Artificial Neural Network Modeling and Analysis of EN24 &EN36 Using CNC Milling Machine

NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 3 MARCH2015
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Artificial Neural Network Modeling and Analysis of EN24
&EN36 Using CNC Milling Machine
A Bhargav,
Assistant professor
CNV Sridhar
Professor& Head
S Girivardhan Naidu
PG student
Abstract
Metal cutting is one of the most significant manufacturing processes in the area of material
removal. It is Block it is defined metal cutting as the removal of metal chips from a work
piece in order to obtain a finished product with desired attributes of size, shape, and surface
roughness.The imperative objective of the science of metal cutting is the solution of practical
problems associated with the efficient and precise removal of metal from work piece. It has
been recognized that the reliable quantitative predictions of the various technological
performance measures, preferably in the form of equations, are essential to develop
optimization strategies for selecting cutting conditions in process planning. In this thesis
experiments has to be conducted to improve the surface finish quality of a work piece by
using carbide tips. The type is bull nose tip. A series of experiments have to be done by
varying the milling parameters spindle speed, feed rate and depth of cut and modeling is done
by ANN. and Analysis is done by ANSYS.
Introduction
The important goal in the modern industries is to manufacture the products with lower cost
and with high quality in short span of time. There are two main practical problems that
engineers face in a manufacturing process. The first is to determine the values of process
parameters that will yield the desired product quality (meet technical specifications) and the
second is to maximize manufacturing system performance using the available resources.
Machining is the process of removing the unwanted material from the work piece in the form
of chips. If the work piece is a metal, the process is often called as metal cutting process chip
forming processes. Metal cutting is a machining process by which a work piece is given as
follows
• A desired shape
• A desired size
• A desired surface finish
To achieve one or all of these, the excess (undesired) material is removed (from the work
piece) in the form of chips with the help of some properly shaped and sized tools. Metal

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cutting processes are performed on metal cutting machines, more commonly termed as
Machine Tools by means of various types of cutting tools.
The increase of customer needs for quality products (more precise tolerances and better
product surface roughness) have driven the metal cutting process. In the global antagonism,
manufacturing organizations are working in the direction of improving the product quality
and performance of the product with lower cost in short span of time, but practices aimed at
lowering the costs don’t usually improve quality.
There is hierarchical relationship between cost and quality. The intense international
competition has focused the attention of the manufacturers on automation as means to
increase productivity and improve quality. To realize full automation in machining, computer
numerically controlled (CNC) machine tools have been implemented during the past decades.
CNC machine tools require less operator input, provide greater improvements in productivity
and increase the quality of the machined part.
Among several CNC industrial machining processes, milling is a fundamental machining
operation. End milling is the most common metal removal operation encountered. It is widely
used in a variety of manufacturing industries including the aerospace and automotive sectors,
where quality is an important factor in the production of slots, pockets, precision molds and
dies etc.,.
The surface roughness plays an imperative role in the manufacturing industry. The quality of
surface affects functional requirements and plays a very important role in the performance of
the milling as a good quality of milled surface significantly improves fatigue strength,
corrosion resistance or creep life. Surface roughness also affects the several functional
attributes of parts such as contact causing surface friction, wearing, light reflection, heat
transmission and ability of distributing and holding lubricant coating or resisting fatigue.
Therefore the desired finish surface is usually specified and the appropriate processes are
selected to reach the required quality.
Several factors will influence the final surface roughness in a CNC milling operation. The
final surface roughness might be considered as the sum of two independent effects.
1. The ideal surface roughness is a result of the geometry of the tool & feed and
2. The natural surface roughness is a result of the irregularities in the cutting operation.
(Boothoryd and Knight, 1989).
In this work, the design of experiments techniques such as orthogonal arrays in
Taguchi design, ANOVA and Artificial Neural Network has been implemented to develop
model and analysis for a better product quality.
II DESIGN OF EXPERIMENTS (DOE):
A Design of Experiment (DOE) is a structured, organized method for determining the
relationship between factors affecting a process and the output of that process. Conducting
and analyzing controlled tests to evaluate the factors that control the value of a parameter or
group of parameters. “Design of Experiments" (DOE) refers to experimental methods used to
quantify indeterminate measurements of factors and interactions between factors statistically
through observance of forced changes made methodically as directed by mathematically
systematic tables.
2.1Design of Experiment Techniques
1. Factorial Design.
2. Response Surface methodology.
3. Mixture Design.
4. Taguchi Design.

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Among those we had selected Taguchi Design for optimizing surface finish and cutting
forces in face milling Operation.
2.2Orthogonal array
In order to reduce the total number of experiments “Sir Ronald Fisher” developed solution:”
orthogonal arrays”. The orthogonal array can be thought of as a distillation mechanism
through which the engineers experiment passes (Ealey, 1998). The array allows the engineer
to vary multiple variables at one time and obtain the effects which that set of variables has an
average and the dispersion. Taguchi method can be calculated based on the degrees of
freedom approach.
N୘୅ୋ୙େୌ୍ = 1 + ∑ ሺ‫ܮ‬௜ − 1ሻே௏
௜ୀଵ ---------- (3.1)
Taguchi employs design experiments using specially constructed table, known as
"Orthogonal Arrays (OA)" to treat the design process, such that the quality is build into the
product during the product design stage. Orthogonal Arrays (OA) are a special set of Latin
squares, constructed by Taguchi to lay out the product design experiments. An orthogonal
array is a type of experiment where the columns for the independent variables are
“orthogonal” to one another. Orthogonal arrays are employed to study the effect of several
control factors. Orthogonal arrays are used to investigate quality. Orthogonal arrays are not
unique to Taguchi, they were discovered considerably earlier (Bendell, 1998). However
Taguchi has simplified their use by providing tabulated sets of standard orthogonal arrays and
corresponding linear graphs to fit specific projects (ASI, 1989; Taguchi and Kenishi, 1987).
Table3.1 A typical L9 orthogonal array: (34
)
A B C D
1 1 1 1 1
2 1 2 2 2
3 1 3 3 3
4 2 1 2 3
5 2 2 3 1
6 2 3 1 2
7 3 1 3 2
8 3 2 1 3
9 3 3 2 1
In this array the columns are mutually orthogonal. That is for any pair of columns all
combination of factors occurs; and they occur an equal number of times. Here there are 4
parameters, A, B, C and D each at three levels. This is called an ‘L9’ design; with the 9
indication the nine rows, configurations or prototypes to be tested. Specific test
characteristics for each experimental evaluation are identified in the associated row of the
table. Thus L9 (34
) means that nine experiments are to be carried out to study four variables
with three levels. There are greater savings in testing for larger arrays.
III EXPERIMENTATION
Experiments have been performed in order to investigate the effects of one or more factors of
the process parameters (spindle speed, feed rate and depth of cut) on the surface finish of the
machined surface.

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The main aim of the project is to determine the influence of radius carbide tips in metal
working. The investigation is based on surface roughness during milling of EN24 and EN36
steel with carbide tool. The cutting parameters considered are feed rate, spindle speed and
depth of cut.
3.1Experimental Procedure
This experiment employed a CNC vertical milling machine. Carbide cutting tool is used. The
experiment has been done under conditions of feed rate 700mm/min, 980mm/min, 1400
mm/min, 1900 mm/min and 2600 mm/min. For EN24 Spindle speeds are2000rpm, 1800rpm,
2100rpm, and depth of cut 0.25mm, And for EN36, spindle speeds are 2100rpm, 2200rpm,
2300rpm, 2400rpm and 2500rpm.
Five square pieces of EN 24 material and EN36 material are taken for machining.
fig3.1 spindle rotation
3.2Cemented carbide Tool
Advantage of cemented carbide in around 1905 made a break through at that time in the
history of cutting tool materials though got later superseded by many other novel tool
materials like cemented carbides and ceramics which could machine much faster than the
HSS tools. The basic composition of HSS is 94% WC, 6.0% CO, 1% and rest Fe. Such
carbide tool could machine (turn) mild steel jobs at speed only up to 20 ~ 30 m/min
Carbide is used as cutting tool material where;
• The tool geometry and mechanics of chip formation are complex, such as helical twist
drills, reamers, gear shaping cutters, hobs, form tools, broaches etc.
• Brittle tools like carbides, ceramics etc. are not suitable under shock loading
• The small scale industries cannot afford costlier tools
• The old or low powered small machine tools cannot accept high speed and feed.
Table 3.1 CNC milling machine and its specifications
DESCRIPTION SPECIFICATION
Model CKEN 40
Table clamping area 2M/1800
Travel X-axis
Y-axis
Z-axis
1800 mm
850 mm
750mm

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Table size
2000 X 800mm
Spindle Motor Capacity 0.5 HP
Spindle speeds 150 - 4500RPM
Power Source 230V, Single Phase, 50 Hz
Feed rates BT 50
Rapid traverse x, y, z-axis 0-1 m/min
CNC control system PNEUMATIC
Machine Dimensions 1000x575x650mm
Fig.3.2. CNC milling machine CKEN 40
Table 3.2 Material Input Parameters of EN-24
SECTION-A
S.NO. FEED
(mm/min )
SPEED
(rpm)
DEPTH OF CUT
(mm)
1. 700 2000 0.25
2. 980 1800 0.25
3. 1400 2100 0.25
4. 1900 2000 0.25
5. 2600 2000 0.25

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Table 3.3 Machining input Parameters of EN 24
SECTION-B
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
1. 700 2100 0.25
2. 980 2200 0.25
3. 1400 2300 0.25
4. 1900 2400 0.25
5. 2600 2500 0.25
Table 3.4 Material Input Parameters of EN-36
SECTION-A
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
1. 700 2000 0.25
2. 980 1800 0.25
3. 1400 2100 0.25
4. 1900 2000 0.25
5. 2600 2000 0.25
Table 3.5 Machining input Parameters of EN 36
SECTION-B
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
1. 700 2100 0.25
2. 980 2200 0.25
3. 1400 2300 0.25
4. 1900 2400 0.25
5. 2600 2500 0.25

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3.3 Surface finish measurement
Talysurf meter instrument is widely used to measure the shape or form of components. A
profile measurement device is usually based on a tactile measurement principle. The surface
is measured by moving a stylus across the surface. As the stylus moves up and down along
the surface, a transducer converts these movements into a signal which is then transformed
into a roughness number and usually a visually displayed profile. Multiple profiles can often
be combined to form a surface representation. Talysurf meter is shown in fig 4.4.
Fig.3.3. Talysurf roughnessmeter
3.4 Experimental procedure
i) The parameters which influence the milling process and their levels are listed based on
previous works (table 4.4 and 4.5).
ii) Milling operation is performed on EN24 and EN36 by using the CNC Milling machine.
iii) The surface finish values of work are measured using Talysurf meter (table 4.6).
The experimental results are presented in Table.-3.6
Table 3.6Material Surface Finish Result for EN-24
SECTION-A
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
Surface finish (Ra)
1. 700 2000 0.25 4.36+2.78/2=3.57
2. 980 1800 0.25 4.1+2.85/2=3.475
3. 1400 2100 0.25 1.16+3.76/2=2.46
4. 1900 2000 0.25 1.34+1.30/2=1.32
5. 2600 2000 0.25 1.2+1.24/2=1.22

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Table 3.7Material Surface Finish Result for EN-24
SECTION-B
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
Surface finish (Ra)
1. 700 2100 0.25 4.23+2.90/2=3.63
2. 980 2200 0.25 4.6+2.97/2=3.785
3. 1400 2300 0.25 1.45+3.92/2=2.685
4. 1900 2400 0.25 2.13+1.72/2=2.785
5. 2600 2500 0.25 1.61+1.4/2=2.31
Table 3.8 Experimental results Raof EN24
SECTION-A
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
Surface finish (Ra)
1. 700 2000 0.25 4.76+3.1/2=3.93
2. 980 1800 0.25 4.41+3.1/2=3.755
3. 1400 2100 0.25 1.32+3.86/2=3.25
4. 1900 2000 0.25 1.53+1.71/2=1.62
5. 2600 2000 0.25 1.3+1.56/2=1.96
Table 3.9 Experimental results Ra of EN36
SECTION-B
S.NO. FEED
(mm/min)
SPEED
(rpm)
DEPTH OF CUT
(mm)
Surface finish (Ra)
1. 700 2100 0.25 4.86+3.31/2=4.085
2. 980 2200 0.25 4.92+3.42/2=4.17
3. 1400 2300 0.25 1.61+3.92/2=2.765
4. 1900 2400 0.25 2.42+2.1/2=2.26
5. 2600 2500 0.25 1.71+1.52/2=1.615

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RESULTS AND DISCUSSIONS
Thrust Force and Torque Calculations of Materials - En-24 and En-36b
Cutter dia = 25R5
Width of Workpiece = 75mm
No of Teeth on cutter = 4 = nc
Depth of Cut = d = 0.2
Width of Cut = b = 5mm
Width of chip = bc = 5mm
V = Cutting Velocity
rt = Chip Thickness Ratio
rt = t/tc = vc/v = lc/l
LC = Length of Chip = 7mm
L = Uncut Chip Length = 75mm
∝ = Rake Angle = 20°
β = Friction Angle = 40
∅ = Shear Angle
Speeds (RPM) Feed (mm/min)
3000 200
2500 300
2000 400
Chip Thickness Ratio
rt = t/tc = vc/v = L_C/L
rt = L_c/L = 7/75 = 0.093
Shear Angle ( ∅ )
Tan ∅ = (r_tcos∝)/(1-r_t sin∝)
Tan ∅ = (0.093 cos20)/(1-0.093 sin20)
∅ = 5.781
To Calculate Thrust Force
Ft = µ [ (HA_c)/3 ((Cot∅)/√3+1)]+A_F (0.62H√(43H/E))
AC = Cross – Section of Chip
Af = Area of Tool Flank Face
∅ = Shear Angle in w/p
µ = Friction Coefficient on Rake Face
H = Hardness of w/p
E = Young’s Modulus of w/p
EN-24 Material with speed of 2000rpm, feed 700mm/min.
Cutting speed (V)=πDN/1000m/min
Where D=cutter dia =25mm
N= spindle speed = 2000rpm
V=cutting speed
V=(π×25×2000)/1000=157.07 m/min
Feed per tooth of cutter :
ft=F/((n_(c )×N) ) mm/rev/tooth
n_(c ) = no.of cutting edges or teeth on cutter = 4
F = 700mm/min= table feed
ft=700/((4×2000) )=0.087 mm/rev/tooth
Ac = plan area of cut =cross section of chip

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Ac= w×d
w =width of work piece being cut =75mm
d = depth of cut
Ac= 15sq.mm
A=feed length for cutter to reach full depth = Af =area of tool flank face
A = √((D-d)×d)=√((25-0.25)×0.25)=2.487mm
Cutting Forces in Orthogonal Cutting
Fh=horizontal force component parallel to cutting velocity
Fv=vertical force component normal to Fh
Fs=force component parallel to shear plane
Fp= force component normal to Fs
Ft= force component parallel to tool rake face
Fn= force component normal to Ft
F_S=R cos (∅+β-α)
F_h=R cos (β-α)
F_n=R cos (β)
F_V=R sin (β-α)
F_S=R sin (∅+β-α)
F_t=R sin β
WHERE
R=(t.b.K)/sin 〖∅cos (∅+β-α) 〗
Where k= yield stress of material in shear =325Mpa
Thrust Force Calculation
F_t=µ[(HA_c)/3 (cot ∅/√3+1)]+A_f (0.62H√(43H/E))
A_c=w×d=15sq.mm
w = 75mm=width of work piece being cut
d=depth of cut=0.25
A= A_f=area of tool flank face = 2.487
A=√((D-d)d)
Where D = cutter dia
d=depth of cut
A=√((25-0.25)0.2) 5
A = A_f=2.487
µ=friction angle=tan 〖β=0.8〗
Where β=90
H=hardness of the work piece = 292
Density = 0.000007850 J/goC
E=young’s modulus of work piece =213000 Mpa
F_t=0.8[(292×15)/3 (cot 5.781/√3+1)]+2.487(0.62×292√((43×292)/213000))
F_t=55350.39N
EN-36 Material with speed of 2000rpm, feed 700mm/min.
Cutting speed (V)=πDN/1000m/min
Where D=cutter dia =25mm
N= spindle speed = 2000rpm
V=cutting speed
V=(π×25×2000)/1000=157.07 m/min

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Feed per tooth of cutter :
ft=F/((n_(c )×N) ) mm/rev/tooth
n_(c ) = no.of cutting edges or teeth on cutter = 4
F = 700mm/min= table feed
ft=700/((4×2000) )=0.087 mm/rev/tooth
Ac = plan area of cut =cross section of chip
Ac= w×d
w =width of work piece being cut =75mm
d = depth of cut
Ac= 15sq.mm
A=feed length for cutter to reach full depth = Af =area of tool flank face
A = √((D-d)×d)=√((25-0.25)×0.25)=2.487mm
Cutting Forces in Orthogonal Cutting
Fh=horizontal force component parallel to cutting velocity
Fv=vertical force component normal to Fh
Fs=force component parallel to shear plane
Fp= force component normal to Fs
Ft= force component parallel to tool rake face
Fn= force component normal to Ft
F_S=R cos (∅+β-α)
F_h=R cos (β-α)
F_n=R cos (β)
F_V=R sin (β-α)
F_S=R sin (∅+β-α)
F_t=R sin β
WHERE
R=(t.b.K)/sin 〖∅cos (∅+β-α) 〗
Where k= yield stress of material in shear =325Mpa
Thrust Force Calculation
F_t=µ[(HA_c)/3 (cot ∅/√3+1)]+A_f (0.62H√(43H/E))
A_c=w×d=15sq.mm
w = 75mm=width of work piece being cut
d=depth of cut=0.25
A= A_f=area of tool flank face = 2.487
A=√((D-d)d)
Where D = cutter dia
d=depth of cut
A=√((25-0.25)0.2) 5
A = A_f=2.487
µ=friction angle=tan 〖β=0.8〗
Where β=90
H=hardness of the work piece = 341
Density = 0.000007850 J/goC
E=young’s modulus of work piece =207000 Mpa
F_t=0.8[(341×15)/3 (cot 5.781/√3+1)]+2.487(0.62×341√((43×341)/207000))
F_t=64650.93N

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CONCLUSION:
In this thesis, experiments are conducted to improve the surface finish quality of a work piece
by using carbide tips. The type of tip is bull nose tip. A series of experiments is done by
varying the milling parameters spindle speed, feed rate. The experiments are conducted on
vertical milling machine of make Chenho. The work piece materials are alloy steel EN24 and
EN36.
Two sets of each work piece material are machined by specifying following parameters:
Feed rates of 700mm/min, 980mm/min, 1400mm/min, 1900mm/min and 2600mm/min,
Spindle speeds of 2000rpm, 1800rpm, 2100rpm, and 2000rpm. By observing the
experimental results, for EN24 material machining at 2600mm/min feed rate and spindle
speed of 2000rpm yields better results as the surface finish is good.
For material EN36 machining at 1900mm/min and 2000rpm yields better results as
the surface finish is good.Theoretical calculations are done to calculate the thrust force and
torque. Structural analysis is also done to verify the strength and modeling is done in ANN

Artificial Neural Network Modeling and Analysis of EN24 &EN36 Using CNC Milling Machine

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