An Optimization of Machinability of Aluminium Alloy 7075 and Cutting Tool Parameters by Using Taguchi Technique

7/30/2019 An Optimization of Machinability of Aluminium Alloy 7075 and Cutting Tool Parameters by Using Taguchi Technique

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 6340(Print), ISSN 0976 6359(Online) Volume 3, Issue 2, May-August (2012), IAEME

480

AN OPTIMIZATION OF MACHINABILITY OF ALUMINIUM ALLOY

7075 AND CUTTING TOOL PARAMETERS BY USING TAGUCHI

TECHNIQUEN.B.Doddapattar, N Lakshmana swamy

Research scholar, Department of Mechanical engineering, UVCE, Bangalore University, Blore

Professor, Department of Mechanical engineering, UVCE, Bangalore University, Blore

ABSTRACT

The manufacturing cost can be minimized by reducing the machining cost through optimization

of machining environment by optimizing the machining parameters like cutting speed, feed and

depth of cut, etc, and proper setting of various parameter during machining since machining

operation is one of the major cost centers for manufacturing the product, the production cost can

also be reduced by reducing the lead time and proper selection of machine tools, cutting tools

material, tool geometry and cutting parameters. These variables govern the economics of

machining operations. Therefore, the attempt has been made to carry out an experimental

investigation by using Taguchi technique mainly to find and correlate the technological factors to

the economics of machining process. The Taguchi method is systematic application of design

and analysis for experiments. It is an effective approach to produce high quality products at

relatively low cost. For turning operation, tool life is higher the better performance characteristic,

however the cutting force& surface roughness are the lower the better performancecharacteristics. As a result improvement of one parameter lead to degradation of other parameter,

hence optimization of multiple parameters is much more complicated, hence Taguchi method is

used to investigate the multiple performance characteristic in the turning operation.

Keywords: Machinability, Taguchi technique, cutting parameters, machining environment

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING ANDTECHNOLOGY (IJMET)

ISSN 0976 6340 (Print)

ISSN 0976 6359 (Online)

Volume 3, Issue 2, May-August (2012), pp. 480-493

IAEME: www.iaeme.com/ijmet.htmlJournal Impact Factor (2012): 3.8071 (Calculated by GISI)www.jifactor.com

IJMET

I A E M E


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1) INTRODUCTIONMany types of tool materials, ranging from high carbon steel to ceramics and diamonds are used

as cutting tools in todays metal working industry. It is important to be aware that differences do

exist among tool materials. The various tool manufacturers assign many names and numbers totheir products while many of these names and numbers may appears to be similar, the

applications of these tool materials may be entirely different.

Traditionally, the machinability of materials involve tool life, cutting forces, productivity or chip

formation, with less attention paid to particle emission. In this work, the authors address the

machinability of aluminium alloys from several points of view, including cutting forces, chip

formation and segmentation and metallic particle emission.

The following section addresses machinability on metallic particle emission during the

machining of aluminium and the effect of materials, cutting conditions and lubrication mode.

On the other hand, in metal cutting process, the desired metal cutting parameters are determined

either by experience or by using a hand book which does not ensure the selected parameters to be

optimal. To determine the optimal cutting conditions, reliable mathematical models have to be

formulated to associate the cutting parameters with cutting performance in terms of statistical

approach. The response surface methodology has been used by some researchers for the analysis

and predictions of tool life or surface roughness[1] & [2 & 3]. Moreover, some works on

machining of carbon or alloy steel have given to a full or fractional factorial design [4 to 6]

2) PROBLEM DEFINITIONParticularly in the field of transport engineering massive application of lightweight materials

represents the order of the day. The goal of saving fuel and other energy forms can mainly be

achieved through the reduction of vehicle weights. Apart from various synthetic materials, the

classical light metal aluminium offers the best pre-requisite for reaching this objective. In other

application fields too, its numerous favorable properties make aluminium an appreciated

construction material for engineers.

Innovative machining strategies are characterized by maximum cutting speeds and feed rates in

order to obtain the highest possible metal removal rates. Refraining from massive use of cooling

lubricant represents an important demand with reference to the environmental impact. Process

safety and an increase in productivity are the pre-requisites for a favorable market position and

thus competitiveness. The machining properties of aluminium are perfectly suitable for putting

modern machining concepts into practice.

Machinability is a consideration in the materials selection process for automatic screw machine

parts. The case with which a metal can be machined is one of the principal factors affecting a

products utility, quality and cost. The usefulness of means to predict machinability is obvious;


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machinability is so complex a subject that it cannot be unambiguously defined. Depending on the

application, machinability maybe seen in forms of tool wear rate, total power consumption,

attainable surface finish or several other bench marks. Machinability therefore depends a great

deal on the view point of the observer, in fact, the criterion for one application frequently conflict

with those for another.

3) OBJECTIVES OF THE WORKThe objective of the work is to discuss the various methods of Taguchi technique and strategies

that are adopted in order to find the following parameters by both experimentally and Taguchi

techniques.

i) The use of arrays to study the effort of machining parameters influence on surfaceroughness.

ii) To develop relationship between the control parameters and response parametersduring machining.

iii) To study the effect of nose radius on the machinability response i.e., surface finish,material removal, machining force and power consumption.

iv) To optimize turning operation parameters for surface roughness, material removal,machining force and power consumption.

v) To optimize unit production cost and it is established on the basis of actualmachining time, setup time, tool re-use time, tool life and tool changing time.

4) EXPERIMENTAL STUDY

Fig.1 Methodology to determine the effectiveness of the turning

parameters on surface roughness of Al 7075 alloy


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The as-received Al 7075 alloys were used in this study shown in Fig. 1 and their chemical

composition are given in the Table 1.7075 Al alloy possess highest strength amongst

aluminum alloys hence they used in space and aerospace engineering. Its strength to weight

ratio is excellent and it is ideally used for highly stressed parts. It may be formed in the

annealed condition and subsequently heat treated.

Table. 1 Chemical composition 7075 Aluminum alloys in weight percentage

Alloy Si Fe Cu Mn Mg Cr Zn Zr Al

7075 0.07 0.24 1.4 0.07 2.5 0.19 5.6 0.15 Balance

6.2 Material Removal Rate (MRR)

The material removal rate is the volume of material removed per unit time. Volume of material

removed per unit time. Volume of material removed is a function of speed, feed and depth of cut.

Higher the values of these more will be the material removal rate.

Let, Di initial diameter of work-piece, mm

d =Depth of cut, mm and

f = Feed, mm/revolution.

Then, material removed per revolution is the volume of chip whose length is and whosecross-sectional area is d x f. That is,

Volume of material removed in one revolution = Since the job is making N rpm., the MRR in / is given by

MRR = /minIn terms of cutting speed V in m/min = / is given by:

MRR = 1000 /min3 Machining time

Tmp = = + +

=


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=

=

=

= / = 5 = 5 = 35

Machining time = 6.4 Cutting force and power requirement

k = 500N = Specific cutting energy co-efficient in /mm2

a)

b)

Fig. 2 Specimens of a) and b) Al 7075 alloys


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Taguchi technique

L8 technique

Experimental design was done using Taguchi method. Hence, ithas been possible to reach more

comprehensive results with doingless experiment. In this sense, time and money have been

usedmore efficiently [7-8]. In the determinationof the characteristics of the quality as the rates of

surfaceroughness to be measured, MRR, cutting time, and cutting force wererequired to be

minimum, less is more principle has been appliedamong the quality values expected to be

reached at the end of theexperiments.

=10 1

Where n is the number of experiments done under experimentconditions and y represents the

calculated characteristics.

Notice that these S/Nratios are expressed on a decibel scale. In this work use S/N if the objective

is to reduce variabilityaround a specific target, S/N if the system isoptimized when the response

is as large aspossible. Factorlevels that maximize the appropriate S/N ratio areoptimal. The goal

of this research was to produceminimum surface roughness (Ra) in a turningoperation. Smaller

Ra values represent better orimproved surface roughness. Therefore, asmaller-the-better quality

characteristic wasimplemented and introduced in this study [9].

The Taguchi method, which is a powerfultool in the design of an experiment, is used tooptimize

the turning parameters for effectivemachining of Al 7075 alloy. This method recommends the

use of S/Nratio to measure the quality characteristicsdeviating from the desired values. To

obtainoptimal testing parameters, the-lower-the-betterquality characteristic for machining the Al

was taken due to the measurement ofthe surface finish. The S/N ratio for each level oftesting

parameters was computed based on theS/N analysis. This design is sufficient toinvestigate the

four main effects and the influenceof their interactions on the surface roughness.With S/N ratio

analysis, the optimal combinationof the testing parameters could be determined.

The control parameters were cutting speed(V), feed rate (f), depth of cut (d) and tools

noseradius (r, mm). Two levels were specified foreach of the factors as indicated in Table 2.

Theorthogonal array chosen was L8, which has 8 rows corresponding to the number of

parametercombinations (7 degrees of freedom), with 7 columns at two levels as shown in Table 3


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[10].The first column was assigned to the cuttingspeed (V), the second column to the feed rate

(f),the fourth column to the depth of cut (d), theeighth column to the tools nose radius (r) and

theremaining columns to the interactions.

Table 2. Assignment of the levels to the factors

Control

Factor

Unit Levels Degree of

Freedom

(DoF)Level

1

Level

2

Cutting

speed, V

m/min 500 1500 01

Feed rate,f mm/rev 0.16 1.16 01

Depth ofCut,d

mm 0.2 0.8 01

Tools nose

radius,r

mm 0.2 0.8 01

Table 3. Orthogonal array L8 of Taguchi

Trail No. Column Number

1 2 3 4 5 6 7

1 1 1 1 1 1 1 1

2 1 1 1 2 2 2 2

3 1 2 2 1 1 2 24 1 2 2 2 2 1 1

5 2 1 2 1 2 1 2

6 2 1 2 2 1 2 1

7 2 2 1 1 2 2 1

8 2 2 1 2 1 1 2

Taguchi analysis for Al 7075 alloy

Similar methods are followed to measured S/N ratio for surface roughness, material removal

rate, machining time, cutting force and power requirement for Al7075 alloy given in Table 4-8

respectively.


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Table 4 Experimental results and S/N ratio of Ra for Al 7075 alloy

Exp.

No.

Actual setting valuesTest result ofRa[m] Average

Ra (m)S/N ratio

NR Feed Speed DOC

1 0.2 0.16 500 0.2 2.799 3.070 3.161 3.010 -1.696

2 0.2 0.16 1500 0.8 2.595 2.846 2.930 2.790 -1.446

3 0.2 1.16 500 0.8 4.548 4.988 5.135 4.890 -3.834

4 0.2 1.16 1500 0.2 2.260 2.479 2.552 2.430 -1.036

5 0.8 0.16 500 0.8 1.330 1.459 1.502 1.430 0.101

6 0.8 0.16 1500 0.2 1.451 1.591 1.638 1.560 -0.047

7 0.8 1.16 500 0.2 8.240 9.037 9.303 8.860 -8.348

8 0.8 1.16 1500 0.8 1.283 1.408 1.449 1.380 0.158

Table 5 Experimental results and S/N ratio of MRR for Al 7075 alloy

Exp.

No.

Actual setting values MRR (mm /min) Average

MRR

S/N ratio

NR Feed Speed DOC

1 0.2 0.16 500 0.2 1434 1573 1619 1542 -1.696

2 0.2 0.16 1500 0.8 1434 1573 1619 1542 -1.446

3 0.2 1.16 500 0.8 7896 8661 8915 8491 -3.834

4 0.2 1.16 1500 0.2 7896 8661 8915 8491 -1.036

5 0.8 0.16 500 0.8 5026 5513 5675 5405 0.101

6 0.8 0.16 1500 0.2 5026 5513 5675 5405 -0.047

7 0.8 1.16 500 0.2 27675 30353 31246 29758 -8.348

8 0.8 1.16 1500 0.8 27675 30353 31246 29758 0.158


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Table 6 Experimental results and S/N ratio of machining time for Al 7075 alloy

Exp.

No.

Actual setting valuesMachining time (s)

b

Average

Machining

time

S/N

ratioNR Feed Speed DOC

1 0.2 0.16 500 0.2 1.0695 1.1730 1.2075 1.1500 -1.696

2 0.2 0.16 1500 0.8 1.0695 1.1730 1.2075 1.1500 -1.446

3 0.2 1.16 500 0.8 0.5766 0.6324 0.6510 0.6200 -3.834

4 0.2 1.16 1500 0.2 0.5766 0.6324 0.6510 0.6200 -1.036

5 0.8 0.16 500 0.8 0.3069 0.3366 0.3465 0.3300 0.101

6 0.8 0.16 1500 0.2 0.3069 0.3366 0.3465 0.3300 -0.047

7 0.8 1.16 500 0.2 0.1674 0.1836 0.1890 0.1800 -8.348

8 0.8 1.16 1500 0.8 0.1674 0.1836 0.1890 0.1800 0.158

Table 7. Experimental results and S/N ratio of machining force for Al 7075 alloy

Exp.

No.

Actual setting values Machining force, N= AverageMachiningforce

S/N

ratioNR Feed Speed DOC

1 0.2 0.16 500 0.2 39.5 43.4 44.6 42.5 -1.696

2 0.2 0.16 1500 0.8 39.5 43.4 44.6 42.5 -1.446

3 0.2 1.16 500 0.8 217.6 238.7 245.7 234.0 -3.834

4 0.2 1.16 1500 0.2 217.6 238.7 245.7 234.0 -1.036

5 0.8 0.16 500 0.8 39.5 43.4 44.6 42.5 0.101

6 0.8 0.16 1500 0.2 39.5 43.4 44.6 42.5 -0.047

7 0.8 1.16 500 0.2 217.6 238.7 245.7 234.0 -8.348

8 0.8 1.16 1500 0.8 217.6 238.7 245.7 234.0 0.158


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Table 8. Experimental results and S/N ratio of Power requirement for Al 7075 alloy

Exp.

No.

Actual setting values Power requirement, w

= )Average

Power

requireme

nt

S/N ratio

NR Feed Speed DOC

1 0.2 0.16 500 0.2 12.0 13.1 13.5 12.9 -1.696

2 0.2 0.16 1500 0.8 12.0 13.1 13.5 12.9 -1.446

3 0.2 1.16 500 0.8 65.8 72.2 74.3 70.8 -3.834

4 0.2 1.16 1500 0.2 65.8 72.2 74.3 70.8 -1.036

5 0.8 0.16 500 0.8 41.9 45.9 47.3 45.0 0.101

6 0.8 0.16 1500 0.2 41.9 45.9 47.3 45.0 -0.047

7 0.8 1.16 500 0.2230.6

252.9

260.4 248.0 -8.348

8 0.8 1.16 1500 0.8

230.6

252.

9

260.

4 248.0 0.158

7 RESULTS AND DISCUSSIONThe main objective of the experiment is to optimizethe turning parameters (cutting speed, feed

rate, depth of cut and tool grade) to achieve low value ofthe surface roughness. The experimental

data forthe surface roughness values and the calculatedsignal-to-noise ratio are shown inTable 8

for Al 7075 alloy. The S/Nratio values of the surface roughness are calculated,using the smaller

the better characteristics.

Table 8 shows the actual data of surfaceroughness along with its computed S/N ratiovalue.

Analysis of variance for S/N ratio. Taguchi recommends analyzing datausing the S/N ratio that

will offer twoadvantages; it provides guidance for selectionthe optimum level based on least

variationaround on the average value, which closest totarget, and also it offers objectivecomparisonof two sets of experimental data with respect todeviation of the average from the

target [10]. Theexperimental results are analyzed to investigatethe main effects and differences

between the main effects of level 0 and 1 on the variables.Average S/N ratio for each level of

experimentis calculated based on the value ofTable 8. The different values of theS/N ratio

between maximum and minimum shown in tables. The feed rate and the cutting speed are two


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factors withthe highest different in values significance0.917 and 0.986, respectively. Based on

Taguchiprediction that the bigger different in value ofS/N ratio shows a more effect on

surfaceroughness or more significant. Therefore, it canbe concluded that, increase changes the

feedrate reduces the surface roughness significantly.

Furthermore, the tool geometry changes, mainlytool nose radius, increase or decrease thesurface

roughness significantly.The result of S/N ratio analysis for thesurface roughness values, which

was calculatedusing Taguchi Method.Then, analysisof variance is shown in Table 9,which

consists of DF (degree of freedom), S(sum of square), V (variance), F (variance ratio)and P

(significant factor) [11,12]. In mostengineering cases, the significant value selectedwas 5% (=

0.05).

Table 9 Anova source of variation based on L8 model specified by the interaction list

SourceSum of

squares

Degrees

of

freedom

F-RatioSignifican

ce (P)

Nose Radius

(A)-0.000 1 -0.000 undefined

Feed Rate (B) 11.223 1 4.633 0.917

Cutting Speed

(C)34.222 1 14.129 0.986

Depth of cut,

(D)2.822 1 1.165 0.669

A X B -0.000 1 -0.000 undefined

A X C -.000 1 -0.000 Undefined

B X C 13.250 1 5.470 0.934

A X D -0.000 1 -0.000 Undefined

B X D 12.888 1 5.321 0.932

C X D 2.722 1 1.124 0.661

Table 9 shows that the significant valueofthe nose radius (A) is 0.000. Itmeans that the nose

radius influences insignificantly on the surfaceroughness value at significant value of 0.05.In

addition to P value for the cutting speed anddepth of cut are more significant. The feed rateandthe cutting have a contribution for the surface roughness are 0.917 and 0.986 respectively.

From this result, it canbe concluded that the feed rate is moresignificant factor and give most

contributionon the surface roughness. Bhattacharyya foundthat the surface roughness was

primarilydependent on the feed rate and the nose radiusof tool [13]. The nose radius related to

toolgrade and tool geometry. Since three types oftool were applied in this experiment


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havedifferent tool nose radius, so effect of tool nosegeometry changes on surface roughness

wassignificant.The interaction between the cuttingspeed and feed rate (C X B), the cutting speed

and depth of cut (C x D) and the feed rate anddepth of cut (B X D) are also insignificant.

These significant values of interaction are0.934 from C x B, 0.661 from C x D and 0.934from B

X C. While a contribution for eachinteraction is small.The most significant factor, whichaffects

the surface roughness measured inturning Al 7075 alloy, is the cutting speed thereforethe quality

of surface roughness can becontrolled by a suitable feed rate value.Previous researchers suggest

similar results.They claimed that the surface roughness wellstrongly depends on the feed rate

followed bythe cutting speed. Jaharah et al. [14]recommended to obtain better surface finishfor

specific test range in end milling was useof high cutting speed (355 m/min), low feedrate (0.1

mm/tooth) and low depth of cut (0.5mm).

The optimum condition in turning ofAl 7075 alloy whichproduces a low surface roughness is at

cutting speed of level 1, feed rate of level 0, depth of cut of level 0 and nose radius of level

1.Meanwhile, optimum condition for interactionfactors is the cutting speed and feed rate oflevel

1, the cutting speed and depth of cut oflevel 0, and the feed rate and depth of cut oflevel 0.The

main effects for each level of parameteron surface roughness are shown in Fig 3.

0.80.2

4

3

2

1

7010

1500500

4

3

2

1

0.80.2

Nose Radius

Mean

Feed

Speed DOC

Main Effects Plot for RaFitted Means

Fig. 3 Main effects for factors machining verse S/N ratio of surface roughness

It can be seen from Fig. 3 that B0 is themaximum value and increase dramatically to B1. For the

graph of feedrate, the slope between the horizontal and feedrate line is bigger. It means that the

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feed ratechanges effected significantly on surfaceroughness, and the same trend can also

beobserved on the graph of tool grade factor foreach level.

7010 1500500 0.80.2

6

4

2

6

4

2

6

4

2

Nose Radius

Feed

Speed

DOC

0.2

0.8

Radius

Nose

10

70

Feed

500

1500

Speed

Interaction Plot for Ra - AvgData Means

Fig.4. Interaction effects for factors machining verse S/N ratio of surface roughness Al 7075 alloy

Fig. 4 shows the interaction between the cutting speed and feed rate (B X C), the cutting speed

and depth of cut (C x D) and the feed rateand depth of cut (Bx D). The S/N ratio value at (B X

C)1 is a best interaction because of it givesthe biggest delta value, and then followed by

interaction (C x D). The cutting speed at level 1(A1) and the feed rate at level 0 (B0) have

amaximum value.It can be also seen from the table that theoptimum predicted result for eachmain factor (linear) gives contribution is 0.945 and interactiongives contribution is 0.76%.

8 CONCLUSIONSThe following conclusions may be drawnfrom various cutting conditions in machining theAl7075 alloy by HSS tools on lathe.

1. Based on the analysis feed is seen to be the most important single factor affecting thesurface roughness.

2. Based on the analysis, it can be seen that interactions have a very important role to play inthe determination of the surface roughness.

3.The interaction between feed and speed is statistically most influential term.4. Following the interaction between feed and speed the next most statistically importantterm is again an interaction i.e. interaction between nose radius and feed.

5. Only after the above two terms doe the factor feed influence the surface roughness.6. Again another interaction between nose radius and speed is also found to be influential.7. Of the four statistically verified terms which influence the surface roughness three are

interactions. This shows that interactions between the factors are in fact more important

than any single factor in determination of surface roughness.

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8. Depth of cut and nose radius are shown not to be of very high importance in itself orinteractions.

9. The feed has consistently shown itself to be an important factor in surface finishing, asindicated by the literature survey.

10. The variance of the parameter nose radius is also seen to be high, suggesting that thesurface finish is also dependent on nose radius.

11. However due to confounding of the interactions and factors and due to the low resolutionof the arrays the prediction of the surface finish with the presently available results is

difficult.

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Engineering, Annamalai University, India, pp. 465472.3. A. Manna, B. Bhattacharyya, A study on different tooling systems during machining of

Al/SiC-MMC, J. Mater. Process. Technol. 123 (3) (2002) 476482.4. I.S. Jawahir, Designing for Machining: Machinability and Machining Performance

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5. B.N. Colding, Tool-Temperature, Tool-Life Relationship Covering a Wide Range ofCutting Data, Annals of the CRIP 40/1 (1991) 35-40

6. B. Lindstrom, Cutting Data Field Analysis and PredictionsPart 1: Straight TaylorSlopes. Annals of the CIRP 3S/1 (1989) 103-106.

7. Arbizu, I.P., Perez, C.J.L. (2003). Surface roughness prediction by factorial design ofexperiments in turning processes.Mater. Process. Technol., vol. 143-144, p. 390-396.

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Abouelatta, O.B., Mdl, J. (2001). Surface roughness prediction based on cuttingparameters and vibrations in turning operations. Mater. Process. Technol., vol. 118, p.269-277.

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10.C.E. Walker, A.M. Parkhurst, Response surface analysis of bake-lab data with a personalcomputer, Cereal Foods World 29 (10) (1984) 662.

11.K. Palanikumar, R. Karthikeyan, Optimal machining conditions for turning of particulatemetal matrix composites using Taguchi and response surface methodologies, Mach. Sci.Technol. 10 (2006) 417433.

12.G. Boothroyd, W.A. Knight, Fundamentals of Machining and Machine Tools, third ed.,CRC Press, Taylor & Francis Group, 2006, pp. 192196.13.S. Nes_eli, S. Yaldz, The effects of approach angle and rake angle due to chattervibrations on surface roughness in turning, J. Polytech. 10 (4) (2007) 383389.

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An Optimization of Machinability of Aluminium Alloy 7075 and Cutting Tool Parameters by Using Taguchi Technique

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