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International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 123
202301-7878-IJMME-IJENS © February 2020 IJENS I J E N S
Investigation of Surface Texture and Surface
Residual Stresses in the Dry Face Turning Process
of AL2024-T351
Hussein Zein 1, 2, * 1 Mechanical Engineering Department, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia
2 Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
*dr.husseinzein@qec.edu.sa
Abstract-- The objective of this research is to investigate the
effect of the machining parameters on the surface texture and
the surface residual stresses distribution for AL2024-T351 in
the face turning process. Experimental work is carried out on
samples of AL2024-T351 for the face turning process at
different machining parameters of cutting speed and feed rate
with a constant depth of cut. 3D Optical Microscope is used to
assess the surface topography for the machined samples. 3D–
simulation model developed for plane stress finite element
modeling of a pure orthogonal cutting process for Al2024-
T351 at different machining conditions. In this research,
ABAQUS/Explicit package was used to predict the surface
residual stress distributions in the face turning process at
different cutting conditions. Finally, correlations are made
between the measures of the surface roughness and the finite
element analysis results to predict the optimal trend of
machining parameters for quality surface finish and minimum
surface residual stress.
Index Term-- Machining; Surface Texture; Surface Residual
Stresses; Cutting Speed; Feed Rate; Finite Element.
NOMENCLATURE
A: The yield strength constant (MPa)
B: The work hardening coefficient
C: The strain rate coefficient
D: Johnson-Cook damage model parameter
E: Young’s modulus (GPa)
m: The thermal softening exponent
n: The work hardening exponent
Rt: The maximum peak-to-valley height for surface
topography (μm)
Rz: A ten-point height for surface topography (μm)
T: The effective temperature (oC)
T*: The homologous temperature (oC)
Troom: The ambient temperature (oC)
υ: Poisson’s ratio
ρ: Density (kg/m3)
ϵp: The equivalent plastic strain
ϵf: The strain at failure
ϵ̇∗: The dimensionless plastic strain rate (s-1)
ϵ̇p: The equivalent plastic strain rate (s-1)
ϵ̇o: The equivalent initial strain rate (s-1)
σ: The flow stress (MPa)
σ∗: The dimensionless pressure stress ratio
σm: The average of the three normal stresses (MPa)
σ̅: The Von Mises equivalent stress (MPa)
σyo: Initial yield stress (MPa)
1. INTRODUCTION
The machining process is a very basic manufacturing
process within the industry and a major effort is made to
improve its processes. Due to a large number of affecting
parameters and the extreme range of conditions, machining is
a very complex process. In both product development and
customer work designs, simulation of cutting is a widely used
tool. Cutting conditions in a machining process consist of
cutting speed, feed rate and depth of cut. The effects of
cutting speed and feed rate on the surface topography are
different with a different change in cutting depth, tool angles,
and materials. Due to this surface finish variation,
dimensional accuracy is very important for designers and
manufacturers of machine tools, as well as to the user.
Metal cutting is one of the most significant
manufacturing processes in the area of material removal
(Chen and Smith, 1997 [1]). 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 the workpiece. It has been recognized that the reliable
quantitative predictions of the various technological
performance measures, preferably in the form of equations,
are essential to developing optimization strategies for
selecting cutting conditions in process planning [2 – 4].
Black, 1979 [5] defined metal cutting as the removal of
metal chips from a workpiece in order to obtain a finished
product with desired attributes of size, shape, and surface
roughness. The progress in the development of predictive
models, based on cutting theory, has not yet met the
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objective; the essential cutting performance measures, such
as tool life, cutting force, the roughness of the machined
surface, energy consumption, ... etc., should be defined
using experimental studies. Therefore, further improvement
and optimization for the technological and economic
performance of machining operations depend on a well-
based experimental methodology. Unfortunately, there is a
lack of information dealing with test methodology and data
evaluation in metal cutting experiments [6].
Surface roughness plays an important role as it
influences the fatigue strength, wear rate, coefficient of
friction, and corrosion resistance of the machined
components. In actual practice, there are many factors which
affect the surface roughness, i.e., tool variables, workpiece
hardness, and cutting conditions. Tool variables include tool
material, nose radius, rake angle, cutting edge geometry, tool
vibration, tool point angle. Theoretical surface roughness
achievable based on tool geometry and feed rate are given
approximately by the formula: Ra = 0.032 f2/rε. In hard
turning, the surface finish has been found to be influenced
by a number of factors such as feed rate, cutting speed, tool
nose radius and tool geometry, cutting time, workpiece
hardness, the stability of the machine tool and the workpiece
setup [7].
Thiele and Melkote, 1999 [8] investigated the effect of
cutting edge geometry on surface roughness in finish turning
by cutting bars of steel AISI 52100 at three different
hardness values (41; 47; 57 HRC). They used low-CBN
inserts with a four edge radius. The experiments were
carried out using different feed rates (0.05, 0.10, 0.15
mm/rev) and fixed cutting speeds (121.9 m/min) at a fixed
depth of cut (0.254 mm). The authors observed that the
effect of the cutting edge hone on surface roughness
decreases with an increase in workpiece hardness. Also, they
noted the significant effect of cutting edge geometry on the
axial and radial cutting force components.
Aouici et al., 2010 [9] studied the machining of slide-
lathing grade X38CrMoV5-1 steel treated to 50 HRC by a
CBN 7020 tool to investigate the influences of cutting
parameters: feed rate, cutting speed and depth of cut on
cutting forces as well as on surface roughness. The authors
found that the tangential cutting force was very sensitive to
the variation of cutting depth. It was observed that surface
roughness was very sensitive to the variation of feed rate,
and that flank wear had a great influence on the evolution of
cutting force components and on the surface roughness.
The achievement of high quality, in terms of workpiece
dimensional accuracy, surface finish, high production rate,
less wear on the cutting tools, economy of machining in
terms of cost-saving and increase the performance of the
product with reduced environmental impact are the main and
effective challenges of modern metal cutting and machining
industries [10].
Traditionally, hardened steels are machined by grinding
process due to their high strength and wear resistance
properties but grinding operations are time-consuming and
limited to the range of geometries to be produced. In recent
years, machining the hardened steel in turning which uses a
single-point cutting tool has replaced grinding to some
extent for such application. This leads to reducing the
number of setup changes, product cost and ideal time
without compromising on surface quality to maintain the
competitiveness [11], [12].
The improve of the technological process, proper tool
selection, determination of optimum machining parameters
(cutting speed, feed, depth of cut, etc.) or tool geometry
(nose radius, rake angle, edge geometry, etc.) are necessary
in order to obtain the desired surface finish as compared to
grinding [13 –15].
The metal cutting processes used can be divided into two
types: orthogonal cutting, where the tool’s cutting edge is
perpendicular to the direction of motion, and oblique cutting
where the cutting edge forms an inclination angle relative to
the cutting direction [16]. Orthogonal cutting is not
commonly used in the industry but it is common in research
as a sort of simplification of the cutting process. A 3D-
simulation model of cutting is costly since the relatively
sharp edge of the tool requires a very fine mesh. Orthogonal
cutting can be modeled as a plane stress problem and,
therefore, is more frequently used in research [17 – 19].
Camposeco-Negrete et al., 2016 [20] utilized the Robust
Design methodology for optimizing the cutting parameters
in order to get the lowest value of energy consumed by the
machine, considering two sources of noise: the presence or
absence of cutting fluid and the machine tool used to
perform the machining operation at a constant material
removal rate.
Li and Wang 2016 [21] reviewed the state of the art
improvements in residual stresses and distortion in
machining aeronautical aluminum alloy parts. They sum up
all the generation, distribution, effecting parameters,
measurement and control of bulk residual stresses and
machining surface residual stresses and their influences on
distortion in machining aeronautical aluminum alloy parts.
Ribeiro et al., 2017 [22] used the Taguchi Method and
Analysis of Variance (ANOVA) to find the combination of
cutting speed, feed rate, radial depth of cut, and axial feed for
minimizing the surface roughness. Khare and Agarwal, 2017
[23] acquired the optimum machining parameters for the
cryogenic turning of AISI 4340 steel, for minimizing the
surface roughness based on the Taguchi method. They carried
out the experiments to investigate the effect of various
parameters versus cutting speed, feed rate, depth of cut, and
rake angle on the surface roughness.
Wang et al., 2017 [24] developed an efficient multi-scale
finite element analysis modeling method to forecast the
surface residual stresses on an actual machined surface by
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bridging the length and time scales in a turning process. Also,
they provided an experimental basis for turning Inconel 718
and surface residual stress measurement to serve the finite
element analysis simulations. Finally, they validated the
model by comparing the expected surface residual stresses
with the experimental data.
Chang, et al. 2018 [25] studied the effect of ultra-
precision turning processes parameters on the residual
stresses of aluminum 2024-T3 by utilizing the finite element
method. Also, Chang and coworkers measured the surface
residual stress by utilizing the X-ray diffraction technique.
They found that the surface residual stresses decrease with
increasing the cutting speed and reducing the radius of the
cutting tool.
Sarnobat and Raval 2019 [26] investigated the influence
of tool edge radius, the hardness of the workpiece, and
turning process factors on the surface roughness, the residuals
stresses, and nano hardness changes in the workpiece during
the hard turning of AISI D2 steel. They utilized Monte Carlo
Simulation method to obtain the sensitivity of the tri-axial
residual stresses (axial, radial, and circumferential) to the
turning process parameters. They established that the
sensitivity analysis shows that the radial and circumferential
residual stresses and surface quality are more sensitive to feed
rate while the axial residual stress has a sensitivity to depth
of cut.
Mirkoohi, et al. 2019 [27] suggested an analytical
solution to predict the surface residual stresses on the
machined workpiece during the manufacturing process. Also,
Mirkoohi and his associates utilized an inverse analysis
method to optimize the machining process parameters for
minimum residual stresses by using variance based recursive
technique. Finally, they validated their suggested models by
comparing the values of the experimental measurements of
the residual stresses and the values of the predicted residual
stresses.
Salman, et al. 2019 [28] presented an experimental
study of the influences of diverse machining parameters
(cutting velocity, feed, and depth of cut) in addition to cutting
tool geometry (cutting tool edge radius, and cutting tool
coating) on the cutting force, surface residual stresses, cutting
temperature, and surface microstructure. They used a
Taguchi technique for their experimental study. They
utilized the X-ray diffraction method for measuring the
surface residual stresses. Finally, Salman and coworkers
performed numerical simulations for the turning process by
utilizing AdvantEdge software program.
The objective of this work is to acquire the optimal
trends of machining parameters (cutting speed, and feed rate)
to minimize the surface roughness and the surface residual
stresses in the face turning process for Al2024-T351.
Experimental work is carried out on samples of AL2024-
T351 for the face turning process at different machining
parameters of the cutting speed and the feed rate with
a constant depth of cut. 3D Optical Microscope is used to
measure the surface roughness parameters for the machined
samples at different machining variables. Additionally,
a scanning electron microscope (SEM) is utilized to inspect
the surface structure of the machined surfaces. Furthermore,
3D–simulation model is developed for plane stress finite
element modeling of a pure orthogonal cutting process for
Al2024-T351 rods material at different conditions of cutting
speed and feed rate. ABAQUS/Explicit package was used to
get the surface residual stress distributions in the face turning
process at different cutting conditions. Finally, correlations
are made between surface topography and the finite element
analysis results aiming at predicting an optimal trend of
machining parameters for the quality surface finish and the
minimum surface residual stress.
2. EXPERIMENTAL WORK
2.1. Material and facing process
Aluminum alloy (Al2024-T351) cylindrical rods with an
initial diameter of 38 mm and a length of 480 mm were used
for this study. The bar was cut into four pieces each of 120
mm. Each piece was further cut into 4 samples for the
purpose of machining. The samples were classified into four
groups A, B, C, and D as shown in Table 1, where the face
turning operations with different machining parameters were
performed on each sample. The tool geometry has the
following specifications: nose radius 1 mm, approach angle
40o, clearance angle 7o, and back rake angle -5o. The
experiments were performed in the mechanical workshop of
the engineering college, Qassim University by using a center
lathe machine; as shown in Figure 1.
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Table I
Machining parameters for all samples in the face turning process
Group
No.
Sample
No.
Spindle
Speed
(rpm)
Cutting Speed
(m/min)
Constant machining
parameters
A
1 500 59.690
f = 0.15 mm/rev, and
dc = 0.2 mm
2 750 89.535
3 1100 131.319
4 1500 179.071
B
5 500 59.690
f = 0.25 mm/rev, and dc = 0.2 mm
6 750 89.535
7 1100 131.319
8 1500 179.071
C
9 500 59.690
f = 0.4 mm/rev, and dc = 0.2 mm
10 750 89.535
11 1100 131.319
12 1500 179.071
D
13 500 59.690
f = 0.5 mm/rev, and dc = 0.2 mm
14 750 89.535
15 1100 131.319
16 1500 179.071
2.2. Measuring surface topography
The surface topography of the machined samples was
measured by using an optical profiling system, manufactured
by (Contour GT-K1-3D Optical Microscope, Bruker,
Billerica, MA, USA); as displayed in Figure 2. The profiling
system utilizes a technique of streamlined interface and
intuitive workflow. It adopts white and green light
interferometry. The profiling system can perform fast three-
dimensional surface measurements from millimeter-scale to
nanometer scale with sub-nanometer resolution. The
combination of the easy measurement setup, fast data
acquisition, and small footprint allow the Contour GT-K1 to
deliver 3D surface metrology performance. Furthermore, the
roughness parameter of the eroded surface at different
machining conditions was studied through measuring its
arithmetic average of the absolute value (Ra) as recorded and
expressed by the following Eq. (1) [29]:
Fig. 1. Face turning setup on the used center lathe machine.
Ra = 1
L∫ |Z (x )|dx
L
0 (1)
Furthermore, Rq is a Root-mean-square (RMS) roughness. Rq is the average of the measured height deviations taken within
the evaluation length or area and measured from the mean linear surface. Rq is the RMS parameter corresponding to Ra [29].
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Rq = √1
L∫ Z2(x )dx
L
0 (2)
Rt (PV) is the maximum peak-to-valley height. Rt is the absolute value between the highest and lowest peaks; presented
in Figure 3 [29].
Rt = RP + RV (3)
And finally, RZ the average absolute value of the five highest peaks and the five lowest valleys over the evaluation length;
is illustrated in Figure 4 [29].
RZ =(P1+P2+⋯P5)−(V1+V2+⋯V5)
5 (4)
Fig. 2. 3D optical profiling system (the Contour GT-K1).
Fig. 3. Highest and lowest peaks [29].
Fig. 4. Five peaks-to-valleys profile roughness [29].
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3. FINITE ELEMENT ANALYSIS
3.1. Finite element model
The main objective is to simulate the cutting process by
applying the finite element method using computer program
code (ABAQUS/Explicit) to get the surface residual stress
distributions in the machining process at different cutting
conditions. The rake angle and relief angle of the cutting tool
are 8.5o and 8.5o, respectively. The discrete rigid form was
utilized to model the cutting tool whose movement was
represented by the movement of a single node, identified as
the rigid body reference node. The cutting tool meshes with
R3D4 elements. Consequently, the workpiece of Al2024-
T351 (12 mm for length x 4 mm for height) was modeled as
a 3D deformable solid extrude and meshed with the 3D stress
element type of reduced integration C3D8R elements;
Figure 5 [30]. Figure 6 defines and displays the shear
deformation zones of the orthogonal cutting model.
The simple Coulomb friction model was considered on
the whole contact zone of the cutting tool and chip interface.
The frictional stresses are assumed proportional to the normal
stresses, using a constant coefficient of friction μ. The model
is defined as:
τ = μ σn (5)
The Coulomb friction model was utilized for simulations
of surface residual stresses following Proudian [31].
Fig. 5. FEA model for single point orthogonal cutting for Al2024-T351.
Fig. 6. Shear deformation zones in Orthogonal Cutting.
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3.2. Material properties
The workpiece material aluminum alloy (Al2024-T351)
which is considered elastic-plastic Von Mises materials
throughout the study. The mechanical properties of the
cylindrical workpiece material are listed in Table 2 [32].
Table 3 shows the chemical composition of the workpiece
material Al2024-T351 [33].
Table II
Material properties of the workpiece material [32]
Material properties Value
Young’s modulus (E) 73.1 GPa Poisson’s ratio (ν) 0.33
Initial yield stress (σyo) 345 MPa
Ultimate tensile strength 483 MPa
Density (ρ) 2770 Kg/m3
The melting temperature 502 oC
Table III
Chemical composition of Al2024-T351 [33]
The content of element, wt %
Cu Mg Mn Fe Si Zn Ti Al
4.80 1.41 0.72 0.28 0.13 0.07 0.15 Balance
3.3. The material model
In metal cutting, the material undergoes rapid elastoplastic deformation under extreme conditions. To give an adequate
result the material model must be able to describe deformation behavior such as hardening and softening over great ranges of
strain, strain rate and temperature [16].
The Johnson-Cook constitutive material model, which is used in the implementation of isotropic hardening, is a common
material model for describing the thermo-visco-plastic behavior of the workpiece in a cutting process [19]. The flow stress is
formulated as a function of strain, strain rate, and temperature as can be seen in the following Equation (6) [34]:
σ = [ A + B (ϵp)n][1 + C lnϵ̇∗][1 − T∗m] (6)
ϵ̇∗ = ϵ̇p
ϵ̇o (7)
Where: ϵ̇∗ = is the dimensionless plastic strain rate for (ϵ̇o = 1.0 s-1), T* is the homologous temperature.
T* =T− Troom
Tmelt− Troom (8)
Where: Tmelt =502 °C is the melting point or solidus temperature, Troom =20 °C the ambient temperature, T °C the effective
temperature [32]. Table 4 shows the values of the five material constants (A, B, C, n, and m) for AL2024-T351 [30].
Table IV
Parameters used in the Johnson-Cook model for Al2024-T351 [35]
A (MPa) B (MPa) C n m
265 426 0.015 0.34 1
Cumulative Johnson-Cook damage is a dynamic shear failure model in ABAQUS/Explicit which is used for the chip -
workpiece separation in orthogonal cutting simulations by [36] and [37]. For the Johnson-Cook damage law the strain at failure
is given by [34]:
ϵf = [D1 + D2 exp D3σ∗][1 + D4 lnϵ∗̇][1 + D5 T∗] (9)
Depending on the variables (σ∗, ϵ∗̇, T∗). The dimensionless pressure-stress ratio is defined as:
σ∗ = σm
σ̅ (10)
Where σm is the average of the three normal stresses and σ̅ is the Von- Mises equivalent stress. The dimensionless strain
rate, ϵ∗̇ and homologous temperature, T*, are identical to those used in Equation (6). Table 5 displays the values for the five
constants (D1, D2, D3, D4, and D5) for AL2024-T351 [30].
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Table V
Johnson-Cook damage model parameters for Al2024-T351 [35]
D1 D2 D3 D4 D5
0.13 0.13 -1.5 0.011 0
The failure model is based on a calculation of damage parameter D, which is defined by the following Equation (11):
D = ∑ϵp
ϵpf (11)
This parameter (D) is updated in every FEA solving step. Elements are assumed to fail and be deleted when the damage
parameter exceeds unity [33]. Figure 7 displays the schematic representation of tensile test data in a stress-strain curve with
progressive damage degradation. This curve consists of three zones. The first zone (oa) indicates the linear elastic deformation
zone. When the stress goes above the yield stress σo the material enters the second zone (ab), at which material undergoes
stable plastic deformation. And the effect of strain hardening is predominated in this zone. When the damage parameter (D)
equal to zero as the point (b), the plastic instability initiates which turn on the third zone (bd). Afterward, material enters the
stage of failure evolution and the thermal softening takes the priority which results in the decrease of the equivalent stress.
When the stress-strain curve extends to point (d) (at D = 1), the material stiffness is fully degraded and the crack develops [25],
[34 – 36].
Fig. 7. Schematic tensile test stress-strain curve with progressive damage degradation [30], [38 – 41]
3.4. Surface residual stresses in the machined surface
Surface residual stress is the result of several mechanical
and thermal parameters, which take place in the machined
surface during the machining process [42]. Surface residual
stress can be tensile or compressive and the stressed layer can
have multiple depths, depending upon the machining
conditions, working material, cutting tool geometry, and
contact conditions at the tool/chip and tool/workpiece
interfaces. Compressive surface residual stresses generally
improve component performance and life because they
promote a service (working) tensile stresses and prevent
crack nucleation [43]. So, compressive surface residual
stresses are usually desirable on the machined surface and the
subsurface, because these stresses generally increase the
fatigue life [44]. Figure 8 shows the surface residual stresses
distribution below the machined surface.
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Fig. 8. Surface residual stresses distribution along with the depth below the machined surface [43].
4. RESULTS AND DISCUSSION Figures 9 to 12 show the 3-D surface texture profiles of the single point data acquisition at different cutting speeds and
feed rates with a constant depth of cut for Al2024-T351 machining samples.
Fig. 9. 3-D surface texture for the machined sample No. 1 from the group (A) where Ra = 1.167 µm at low cutting speed = 59.690 m/min with low
feed rate = 0.15 mm/rev.
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Fig. 10. 3-D surface texture for the machined sample No. 13 from the group (D) where Ra = 1.4993 µm at low cutting speed = 59.690 m/min with
high feed rate = 0.5 mm/rev.
Fig. 11. 3-D surface texture for the machined sample No. 4 from the group (A) where Ra = 0.6787 µm at high cutting speed = 179.071 m/min with low
feed rate = 0.15 mm/rev.
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Fig. 12. 3-D surface texture for the machined sample No. 16 from the group (D) where Ra = 0.8707 µm at high cutting speed = 179.071 m/min with
high feed rate = 0.5 mm/rev.
Generally speaking the increase of the cutting speed and
the decrease of the feed rate resulted in better surface texture
with less surface roughness (Ra = 0.6787 µm); as
demonstrated in Figure 9, Figure 10, Figure 11, and Figure
12 above. The surface parameters of the machined samples
are summarized and presented in Tables A-1 to A-4 of
Appendix A.
Figure 13 and Figure 14 present the images of the surface
texture inspection of the machined samples by utilizing a
scanning electron microscope (SEM) at low magnification.
Figure 13 (a) and Figure 13 (b) display the surface texture of
the machined samples by utilizing SEM at low cutting speed
and different feed rates. While Figure 14 (a) and Figure 14
(b) illustrate the surface texture of the machined samples by
SEM at high cutting speed and different feed rates. It’s clear
that the better surface texture is achieved at high cutting
speed and low feed rate.
(a) The low cutting speed with the low feed rate
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(b) The low cutting speed with the high feed rate
Fig. 13. SEM images (low magnification = 200x) for the surface texture of the machined samples at low cutting speed with: (a) low feed rate = 0.15
mm/rev, (b) high feed rate = 0.5 mm/rev.
(a) The high cutting speed with the low feed rate
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(b) The high cutting speed with the high feed rate
Fig. 14. SEM images (low magnification = 200x) for the surface texture of the machined samples at high cutting speed with: (a) low feed rate = 0.15
mm/rev, (b) high feed rate = 0.5 mm/rev.
Figure 15 and Figure 16 explain the images of the machined surface inspection using SEM at high magnification (5000x).
Figure 15 shows the surface texture for the machined surface at high cutting speed and low feed rate. Figure 16 displays the
surface texture for the machined surface at low cutting speed and high feed rate. From Figure 15 and Figure 16, it became clear
that a good surface finish can be obtained in the case of high cutting speed with less feeding rate.
Fig. 15. SEM micrograph (high magnification = 5000x) displays the microtexture of the machined surface at the high cutting speed and low feed rate
for Al2024-T351 (V = 179.071 m/min, f= 0.15 mm/rev, and dc= 0.2 mm).
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Fig. 16. SEM micrograph (high magnification = 5000x) displays the microtexture of the machined surface at low cutting speed and high feed rate for
Al2024-T351 (V = 59.690 m/min, f= 0.5 mm/rev, and dc= 0.2 mm).
The variation of the surface texture parameters (Ra, Rq,
Rt, and Rz) with different values of cutting speed at the
different values of feed rate with a constant depth of cut was
plotted by the response surface method (RSM) from Figure
17 to Figure 20. It is shown that as the cutting speed increases
and the feed rate decreases, the surface quality is improving
with being smoother with lower topography parameters (Ra,
Rq, Rt, and Rz). The rise in feed rate increases the heat
generation and hence, tool wear, as sensed by tool blunting
and need for sharpening which resulted in the higher surface
roughness and worse topography. The rise in feed rate also
increased the chatter and produced incomplete machining at
a faster traverse, which led to higher surface roughness and
worse topography. During the machining process, at minor
cutting speed, the large material flow and coarse chip
formation produced higher surface roughness.
Fig. 17. Surface roughness parameter (Ra) at different values of the cutting speed and feed rate with a constant depth of cut (dc = 0.2 mm).
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Fig. 18. Root mean square roughness parameter (Rq) at different values of the cutting speed and feed rate with a constant depth of cut (dc = 0.2 mm).
Fig. 19. Surface roughness parameter (Rt) at different values of the cutting speed and feed rate with a constant depth of cut (dc = 0.2 mm).
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Fig. 20. Surface roughness parameter (Rz) at various values of the cutting speed and feed rate with a constant depth of cut (dc = 0.2 mm).
It is commonly found that the absolute value of the surface
residual stress close to the surface of the workpiece is high
and decreases as you move deeper into the workpiece.
Surface residual stress can be tensile or compressive and the
stressed layer can have multiple depths, depending upon the
cutting conditions, working material, cutting tool geometry,
and contact conditions at the tool/chip and tool/workpiece
interfaces [43]. Figure 21 shows the surface residual stress
distributions of the workpiece in the face turning process at
different feed rates with constant values for the cutting speed
and the depth of cut (V = 59.690 m/min, and dc = 0.2 mm).
It is shown that the surface residual stress values during the
machining process are increasing with increasing the feed
rate at constant cutting speed and depth of cut.
Fig. 21. Effect of feed rate on the surface residual stress distributions during the face turning process at V = 59.690 m/min and dc = 0.2 mm.
Figure 22 to Figure 24 displays the surface residual stress
distributions of the workpiece in the face turning process at
different feed rates and cutting speeds with the constant value
of the depth of cut (dc = 0.2 mm). Also, it is clear that the
surface residual stress values during the machining process
are raised with rising of the feed rate at constant cutting speed
and depth of cut. Finally, it is shown that as the cutting speed
rises and the feed rate reduces the surface residual stress is
decreasing. During the face turning process, in the case of the
lower cutting speed, the large material flow, and chip
formation produced increasing surface residual stresses and
vice versa as shown in Figure 13 (a), and Figure 13 (b), Figure
14 (a), and Figure 14 (b).
-1200
-1000
-800
-600
-400
-200
0
200
0 20 40 60 80 100 120
Su
rfa
ce R
esi
du
al
stress
es
(MP
a)
Depth below the surface (µm)
f = 0.15 mm/rev
f = 0.25 mm/rev
f = 0.4 mm/rev
f = 0.5 mm/rev
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 139
202301-7878-IJMME-IJENS © February 2020 IJENS I J E N S
Fig. 22. Effect of feed rate on the surface residual stress distributions during the face turning process at V = 89.535 m/min and dc = 0.2 mm.
Fig. 23. Effect of feed rate on the surface residual stress distributions during the face turning process at V = 131.319 m/min and dc = 0.2 mm.
-1000
-800
-600
-400
-200
0
200
400
0 20 40 60 80 100 120
Su
rfa
ce R
esi
du
al
stress
es
(M
Pa)
Depth below the surface (µm)
f = 0.15 mm/rev
f = 0.25 mm/rev
f = 0.4 mm/rev
f = 0.5 mm/rev
-1400
-1200
-1000
-800
-600
-400
-200
0
200
0 20 40 60 80 100 120
Su
rfa
ce R
esi
du
al
stress
es
(M
Pa
)
Depth below the surface (µm)
f = 0.15 mm/rev
f = 0.25 mm/rev
f = 0.4 mm/rev
f = 0.5 mm/rev
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 140
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Fig. 24. Effect of feed rate on the surface residual stress distributions during the face turning process at V = 179.071 m/min and dc = 0.2 mm.
From the roughness results and FEA results, it is clear that
the manufacturer can obtain a high-quality surface finish and
less surface residual stresses by increasing the cutting speed
with reducing the feed rate.
5. CONCLUSIONS An experimental study on the effect of the machining
parameters during the face turning process on the surface
roughness of Al2024-T351 is presented. Machining
parameters such as the cutting speed and the feed rate were
investigated at a constant depth of cut. 3D high-precision
Optical Microscope is used to measure and analyze the
roughness parameters (Ra, Rq, Rt, and Rz). In addition, a
scanning electron microscope (SEM) device is utilized to
inspect the microstructural details of the surface texture for
the machined samples. Finally, a 3D finite element modeling
technique is developed to predict the surface residual stresses
at different machining conditions. The results show that:
Increasing the cutting speed decreases the roughness
parameter values (Ra, Rq, Rt, and Rz). For example,
increasing the cutting speed from 59.690 m/min
to 179.071 m/min decreased the value of Ra from 1.167
µm to 0.6787 µm at feed rate = 0.15 mm/rev and depth of
cut = 0.2 mm.
Decreasing the feed rate decreases the roughness
parameter values (Ra, Rq, Rt, and Rz). For instance,
decreasing the feed rate from 0.5 mm/rev to 0.15 mm/rev,
decreased the value of Ra from 0.8707 µm to 0.6787 µm
at cutting speed = 179.071 m/min and depth of cut = 0.2
mm.
Accordingly, it was evident that the cutting speed has a
more influential effect than the feed rate on surface
roughness parameter values (Ra, Rq, Rt, and Rz).
The finite element results are in agreement and matching
the experimental results at the optimal machining
parameters. It is also evident that increasing the cutting
speed and decreasing the feed rate decreases the
surface residual stresses. For instance, reducing the feed
rate from 0.5 mm/rev to 0.15 mm/rev, decreased the value
of surface residual stress from 1078 MPa to 482 MPa
at cutting speed = 179.071 m/min and depth of cut = 0.2
mm.
Appendix A
Tables A-1 to A-4 present the surface topography
parameters values (Ra, Rq, Rt, and Rz) respectively at different
values of the machining conditions.
-1200
-1000
-800
-600
-400
-200
0
200
0 20 40 60 80 100 120S
urfa
ce R
esi
du
al
stress
es
(MP
a)
Depth below the surface (µm)
f = 0.15 mm/rev
f = 0.25 mm/rev
f = 0.4 mm/rev
f = 0.5 mm/rev
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 141
202301-7878-IJMME-IJENS © February 2020 IJENS I J E N S
Table A-1
Surface topography measured (Ra) at different values of the machining parameters
Sample
No.
Spindle
Speed
(rpm)
Cutting
Speed
(V, m/min)
Constant
machining
parameters
Surface Topography Parameter (Ra, μm)
Reading
No. 1
Reading
No. 2
Reading
No. 3
Average
value
1 500 59.690
f = 0.15 mm/rev,
and dc = 0.2 mm
1.204 1.185 1.112 1.167
2 750 89.535 0.857 0.848 0.717 0.8073
3 1100 131.319 0.727 0.7 0.713 0.7133
4 1500 179.071 0.683 0.69 0.663 0.6787
5 500 59.690
f = 0.25 mm/rev,
and dc = 0.2 mm
1.243 1.287 1.286 1.272
6 750 89.535 1.095 0.819 1.153 1.0223
7 1100 131.319 0.817 0.892 0.911 0.8733
8 1500 179.071 0.78 0.715 0.755 0.75
9 500 59.690
f = 0.4 mm/rev,
and dc = 0.2 mm
1.322 1.41 1.395 1.3757
10 750 89.535 1.203 1.234 1.301 1.246
11 1100 131.319 1.18 0.986 0.945 1.037
12 1500 179.071 0.786 0.759 0.751 0.7653
13 500 59.690
f = 0.5 mm/rev,
and dc = 0.2 mm
1.493 1.527 1.478 1.4993
14 750 89.535 1.349 1.367 1.213 1.3097
15 1100 131.319 1.241 0.898 0.818 0.9857
16 1500 179.071 0.907 0.897 0.808 0.8707
Table A-2
Root mean square roughness values (Rq) at different values of the machining parameters
Sample
No.
Spindle
Speed
(rpm)
Cutting
Speed
(V, m/min)
Constant
machining
parameters
Root mean square roughness (Rq, μm)
Reading
No. 1
Reading
No. 2
Reading
No. 3
Average
value
1 500 59.690
f = 0.15 mm/rev,
and dc = 0.2 mm
1.566 1.547 1.466 1.52633
2 750 89.535 1.142 1.156 0.995 1.09767
3 1100 131.319 1.016 0.986 1.003 1.00167
4 1500 179.071 0.948 0.957 0.923 0.94267
5 500 59.690
f = 0.25 mm/rev,
and dc = 0.2 mm
1.608 1.675 1.655 1.646
6 750 89.535 1.43 1.102 1.515 1.349
7 1100 131.319 1.101 1.199 1.208 1.16933 8 1500 179.071 1.069 1.007 1.05 1.042
9 500 59.690
f = 0.4 mm/rev,
and dc = 0.2 mm
1.745 1.837 1.802 1.79467
10 750 89.535 1.559 1.607 1.699 1.62167
11 1100 131.319 1.529 1.319 1.265 1.371
12 1500 179.071 1.092 1.057 1.043 1.064
13 500 59.690
f = 0.5 mm/rev,
and dc = 0.2 mm
1.994 2.003 1.922 1.973
14 750 89.535 1.778 1.769 1.572 1.70633
15 1100 131.319 1.652 1.222 1.101 1.325
16 1500 179.071 1.227 1.204 1.095 1.17533
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:01 142
202301-7878-IJMME-IJENS © February 2020 IJENS I J E N S
Table A-3
Maximum peak-to-valley height values (Rt) at different values of the machining parameters
Sample
No.
Spindle
Speed
(rpm)
Cutting
Speed
(V, m/min)
Constant
machining
parameters
The maximum peak-to-valley height (Rt, μm)
Reading
No. 1
Reading
No. 2
Reading
No. 3
Average
value
1 500 59.690
f = 0.15 mm/rev,
and dc = 0.2 mm
10.489 10.434 9.506 10.143
2 750 89.535 8.279 8.362 7.427 8.02267
3 1100 131.319 7.74 7.727 7.902 7.78967
4 1500 179.071 7.372 7.799 7.452 7.541
5 500 59.690
f = 0.25 mm/rev,
and dc = 0.2 mm
10.555 11.67 10.79 11.005
6 750 89.535 10.605 8.389 11.155 10.0497
7 1100 131.319 8.329 8.815 8.752 8.632
8 1500 179.071 8.324 8.169 8.119 8.204
9 500 59.690
f = 0.4 mm/rev,
and dc = 0.2 mm
13.002 13.319 12.706 13.009
10 750 89.535 10.746 11.294 12.173 11.4043
11 1100 131.319 10.44 9.255 8.95 9.54833
12 1500 179.071 8.255 8.178 8.104 8.179
13 500 59.690
f = 0.5 mm/rev, and dc = 0.2 mm
13.72 14.577 13.449 13.9153
14 750 89.535 13.163 12.22 10.491 11.958 15 1100 131.319 10.876 9.075 8.297 9.416
16 1500 179.071 9.206 8.928 8.309 8.81433
Table A-4
Surface topography measured values (RZ) at different values of machining parameters
Sample
No.
Spindle
Speed
(rpm)
Cutting
Speed
(V, m/min)
Constant
machining
parameters
The average absolute value of roughness (RZ, μm)
Reading
No. 1
Reading
No. 2
Reading
No. 3
Average
value
1 500 59.690
f = 0.15 mm/rev,
and dc = 0.2 mm
8.013 7.911 7.363 7.76233
2 750 89.535 5.733 5.726 4.655 5.37133
3 1100 131.319 4.817 4.853 4.729 4.79967
4 1500 179.071 4.698 4.897 4.535 4.71
5 500 59.690
f = 0.25 mm/rev,
and dc = 0.2 mm
8.215 8.695 8.401 7.305
6 750 89.535 7.305 5.519 7.884 6.90267
7 1100 131.319 5.456 6.007 6.151 5.87133
8 1500 179.071 5.117 4.757 5.066 4.98
9 500 59.690
f = 0.4 mm/rev,
and dc = 0.2 mm
9.381 10.014 9.475 9.62333
10 750 89.535 8.148 8.472 9.113 8.57767
11 1100 131.319 7.949 6.672 6.449 7.02333 12 1500 179.071 5.415 5.272 5.167 5.28467
13 500 59.690
f = 0.5 mm/rev,
and dc = 0.2 mm
10.14 10.734 10.092 10.322
14 750 89.535 9.276 9.108 8.013 8.799
15 1100 131.319 8.295 6.313 5.433 6.68033
16 1500 179.071 6.306 6.149 5.539 5.998
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