turning of Inconel 718", International journal of advanced engineering technology, pp: 167-173.

[14.] Senthilkumaar, J.S., R. Saravanan and P. Asokan, 2003. 'Simultaneous optimization for machinability assessment of Inconel 718', Journal. Manf.Tech. Today, 7(2): 3-7.

[15.] Davim, J.P., 2003. Design of optimisation of cutting parameters for turning metal matrix composites based on the orthogonal arrays. J. Mat. Process. Tech., 132: 340-344.

[16.] Ghani, J.A., I.A. Choudhury and H.H. Hassan, 2004. Application of Taguchi method in the optimization of end milling parameters," Journal of Materials Processing Technology, 145: 84- 92.

[17.] Puertas Arbizu, I. and C.J. Luis Perez, 2003. Surface roughness prediction by factorial design of experiments in turning processes. J. Mater. Process. Tech., pp: 143-144, pp: 390-396.

[18.] Ross, P.J., 1996. Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design. 2nd edition, New York, McGraw-Hill.

[19.] Thomas, M.., Y. Beauchamp, Y.A. Youssef and J. Masounave, 1997. An experimental design for surface roughness and built-up edge formation in lathe dry turning. Int. J. Qual. Sci., 2(3): 167-180.

[20.] Aslan, E., N. Camuscu and B. Birgoren, 2007. Design of optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+TiCN mixed ceramic tool," Materials and Design, 28: 1618-1622.

[21.] Hascalik, A. and U. Caydas, 2007. Optimization of turning parameters for surface roughness and tool life based on the Taguchi method. Int. J. Adv. Manuf. Tech, pp: 1147-1154.

(1) A. Srinivasan, (2) K. Senthil Kumar, (3) J.S. Senthilkumaar, (4) D.R. Joshua

(1) Department of Mechanical Engineering, AVS Engineering College, Ammapet, Salem, Tamilnadu, India.

(3) Department of Mechanical Engineering, Nehru College of Engineering, Thiruvilwamala, Thrissur, India.

(2) Department of Mechanical Engineering, Podhigai College of Engineering and technology, Vellore, Tamilnadu, India.

(4) Department of Mechanical Engineering, AVS Engineering College, Ammapet, Salem, Tamilnadu, India.

Received 25 April 2016; Accepted 28 May 2016; Available 5 June 2016

Address For Correspondence:

A. Srinivasan, Department of Mechanical Engineering, AVS Engineering College, Ammapet, Salem, Tamilnadu, India E-mail id: srivankumar@gmail.com
Table 1: Factors (parameters) and levels for Design of experiments:

Parameters        Unit     Levels

                           -1       0        1

Cutting Speed     m/min    80       100      120
Feed              m/min    0.06     0.08     0.1
Depth of Cut      mm       0.5      0.75     1.0

Table 2: the chemical composition of work material in percentage by
weight

Carbon (max)              0.03
Manganese(max)            1.0
Silicon(max)              0.80
Chromium                  24.00/26.00
Molybdenum                3.0/5.0
Nickel                    6.0/8.0
Copper (max)              0.50
Nitrogen                  0.24/0.32
Iron                      Balance
Sulphur (max)             0.02
Phosphorous (max)         0.035

Table 3: Experimental results and corresponding S/N ratio:

Trial     V         F          D (mm)   Ra           S/N
          (m/min)   (mm/rev)            ([micro]m)   ratio

1         80        0.06       0.5      1.35         -2.61
2         80        0.08       0.75     1.63         -4.24
3         80        0.1        1.0      1.91         -5.62
4         100       0.06       0.5      1.16         -1.29
5         100       0.08       0.75     1.47         -3.35
6         100       0.1        1.0      1.61         -4.14
7         120       0.06       0.75     1.17         -1.36
8         120       0.08       1.0      1.37         -2.73
9         120       0.1        0.5      1.03         -0.26
10        80        0.06       0.75     1.43         -3.11
11        80        0.08       0.5      1.52         -3.64
12        80        0.1        0.5      1.77         -4.96
13        100       0.06       0.75     1.33         -2.48
14        100       0.08       1.0      1.62         -4.19
15        100       0.1        0.5      1.18         -1.44
16        120       0.06       1.0      1.31         -2.35
17        120       0.08       0.5      0.98         0.18
18        120       0.1        0.75     1.23         -1.80

Table 4: ANOVA table for Surface Roughness

Variable    DF   SS      MS      F        P       contribution
Factors

V           2    0.529   0.254   286.35   0.000   46.98
F           2    0.093   0.037   42.08    0.006   8.26
D           2    0.355   0.098   110.94   0.002   31.53
V x f       4    0.071   0.014   15.97    0.023   6.30
V x D       4    0.027   0.007   7.65     0.063   2.39
F x D       4    0.048   0.011   11.48    0.042   4.26
Error       3    0.003   0.001                    0.27
Total       21   1.126

Where:

v--Cutting speed; f--feed; d--depth of cut

DF--Degree of Freedom

SS--Sum of squares

MS--Mean of squares

F--a statistical parameter

P--Percentage

Table 5: Mean value of process parameters for surface roughness

Level      V          F          D

1          1.602      1.292      1.284
2          1.395      1.432      1.331
3          1.182      1.455      1.564
Rank       1          3          2

Table 6: Optimal values of machining and response parameters

CP        OV        OL        POV       EOV       OR

V         120       V3        0.972     1.01      0.972
F         0.06      F1                            <Ra>
D         0.5       D1                            1.01

Where, CP-Cutting Parameters

OV-Optimal Values of Parameters

OL-Optimum Levels of Parameters

POV-Predicted Optimum value

EOV-Experimental Optimum Value

OR-Optimum Range of Surface Roughness
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Author:Srinivasan, A.; Kumar, K. Senthil; Senthilkumaar, J.S.; Joshua, D.R.
Publication:Advances in Natural and Applied Sciences
Date:May 30, 2016
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