an optimization of machinability of aluminium alloy 7075 and cutting tool parameters by using...
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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
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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
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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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