IRJET- Optimization of Control Factors for Machining Time in CNC Milling of AL-7075 based MMCs using Taguchi Robust Design Methodology

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 339
Optimization of Control Factors for Machining Time in CNC Milling of
Al-7075 based MMCs Using Taguchi Robust Design Methodology
Ashutosh Satpathy1, Sudhansu Sekhar Singh2
1CAPGS, BPUT, Rourkela, ODISHA
2CAPGS, BPUT, Rourkela, ODISHA
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - Every day’s scientists are developing new
materials and each for new materialforthemilling machining
operation. For the CNC milling machining operation, thework
piece should be normally economical and efficient. Al-7075
based MMCs is carried out as the work piece in the end milling
operation because it has high strength and stiffness, less
density, high electrical performance andhigh wearresistance.
Taguchi methodology is carried out for the experiment to
optimize the various machining parameters as it reduces the
number of experiments. This paper is dealing with the
optimization of selected milling process parameter that’s
spindle speed, feed rate and depth of cut. In the optimization
technique Taguchi orthogonal array containing 3 columns
which represent 3 factors and nine rows which represent the
nine experiments to be conductedand valueofeachparameter
was obtained. The machining time value is considered for the
responses of the experiment. The main objective of SN ratio is
to predicted and verified test values are valid when compared
to the optimum values. The main objective of the paper is to
found the SN ratio value and verified the limits with
corresponding to the Taguchi design.
Key Words: Al-7075 Based MMCs, CNC Milling, Machining
time, SN Ratio, Taguchi Design
1. INTRODUCTION
The main objective of the research work is to find out the
optimum values for the selected control factors in order to
reduce machining time (MT) using Taguchi’s robust design
methodology and to develop the prediction models for
machining time considering the control factors. In the
present work Taguchi method is used to determine the
optimum cutting milling parametersare moreefficiently and
the three cutting parameter are spindle speed, feed rate and
depth of cut are used in three different level in the project
work. The Al-7075 based metal matrix composite is used as
the work piece. Taguchi method is used to optimize the
process parameter i.e. surface roughness and MRR using
signal to noise ratio for milling process of the work piece
materials. The Taguchi experiments are carried outusing L9
(33) orthogonal array.
Milling is process of removing extra material from the work
piece with a rotating with a rotating multi-point cutting tool
is called multi cutter. The primary factor of the milling
machining operation are spindle speed, feed rate and depth
of cut. Other factor of the milling process is depending open
the tool material and adjusting of the control factor etc.
In the CNC milling operation is controlled by the computer
numerical method. In modern CNC system, end-to-end
component is designed in highly automated using computer
aided design and computer aided manufacturing process.
The CNC machine is the general termusedforsystemswhich
control the functions of the machine tool using coded
instructions processed by a computer. The part program of
the CNC machine enhances the ability of the machine to
perform repeat tasks with high degree of accuracy. The CNC
machine program is coded by G-code and M-code. The G-
code is used for tool movements, linear cutting movements.
The M-codes is used for CNC to command on/off signals to
the machine function.
Machining Time is the essential responses of the milling
operation which depends upon the input factors. It affects
the total machining process and depends upon the material
removal rate. The values of the machining time w.r.t to the
input factor in this experiment are defined below. Theresult
of the machining time is different depending upon the input
factor. The machining time is affected themachiningprocess
and economy of the process.
1.1 Literature Review
Arkiadass et. al [1] calculated the flank wear of end milling of
LM25 Al/SiCp and also found the spindle speed . They
predicted the surface roughness depends upon the
composition of Al/SiCp composite material .Grossi et. Al [2]
explained during machining of Aluminum 6082-T4 alloy the
chatter formation depend upon the cutting force, no of
revolution and depth of cut. Sammy et. al [3] proposed that it
was the cyclic interaction between the tool and the work
piece by increasing of chip production depending upon the
increasing feed rate. The bettersurfacefinishoccurreddueto
minimum feed & depth of cut. Vishnu et. al[4] optimized the
parameter of EN-31 steel alloy, mainly feed rate,depthofcut,
coolant flow by taguchi optimization method. Arokiadass et.
al [5] studied the machining characteristics of LM 25Al/SiCp
composite material and found the tool wear by response
surface methodology and another optimization technique
used that was CCD ( Central Composite Design ) method.
Hung et. al [6] profoundly studied the graphical
representation of cutting distance and cutting & flank wear ,
from the graph the cutting speed impacted on tool wear &
flank wear . The tool wear was high depending upon the
increased cutting speed. Chang et. al. [7] described on work

piece S545C medium carbon steel by using as high speed
steel tool & it was coated with TiN .The performance
characteristics commonly used for evaluating side milling
process were, feeding direction roughness, axial direction
roughness & waviness. Wouw et al. [8] investigatedonstable
and unstable chip formation from the SLD diagrammed
depending upon the material model & machine model. They
concluded that the three modelanalysiswasaffectedthechip
formation & it depended on the surface roughness model.
Mustafa et. al. [9] experimented on steel composites by CNC
milling and optimized the tool wear and high speed
developed higher temperatures that cause softening and
reducing of the adhered material on tool. Baek et. al.[10]
explained the optimal feed rate of face milling operation
presented on considering the profile and runouterrorwhich
was operated on work piece AISI 1041 with milling cutter
made by tungsten. Karakas et. al. [11] explained the better
performance in the work piece occurred by coated tool as
compare touncoatedtoolandflankweargraduallydecreased
with decrease in cutting speed. FangHong et. al. [12]
explained that the cutting temperature calculation depends
on the various cutting speed and the cutting temperature
increased if the cutting speedwas increased. A bettersurface
integrity was depending upon the low cutting speed, the tool
wear was calculated as the rake face wear, flank wear other
edge corner.
2. Experimental Setup & Machining Process
The aim of the experimental work is findoutthecombination
of optimum values forthe selected control factors in order to
machining timeusing Taguchi’s Robust DesignMethodology.
The Workpiece of the experiment is taken as the Al 7075
based MMCs (Metal Matrix Composite Materials). The DOE
(Design of Experiments) are conducted using L9 (33)
orthogonal array.
2.1 CNC Vertical Milling Machine
The End milling operation was done by Surya5 CNC milling
machine. The machining tests are conducted by different
conditions of spindle speed, feed rate & depth of cut. The
machine specifications of the CNC machine are:
Table -1: CNC Milling Machine Specification
Machine Characteristics Specification
Name Of The CNC Machine SURYA 5
Type Of the CNC Machine CNC Vertical Milling
Machine
Series Of the machine Fanuc Series Mate MD
Make of the machining HFW- BHARAT FRITZ
WERNER LTD.
Year Of Commissioning Of
machine
2013
Axis Specification of the milling
machine
(800×350×380)mm
Accuracy Of the machine 10 micron
Cost of the machine 1.8 million
Motor Power of the milling
machine
0.5 HP
Fig -1: CNC milling setup
2.2 Work piece Material
In this present work Al-7075based Metal MatrixComposites
(MMCs) material is used. The dimension of the work piece
material is diameter (φ) is 45mm & Height of the material is
20mm. The percentages of the reinforcement and matrix
materials are (Al-7075-97% ,Al2O3-2%, B4C-1%).TheMMC
material is manufactured by stir casting method.
Fig -2: Workpiece Material
2.3 Cutting Tool Material
In the CNC End milling operation the HSS (High Speed Steel)
material is used as the cutting tool material. The dimensions
of the cutting tool are (diameter- 10mm,Helixangle-38°, No
of flutes – 4, Tool length – 72mm, Cutting edge length –
24mm). It consists ofveryhighhardnessandgoodtoughness
and it is principally used for roughing of super alloys and
steel alloys.

Fig -3: HSS end milling tool
3. Design of Experiments (DOE)
The experimental process is taken as the three process
parameters with three levels are chosen as the input
parameters are sufficiently far apart so that they covered
wide range. The process parameters and their ranges
finalised by the literature review, books and machine
operator’s experience. The selected three input parameters
are spindle speed, feed rate and depth ofcut.Al – 7075based
MMCs is machined by this input parameter with three
different levels (low, medium, high). The input parameter
and their level defined in this table.
Table -2: Design of Experiment of the Control Factor
Sl
No
Factors Symbol Level-1
(Low)
Level-2
(Medium)
Level-3
(High)
1 Spindle
speed
A 800 RPM 1200RPM 1500
RPM
2 Feed
Rate
B 200
mm/min
350
mm/min
500
mm/min
3 Depth
of Cut
C 0.4 mm 0.8 mm 1 mm
3.1 Selection of Orthogonal Array
Selection procedure of the OA (orthogonal Array) depends
on the number of factors, levels of each factor and the total
degrees of freedom. The steps of the selections of the
Orthogonal Array are.
1) Number of control Factors = 3
2) Number of levels for each control factors = 3
3) Number of experiments to be conducted = 9
Factor assignment for L9 (33) has shown in table which is
defined by the control factor with different table.
Table -3: Experimental Data of the Milling machine
operation
Sl No Spindle Speed(A) Feed Rate(B) Depth of Cut (C)
1 800 200 0.4
2 800 350 0.8
3 800 500 1
4 1200 200 0.8
5 1200 350 1
6 1200 500 0.4
7 1500 200 1
8 1500 350 0.8
9 1500 500 0.4
4. Result & Discussion
The Al-7075 based MMCs are prepared for the conducting
experiment. Using different levels of the processparameters
the specimens have been machined accordingly, depending
open the spindle speed, feed rate & depth of cut in different
conditions. The machining time is measured by time
required by the machining process. The machining time
result of the experiments has been show in below the table.
Table -4: Output table for the machining time
SL
NO
SPINDLE
SPEED
Feed
Rate
Depth Of
Cut
Machining
Time (in Sec)
1 800 200 0.4 238
2 800 350 0.8 82
3 800 500 1 54
4 1200 200 0.8 123
5 1200 350 1 68
6 1200 500 0.4 106
7 1500 200 1 104
8 1500 350 0.8 77
9 1500 500 0.4 108
The below table describes the result of SN ratio calculated
for the machining time considering smaller is the better
because when the time is less it affects the MRR and
machining economy.
Table -5: SN ratio output of the Machining Time
Sl
No
Spindle
Speed
Feed
Rate
Depth
Of Cut
Machining
Time (In Sec)
S/N
Ratio(MT)
1 800 200 0.4 238 -47.5315
2 800 350 0.8 82 -38.2763
3 800 500 1 54 -36.6479
4 1200 200 0.8 123 -41.7981
5 1200 350 1 68 -36.6502
6 1200 500 0.4 106 -40.5061
7 1500 200 1 104 -40.3407
8 1500 350 0.8 77 -37.7298
9 1500 500 0.4 108 -40.6685

From the graph it is significant that the Signal to Noise ratio
performance as comparedto theinputfactoriscalculatedfor
the machining time. Here S/N ratio calculation for
machining time is calculated by smaller is better because
when the machining time is small it affects the complete
machining process. In the first case the amount of spindle
speed is increased from 800 RPM to 1500 RPM. The S/N
ratio value also gradually increases ascomparedtotheinput
factor from -40.15 to -39.58. In second case the feed rate
value gradually increase from 200mm/min to 500 mm/min.
It indicates the S/N ratio value increase to a certain point
then decrease. It is increasing from -42.90 to -39.27 then
decrease from -39.27 to -37.21. In the third case thedepthof
cut value is gradually increased it affects S/N ratio value
which gradually increase from -42.90 to -37.21.
Fig -4: SN ratio Plot of Machining time
The table represents the rank oftheinputfactorwithrespect
to the S/N ratio. From the table it is represented that depth
of cut is the most significant influencing parameter
compared to feed rate & spindle speed in machining time.
Table -6: Rank calculation of SN ratio of Machining Time
Level Spindle Speed Feed Rate Depth of Cut
1 -40.15 -43.22 -42.90
2 -39.65 -37.55 -39.27
3 -39.58 -38.61 -37.21
Delta 0.57 5.67 5.69
Rank 3 2 1
4.1 Effect of cutting parametersonMachiningTime
Fig -5: Histogram Graph Between Input Parameter Vs
Machining Time
From the above histogram it representedthatthemachining
time fluctuates with respect to the input factor. The below
graph is represented that when the depth of cut is constant
but the spindle speed & the feed rate increases the
machining time value is decreased, as in first experiment &
last experiment the depth of cut is constant (0.4) but the
spindle speed increases from 800 RPM to 1500 RPM & the
feed rate increase from 200mm/min to 500mm/min as a
result the machining time value is decreased from 238
second to 108 second. Hence it proves that the machining
time depends upon the input factor.
Fig -6: Graph between spindle speed and machining time
The above figure ig generated bytheMATLABtool.Itdefined
that the graphical representation betweenthespindlespeed
and machining time. Here it also defined that when the
spindle speed value is increased, the amount of machining
time is less.
5. CONCLUSIONS
The objective of the present work is to find out the set of
optimum values in order to optimize the machining time
using Taguchi’s robust design method considering the

control factors (spindle speed, feed rate and depth of cut)
with three levels for the Al-7075 based MMCs.
Based on the results of the present experimental
investigations the following conclusions are:
1) In the present experiment the optimum value of the
machining time is considered for the combination of the
control factor of the control combination are 800 rpm,
500mm/min and 1mm.The optimum value is predicted by
the SN ratio value. From the SN ratio value it indicated the
rank of the control factor w.r.t the surface roughness value.
2) In this research, it defines the graphical representation
between the experimental result(machiningtimevalue)and
the level of control factor. From this representation it
explains that the relation between the experimental value
and the control factor.
3) In this paper it represents that graphical representation
between spindle speed and machining time with the help of
MATLAB tool.
6. Future Scope
In this work, the optimum values are obtaining using
Taguchi technique. Hence there is a large scope of future
work to be carried.
1) In future work can be carried out by selecting the factors
to be significant using ANOVA technique, Grey Taguchi and
ANN method.
2) In future to calculate the chip thickness and another
response value of the MMCs using this 3-level of the control
factors.
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[3] Samy, G. S., S. Thirumalai Kumaran, and M.
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IRJET- Optimization of Control Factors for Machining Time in CNC Milling of AL-7075 based MMCs using Taguchi Robust Design Methodology

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