Analysis of Hard Machining of Titanium Alloy by Taguchi Method

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ScienceDirect Materials Today: Proceedings 4 (2017) 10729–10738

www.materialstoday.com/proceedings

AMMMT 2016

Analysis of Hard Machining of Titanium Alloy by Taguchi Method S.M.Ravi Kumara,Suneel Kumar Kulkarnib a

Don Bosco Institute of Technology,Mysore road Bangaluru 560060,India b B.T.L.Institute of Technology Hosur Road Bangaluru 560099 India

Abstract Hard Titanium alloys have good strength to weight ratio and resistance against corrosion. Machining such hard alloys causes lot of tool wear. Hard machining is a dry machining process using a single point cutting tool for machining hardened material whose Vickers hardness number is above 45.The main advantage of hard turning is the possibility of eliminating grinding operations and it is environment friendly. The surface integrity of the work piece has to be maintained to give longer life to the machined component. The investigation was done turning titanium alloy in a CNC machine using the L9 orthogonal array as plan of experiments. The objective is to optimize the cutting parameters which is done using Taguchi method. Based on the response table obtained by DOE the optimum surface roughness and tool wear was obtained. The tool wear at different operating conditions were found using confocal microscope and the surface roughness was determined using Form Talysurf. © 2017 Published by Elsevier Ltd. Selection and Peer-review under responsibility of Advanced Materials, Manufacturing, Management and Thermal Science (AMMMT 2016). Keywords: Hard turning;Taguchi’s method,;Surface Roughness;Tool wear:S/N ratio;Titanium alloy.

1. Introduction Titanium and its alloys are widely used in aerospace industries and petrochemical industries due to their high strength to weight ratio and corrosion resistance (1-2).Machining of hard materials is generally a challenge because machining is not only subtracting the metal but also taking account of surface integrity and economics of machining which depends on the ease with which the work material is machined and the tool life of cutting tool. The machineability decreases with the increase of hardness of work material as the cutting force will be more and the tool wear is increased which in turn affects the surface generated. The surface finish is important not only for the aesthetics but also for the machined surface characteristics such as surface roughness and surface damage. With regards to surface integrity the surface roughness is the important parameter because it influences surface sensitive properties like fatigue strength and service life of the component. Rough surface wears quickly and the surface 2214-7853 © 2017 Published by Elsevier Ltd. Selection and Peer-review under responsibility of Advanced Materials, Manufacturing, Management and Thermal Science (AMMMT 2016).

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irregularities are the place from where cracks and corrosion originate. The machining cost is more to get smoother surface finish. Therefore there is a need to obtain a optimum surface finish. Tool wear increases the cost of machining. The manufacturing industries are on a constant drive to decrease the machining cost and improve the machined surface due to the huge demand of such manufactured goods. Hard machining is a dry machining process using a single point cutting tool for machining hardened material whose Vickers hardness number is above 45. The main advantage of hard turning is the possibility of eliminating grinding operations. The high temperature occurring in the process requires cutting tool to withstand elevated temperature. In hard turning there is no need to use cooling lubricant thus the process is environment friendly. Hard turning gives the flexibility of using single point cutting tool as compared to multipoint cutting tool grinding. More complex shaped contours machining is possible in hard turning. The cycle time of grinding and the energy per unit volume of metal removal is very much higher than hard turning. The general procedure of turning hard materials is to rough cut in a lathe machine followed by heat treatment and grinding for finish machining. Hard turning is much economical process compared to conventional grinding and can be alternative to it. [3].Higher productivity, reduced setup, complex part machining ability and surface finish closer to grinding are the advantages of hard turning. Hard turning requires rigid machine tools and extremely hard cutting insert which can withstand high temperatures and having good wear resistance. The metal cutting industry strives for increasing the productivity, quality of finished part and the aesthetics aspects. Process monitoring has gained lot of importance in machining processes. Surface roughness is one of the prominent measures of quality of machining. Better surface finish is the indicator of better quality. Cutting speed, Feed and Depth of cut are the parameters which can be adjusted during machining processes. Many operators choose the operating parameters by intuition and experience. The scientific method of choosing the correct operating parameters is by conducting trial tests and deriving mathematical models based on it. The economics of hard turning should be justified to counter the standard finishing process like grinding which is a standard method for critical hardened surfaces. Hard turning eliminates the need of finish machining by grinding thus the surface finish quality should be as good as that of grinding. In grinding the surface finish depends on the size, shape, hardness and distribution of abrasive grains of the abrasive wheel but in hard turning the surface generated is dependent on cutting tool geometry mainly the nose radius and the feed rate of the cutting tool. Hard turning has become possible only after the advent of new technology and arrival of newer cutting tool materials. These are the ceramic, CBN, Cemented carbide and coated carbide cutting tool inserts. The experimental study is conducted to derive the statistical mathematical model of operating conditions (cutting speed, feed, depth of cut) so that the output of better surface roughness is obtained and also the tool wear is less. The chipping of tool edge and its failure due to the high temperature generated during machining and the resulting effect on the chips was found by Umbrello [4]. Abhay Bhatt et al [5] derived the statistical and mathematical model to improve machining efficiency by optimization techniques. They experimented the turning of material Inconel 718 and studied the mechanism related to wear. Ilhan Asilturk et al [6] used statistical method of Taguchi techniques L9 orthogonal array and ANOVA applying signal to noise ratio for optimizing turning operating conditions to minimize surface roughness measured by Ra and Rz. The dry machining was performed on AISI 4140 work material with carbide insert cutting tools. The effect of cutting parameters like speed, feed and depth of cut on the output parameter of surface roughness was found and analyzed. L B Abhang et al.[7] used work material EN 31 steel alloy with tungsten carbide cutting tool insert for conducting experiments to optimize the combination of machining parameters to minimize surface roughness. Taguchi’s L9 plan of experiments was used to find the effects of input cutting conditions of feed rate, depth and lubricant temperature on the output of surface roughness. 2. Experimental Details 2.1. Machining Test Turning tests were performed on MODEL XL Turn CNC Slant Bed Lathe. The machine resolution is of 0.01 mm.

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2.2. Work piece Material Turning trials were carried out on a cylindrical bar of 60 mm length and 16 mm diameter of Ti-6Al-4V titanium alloy. The alloy is heat treated to give sufficient hardness. This material is commercially available in the market. 2.3. Cutting tool Carbide inserts of designation CNGG 120408-SGF-H13A were used in the machining experiments. The cutting insert was uncoated and rhombic in shape. The nose radii of the inserts were 0.2, 0.4 and 0.8 mm. 2.4. Design Matrix In the present work there are three levels and four parameters. According to Taguchi approach L9 orthogonal array has been selected. So, according to Taguchi L9 array design matrix of variables is formed as shown in Table 1. Table 1. Design matrix of variables

S.N 1 2 3 4 5 6 7 8 9

Nose radius (mm) 0.2 0.2 0.2 0.4 0.4 0.4 0.8 0.8 0.8

Speed m/min 250 410 510 250 410 510 250 410 510

Feed (mm) 0.25 0.5 0.75 0.25 0.5 0.75 0.25 0.5 0.75

Depth of Cut (mm) 0.25 0.5 0.75 0.25 0.5 0.75 0.25 0.5 0.75

2.5. Tool wear Measurement The wear measurement on the flank side of tool was done by using Confocal microscope (Make: Olympus LEXT 4000, Japan) offline off line at CMTI Bangalore. Confocal microscopy is used to obtain excellent 3D images. The high optical resolution, contrast using point illumination helps in obtaining images in the form of 3D solids. This technique is used for many high end applications of scientific and industrial nature. With the Olympus LEXT 4000 laser scanning digital microscope the measurement of 10 nanometre resolution is possible. It is a superior metrology having distinctive features of noncontact type to produce microscopic image and instant image acquisition. 2.6. Surface Roughness Measurement The Surface roughness measurement was done offline at CMTI Bangalore by using Hobson taylor Tester instrument Form Talysurf equipped with image processing software. Surface roughness is the average deviations from the mean line. 2.7. Methodology Taguchi is a statistical method used to improve the product quality [8]. The two major tools used in Taguchi method are the signal to noise ratio and orthogonal array. The number of experiments is significantly reduced and the experimental work is expedited by the use of Taguchi’s method [9].The optimal setting is the parameter combination, which has the highest S/N ratio. Taguchi method is used to determine the control factors i.e. the operating parameters. 2.8. Signal to noise (S/N) ratio Taguchi method is used to get solution by plan of experiments. Signal to noise ratio is used to reduce the variations in the process.

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Results and Discussion The roughness profile of the machined surface is obtained by using Hobson Taylor Tester instrument Form Talysurf equipped with image processing software is shown in fig.1.The average surface roughness Ra value is considered for measurement of surface roughness. As described in ASME B46.1,

Fig.1. Roughness profile of the machined surface

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Fig.2. Wear measurement of cutting tool with Confocal microscope equipped with image analyzer. The tool wear on its flank is measured length wise and breadth wise using Confocal microscope as shown in fig. 2. The optical microscopic image of the worn tool surface is captured for crystal clear 3D surface image. The superior metrology using LEXT image processing software enables to obtain accurate tool wear dimensions.

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3.1. Experimental results The results obtained are tabulated as shown in Table 2. after machining as per the plan of experiments. The surface roughness and the flank wear along length and width was obtained offline. Table 2. Experimental results for cutting tool wear and surface roughness Trial No 1

Nose Radius (mm) 0.2

Speed (rpm)

Feed (m/min)

250

15

Depth of Cut (mm) 0.25

Flank wear length(µm)

Flank wear

462.320

137.344

Surface roughness Ra(µm) 0.9161

width(µm)

2

0.2

410

20

0.5

422.758

162.344

0.8236

3

0.2

510

25

0.75

440.432

142.344

0.9126

4

0.4

250

15

0.25

371.055

169.011

0.7696

5

0.4

410

20

0.5

327.628

114.011

0.7941

6

0.4

510

25

0.75

344.584

100.677

0.9518

7

0.8

250

15

0.25

306.193

182.344

1.7911

8

0.8

410

20

0.5

316.131

215.677

0.7195

9

0.8

510

25

0.75

334.850

239.011

0.6487

3.2. Response table The response table for Tool wears length, Tool wears breadth, Surface roughness are shown in Table 3.The response table consists of a row for the average signal to noise ratio of each factor level. Table 3. Signal to Noise ratio for surface roughness, wear width and wear length S/N Ratio for Ra S/N Ratio wear width S/N Ratio wear width 0.76114

-42.7562

-53.2989

1.68567

-44.2087

-52.5218

0.79439

-43.0668

-52.8776

2.27470

-44.5583

-51.3888

2.00250

-41.1389

-50.3076

0.42909

-40.0586

-50.7459

-5.06240

-45.2653

-49.7199

2.85938

-46.6761

-49.9973

3.75912

-47.5684

-50.4970

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Main Effects Plot (data means) for SN ratios for Ra Nose radius(mm)

3.0

Speed(rpm)

2.5

Mean of SN ratios

2.0 1.5 1.0 0.2

0.4

0.8

250

Feed(mm/rev)

3.0

410

510

Depth of cut(mm0

2.5 2.0 1.5 1.0 15

20

25

0.25

0.50

0.75

Signal-to-noise: Smaller is better

Fig. 3. Main effect plots for Surface roughness Ra Table 4. Response Table for Signal to Noise Ratios (Smaller is better) for Surface roughness Level

Nose radius (mm)

1 2 3 Delta Rank

1.093 1.495 2.894 1.800 1

Speed (rpm) 1.616 2.195 1.670 0.579 4

Feed (mm/rev) 1.350 2.521 1.611 1.170 2

Depth of Cut (mm) 2.183 1.397 1.902 0.787 3

Table 4 shows Taguchi Analysis Surface roughness (Ra) micron versus Nose Radius (mm), Speed (rpm), Feed (m/min), and Depth of Cut (mm) and the predicted optimal values for surface roughness is shown in table 5. Table 5 Predicted optimal values and setting of process parameters for Surface roughness Optimal Nose Optimum Optimum Optimum Optimum Predicted Optimum Radius (mm) Speed Feed Depth of S/N ratio Wear width (rpm) (mm/rev) Cut (mm) (microns) 0.8

410

20

0.5

3.52420

0.6562

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Main Effects Plot (data means) for SN ratios for wear width Nose rad(mm)

Speed(rpm)

-42 -43

Mean of SN ratios

-44 -45 -46 ..2

.4 Feed(mm/rev)

.8

250

410 DOC(mm)

510

0.15

0.20

0.25

0.25

0.50

0.75

-42 -43 -44 -45 -46

Signal-to-noise: Smaller is better

Fig. 4. Main effect plots for Wear width. Table 6. Response Table for Signal to Noise Ratios (Smaller is better) for wear width Level

Nose Radius (mm)

1 2 3 Delta Rank

-43.34 -41.92 -46.50 4.58 1

Speed (rpm)

Feed (m/min)

44.19 -44.01 -43.56 0.63 4

-43.16 -45.45 -43.16 2.29 2

Depth of Cut (mm) -43.82 -43.18 -44.77 1.59 3

Table 6 shows Taguchi Analysis: Wear width (microns) versus Nose Radius (mm), Speed (rpm) ,Feed (mm/rev), Depth of Cut (mm). Table 7. Predicted optimal values and setting of process parameters for wear width Optimal Nose Radius (mm) 0.4

Optimum Speed (rpm) 510

Optimum Feed (mm/rev) 20

Optimum Depth of Cut (mm) 0.5

Optimum S/N ratio -48.9576

Table 7. shows the predicted optimal values and setting of process parameters for wear width.

Predicted Optimum Wear width (microns) 254.566

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Main Effects Plot (data means) for SN ratios for wear length Nose rad(mm)

-50

Speed(rpm)

Mean of SN ratios

-51 -52 -53 ..2

.4

.8

250

410

Feed(mm/rev)

-50

510

DOC(mm)

-51 -52 -53 0.15

0.20

0.25

0.25

0.50

0.75

Signal-to-noise: Smaller is better Fig. 5. Main effect plots for Wear length. Table 8. Response Table for Signal to Noise Ratios (Smaller is better) for wear length Level 1

Nose Radius (mm) -52.90

Speed (rpm) -51.47

Feed (m/min) -51.35

Depth of Cut (mm) -43.82

2

-50.81

-50.94

-45.45

-43.18

3

-50.07

-51.37

-43.16

-44.77

Delta

2.83

0.53

0.50

0.43

Rank

1

2

3

4

Table 8 shows Taguchi Analysis: Wear length (microns) versus Nose Radius (mm), Speed (rpm), Feed (mm/rev), Depth of Cut (mm). The main effect plots for surface roughness Ra is shown in fig.3 and the main effect plots for width and length are shown in fig.4 and fig.5 respectively. Table 9 shows predicted optimal values and setting of process parameters.

`

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Table 9. Predicted optimal values and setting of process parameters. Optimal Nose Radius (mm)

Optimum Speed (rpm)

Optimum Feed (mm/rev)

Optimum Depth of Cut (mm)

Optimum S/N ratio

0.8

410

25

0.5

-49.1930

Predicted Optimum Wear width (microns) 281.843

4. Conclusions The following can be concluded from the present study: • Nose radius of cutting tool insert is the most significant factor affecting surface roughness of the work material followed by feed rate, depth of cut and cutting speed. • Wear width of cutting tool response parameter is also most significantly affected by nose radius followed by feed rate, of cut and cutting speed. • Wear length of the cutting tool insert is most significantly affected by nose radius followed by cutting speed, feed rate and depth of cut. • Considering the performance characteristics the greater S/N ratio corresponds to better performance the greatest S/N ratio is the optimal value of machining parameters. • Using Taguchi’s method the required number of trials to arrive at the optimum cutting parameters is reduced. References [1] R.R.Boyer,Thermec 2003,InternationalConference on Processing and Manufacturing of Advance d materials ( Zurich : Trans Tech Publications,2003) [2] J.C. Fanning, Titanium 99 Science and Technology (St. Petersburg, Russia: CRISM, Promety, 2000). [3] Tonshoff HK,Wobker HG,Brandt D. Tool wear and surface integrity in hard turning, Production Engineering, 3(1),1996,19-24 [4] D. Umbrello, “Finite Element Simulation of Convention-al and High Speed Machining of Ti6Al4V Alloy,” Journal of Materials Processing Technology, Vol. 196, No. 1-3, January 2008, pp. 79-87. [5] Abhay Bhatt, HelmiAttia , R.Vargas , V.Thomson, “Wear mechanisms of WC coated and uncoated tools in finish turning of Inconel 718”, Tribology International 43 (2010) 1113–1121. [6] Ilhan Asilturk, Harun Akkus," Determining the effect of cutting parameters on surface roughness in hard turning using Taguchi method", Measurement. 44 (2011) pp 1697–1704. [7] L B Abhang, M Hamidullah,"Optimization of machining parameters in steel turning operation by Taguchi method", International Conference on Modeling, Optimization and Computing, Procedia Engineering. 38 (2012). pp. 40–48. [8] Ross P.J. Taguchi, “Techniques for quality Engineering “, USA: McGraw-Hill; 1996. [9] Taguchi G, Introduction to Quality Engineering (Asian Productivity, Organization Tokyo, 1990).