Investigation of Surface Texture and Surface Residual ...ijens.org/Vol_20_I_01/202301-7878-IJMME-IJENS.pdf · modeling of a pure orthogonal cutting process for Al2024- T351 at

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

*[email protected]

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



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



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.



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


10 750 89.535

11 1100 131.319

12 1500 179.071

D

13 500 59.690


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].



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].



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.



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].



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.



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.



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.



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



(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



(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).



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).



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).



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





-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)


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

)


f = 0.15 mm/rev

f = 0.25 mm/rev

f = 0.4 mm/rev

f = 0.5 mm/rev




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)


f = 0.15 mm/rev

f = 0.25 mm/rev

f = 0.4 mm/rev

f = 0.5 mm/rev



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



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


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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