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OPEN
Optimization of machining
parameters while turning AISI316
stainless steel using response
surface methodology
Mulugundam Siva Surya
The chromium-nickel alloy known as AISI 316 Stainless Steel is extensively used in various industries,
such as the chemical industry, nuclear power plants, and medical devices, due to its exceptional
mechanical properties and corrosion resistance. Compared to other stainless steels, AISI 316 resists
corrosion from air and other corrosive conditions better. Determining the optimal selection of
machining parameters is still a challenge for many researchers. The Box Behnken technique (L12 array)
is used in this work to design the experiment set. Response surface methodology (RSM) is used to
examine how the cutting velocity (100, 150, and 200 m/min), feed (0.10, 0.15, and 0.20 mm/rev),
and depth of cut (02, 0.4, and 0.6 mm) affect the cutting force (Fc), surface roughness (SR), power
consumption (Pw), and tool life (T). The ANOVA investigation shows that cutting force and surface
roughness increase linearly with an increase in feed. Similarly, power consumption and tool life also
increase linearly with an increase in cutting velocity. The best combination for the lowest Fc, SR, and
Pw and the maximum T is cutting velocity at 122.37 mm/min, feed at 0.13176 mm/rev, and cut depth
at 0.213337 mm. For cutting force, surface roughness, power consumption, and tool life, the actual
and predicted values are (124.31, 129.45), (0.55, 0.57), (1.131, 1.154), and (2112, 2225), respectively.
For the lowest feasible values of Fc, SR, Pw, and maximum T, the optimal settings are a cutting speed
of 122.37 mm/min, feed of 0.13176 mm/rev, and depth of cut of 0.213337 mm.
Keywords AISI316 stainless steel, Machining parameters, Response surface methodology
Numerous technical disciplines, including machining processes, are affected by sustainable manufacturing1.
Sustainable production can be applied to turning, one of the machining processes, by taking into account
the cutting parameters and fluids used in the process, the performance of the cutting tool, the quality of the
machined surface, and the power used for cutting. The reduction of power usage is a crucial sign of sustainable
production2,3. In light of this, the essential actions to assess and reduce the power consumption during the
machining process ought to be assessed. Austenitic stainless steel has become more attractive and a part of
human life because of its complete applications in automobile and aerospace industries, attractive mechanical
properties, and high resistance to corrosion and oxidation. Due to their higher strength and reduced thermal
conductivity, these materials are challenging to machine4,5. The effects of machining due to cutting operations
with coated and uncoated tools were evident in energy utilization6. The selection of proper machining parameters
can affect surface roughness, dimensional accuracy, and power consumption, saving money and improving
sustainable performance7,8. Recent studies have come across many experimental studies investigating the effects
of machining parameters on cutting power and surface roughness during the machining of various stainless
steel forms. Taguchi, grey relational method (GRA), and ANOVA techniques are used to minimize the output
responses by selecting proper machining parameters9–11. Many researchers carried their work on the machining
of AISI 316 stainless steel and optimized output responses like cutting temperature, cutting force, tool wear,
surface roughness, and chip morphology12–14.
Lin15 investigated surface roughness variations in high-speed fine turning of different austenitic stainless
steel grades under different cutting conditions. Ranganathan and Senthilvalen16 developed a mathematical
model specifically for the process characteristics of hard turning AISI 316 stainless steel. Tool wear and surface
roughness were predicted using ANOVA theory and regression analysis. Anthony Xavior and Adithan17
investigated how different cutting fluids affected wear and surface roughness when turning AISI304 austenitic
Mechanical Engineering Department, GITAM (Deemed to be University) Hyderabad, Hyderabad, Telangana
502329, India. email: sivasurya.mtech@gmail.com
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stainless steel. Ibrahim Ciftci18 performed tests on machining AISI 304 and AISI 316 austenitic stainless steels
using CVD multi-layer coated cemented carbide tools. The results indicated that cutting speed has a substantial
impact on the.
machined surface’s surface roughness. Tool wear, tool-chip interface temperature, and surface roughness
were all studied by Cebeli Ozeki et al.19 in order to shut off AISI 304. Numerous scholars have conducted studies
on various grades of stainless steel, with AISI316 austenitic stainless steel receiving less attention, according
to the available literature. The machining procedures have been the subject of prior studies that measured the
machining responses and varied the cutting conditions. However, as turning continues, power usage deserves
more significant consideration. Some studies have suggested that by altering the cutting conditions during
turning, power consumption can be included as one of the machining responses to consider while conducting
a machinability study. Research has been done to determine how machining parameters affect the machining
of AISI 316 SS, according to the literature. However, most research investigations examined how machining
factors affected the surface roughness (SR) of AISI 316 SS. Minimal research has examined how machining
parameters affect MRR and attempts to maximize the MRR when turning AISI 316 SS. More research must be
performed to optimize the machining process parameters for various output reactions, including MRR, as it is
a highly important response from a productivity standpoint.To broaden the research and to establish fruitful
conclusions on the machining of AIS 316 stainless steel, the authors present an investigation of the CNC milling
of AISI 316 stainless steel using coated carbide tools. The method utilized in this work presents a systematic
approach for experimentally examining the impact of critical machining parameters, including cutting speed
(Vc), feed rate (F), and depth of cut (D), on the cutting force, power consumption, surface roughness and tool
life using a suitable design of experiment technique, namely RSM (response surface methodology) based box
Box–Behnken design. It also displays appropriate statistical methods (ANOVA) and a straightforward way of
optimizing several responses. Thus, the present work significantly contributes to the knowledge domain of AISI
316 stainless steel machining.
Experimentation
Austenitic stainless steel (AISI 316) with a length of 200 mm and diameter of 30 mm is used as a workpiece, as
shown in Fig. 1 for machining, which offers excellent strength and high resistance to corrosion and oxidation.
The turning experiments were performed on an ACE Micromatic programmable CNC lathe machine, as shown
in Fig. 2. Table 1 shows Austenitic Stainless Steel’s chemical composition and mechanical and physical properties.
CNMG120408MS WS25PT TiCN coated carbide tool is used for machining AISI 316 alloy. These inserts were
clamped mechanically on a rigid tool holder. The tests were carried out under ISO 3685 as far as possible. It is a
hard material used for studying machining materials such as carbon steel or stainless steel. Carbide tools can also
withstand higher temperatures than standard high-speed steel tools.
Fig. 1. AISI 316 stainless steel.
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Fig. 2. ACE Micromatic programmable CNC lathe machine.
Property
Value
Chemical composition
Cr:16–18%, Ni:10–14%, Mn:2%, Mo:2–3%, Si:1%, C:0.08%
Yield strength (Pa)
290 × 106
Tensile strength (Pa)
580 × 103
Hardness (HB)
140–160
Density(g/cc)
8
Poisson’s ratio
0.25
Modulus of elasticity (Pa)
193 × 109
Table 1. Chemical and mechanical properties of austenitic stainless steel (AISI 316).
Parameters
Units
L-1
-2
L-3
Cutting velocity (Vc)
m/min
100
150
200
Feed (F)
mm/rev
0.10
0.15
0.20
Depth of cut (D)
mm
0.2
0.4
0.6
Table 2. Selected machining parameters and their levels.
The cutting conditions were chosen based on the recommendations suggested by the tool’s manufacturer.
The machining parameters selected for the current investigation, cutting velocity, feed, and depth of cut at three
different levels, are shown in Table 2.
A digital microscope (Zeiss Stemi 200-C) was used to measure the advancement of tool wear at each
predetermined cutting time until the tool satisfied one of the tool life requirements. To reduce mistakes, the
measurements were taken without taking the tool out of its holder. A maximum flank wear width of 0.1 mm
or catastrophic failure were the conditions for the tool life. The Talysurf instrument (Matsutoyo Corporation,
model 178-561-02 A) was used to quantify surface roughness (Ra). Each measurement had a 0.8 mm cut-off
length and a 4 mm sample length. A force dynamometer (Kistler 9265B) connected to a multichannel amplifier
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was used to record the cutting forces during the turning process. This amplifier converted the dynamometer’s
output signal into a force that could be read in three directions. By positioning three portable power meters
(Omron ZNCTX21) at the spindle drive, axis drive, and main power, power consumption was determined.
The manner the data was analyzed for the results allowed for the quantitative measurement of the impact of the
independent factors (feed and cutting speed) on the dependent variables (cutting force (Fc), surface roughness
(SR), power consumption (Pw), and tool life (T). Regression analysis was employed to create mathematical
models for the varied answers. All possible combinations of the input variables at each of the three levels were
described in a three-level full factorial design.
The Expanded Taylor’s tool life equation evaluates the parameters, i.e., VTnFaDb=C. The exponents a and
b will be determined experimentally for each combination of cutting conditions. In practice, typical values for
carbide tools and stainless steel as workpieces are n = 0.33, a = 0.6, b = 0.15, and c = 80.
The mathematical formulas used in calculating different parameters are as follows20,21.
(i) Power Required : P = (K x D x V x F) / (60 ∗ 1000)(1)
The method to calculate the power consumption is to multiply the Metal Removal Rate (MRR) by the Specific
Cutting Force (K).
Specific Cutting Force (K): A material property that indicates the required force needed to extract a chip out
of the workpiece.
K = Ka × CT − µ × (1 − 0.01 × A)(2)
Ka = NORMALIZED SPECIFIC CUTTING FORCE (FROM VALUE CHART)
µ = SLOPE OF KC GRAPH
A = TOP RAKE RANGLE (+ 7◦ )
CT = CHIP THICKNESS
CT = Fr = FEED
Ka: Each material has a specific Cutting Force coefficient that expresses the force in the cutting direction
required to cut a chip area of one square mm that has a thickness of 1 mm with a top rake angle of 0°.
Metal removal Rate (MRR): The MRR is the Volume removed in the machining Process per Machining Time.
Where Volume removed is = (Initial weight of workpiece - Final weight of workpiece)/density of workpiece.
Assuming the input values are in mm and K in Mpa (N/mm2), the result should be divided by 60,000 to get
the power in kW.
(ii) Cutting Force : F = (Kx D xF)(3)
Results and discussion
Table 3 presents the box–behnken L12 design based on response surface methodology (RSM). Surface roughness,
cutting force, tool life, and power consumption are all measured for every experiment. Utilizing the box-behnken
rules for three input components at three levels, each with two repeats at the center point, an empirical model
was constructed for every machining response. These rules have previously been decided upon and recorded in
another source. For every model, an analysis of variance (ANOVA) was done to determine the significance of the
model and its coefficients. The significance criterion was calculated using a 95% confidence interval (Prob > F
to be maximal at 0.05).
Cutting force
Table 4 displays the results for cutting force at different cutting velocities, feeds, and depth of cuts, which fit the
linear model. Table 5 provides the ANOVA for the cutting force data. Prob > F of much less than 0.01 indicates
S.No
Vc
F
D
Fc (N)
SR (µm)
Pw(kW)
T(s)
1
100
0.1
0.4
134.56
1.15
1.073
3992
2
200
0.1
0.4
129.78
1.01
1.299
1710
3
100
0.2
0.4
223.5
2.79
0.987
2505
4
200
0.2
0.4
201.63
2.24
1.452
934
5
100
0.15
0.2
193.49
1.2
0.965
2845
6
200
0.15
0.2
175.12
1.1
1.452
1052
7
100
0.15
0.6
196.42
1.98
0.983
2845
8
200
0.15
0.6
181.39
1.12
1.376
1023
9
150
0.1
0.2
138.23
1.12
0.773
3786
10
150
0.2
0.2
185.23
1.51
1.292
1550
11
150
0.1
0.6
143.54
1.21
0.891
3894
12
150
0.2
0.6
213.67
2.63
1.712
1521
Table 3. Experimental results.
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Source
Sequential
p-value
Adj R2
Pred R2
Linear
20 × 10− 2
86 × 10− 2
77 × 10− 2
2FI
78 × 10− 2
81 × 10− 2
52 × 10− 2
Quadratic
19 × 10− 2
89 × 10− 2
55 × 10− 2
Insufficient fit p-value
Suggested
Aliased
Table 4. Suggested model for cutting force.
Source
SS
dof
MS
F-value
p-value
Model
10336.28
3
3445.43
23.64
0.0002
A-Cutting velocity
450.75
1
450.75
3.09
0.1167
B-Feed
9654.94
1
9654.94
66.25
< 0.0001
C-DOC
230.59
1
230.59
1.58
0.2439
Residual
1165.85
8
145.73
Cor total
11502.13
11
Significant
Table 5. ANOVA table for cutting force.
that the linear model is valid. Only the feed was regarded as a major element in the coefficients. The change in
cutting speed has no effect on the cutting force. Equation (1) states the cutting force (Fc) empirical equation that
was produced in the form of an actual factor.
Cutting force = + 83.94125
− 0.150125 Vc + 694.80000 F + 26.84375 D(4)
In Fig. 3, the equation is shown as a three-dimensional surface, although it can also be shown as a response
surface contour for convenience. The amount of uncut chip thickness and the feed’s impact on the cutting force
are connected. The cutting force increases naturally with feed level. An investigation on turning the hardened
steel supported the idea that Fc is the primary force operating on the tool’s rake face. The cutting speed does not
affect the cutting force since the size of the uncut chip thickness is independent of the cutting speed. According
to earlier research on other workpiece metals22,23, this is accurate.
Surface roughness
Table 6 displays the results for surface roughness at different cutting velocities, feeds, and depth of cuts, which
fit the linear model. Table 7 provides the ANOVA for the cutting force data. Prob > F of much less than 0.01
indicates that the linear model is valid. It was discovered that the only factor influencing surface roughness is
the feed. Equation (2) represents the surface roughness (SR) empirical equation that was produced in the form
of an actual factor.
Surface roughness = − 0.050417 − 0.004125 Vc + 11.70000 F + 1.25625 D(5)
where f represents the feed and SR stands for surface roughness. Figure 4 shows the contour of the response
surface and the 3D surface for Eq. (3). When turning at the chosen cutting parameters, the resultant SR was
primarily within the finish turning range of 0.7–1.5 mm24,25, mainly when the feed was 0.15 or less. The stainlesssteel workpiece exhibited the anticipated behavior, demonstrating a surface roughness that increases with feed.
In theory, nose radius and feed determine surface roughness. Because of their proportionality, the SR value was
unaffected by the cutting speed. Cutting speeds may be within the range of low cutting speeds or the material is
considered if the non-significant effect of cutting speed on surface roughness is observed.
Power consumption
Table 8 displays the results for power consumption at different cutting velocities, feeds, and depth of cuts, which
fit the linear model. Table 9 provides the ANOVA for the data on power use. Prob > F of much less than 0.01
indicates that the linear model is valid. It was discovered that the only factor influencing surface roughness is the
feed. Figure 5 shows the contour of the response surface, and the 3D surface for Eq. (3) represents the empirical
equation obtained for power consumption as an actual factor.
Power Consumption = − 0.048833 + 0.003938 Vc + 3.51750 F + 0.300000 D(6)
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Fig. 3. Response surface graphs for cutting force.
Source
Sequential
p-value
Adj
R2
Pred
R2
Linear
5 × 10− 3
069 × 10− 2
049 × 10− 2
2FI
36 × 10− 2
72 × 10− 2
28 × 10− 2
Quadratic
16 × 10− 2
86 × 10− 2
40 × 10− 2
Insufficient fit p-value
Suggested
Aliased
Table 6. Suggested model for surface roughness.
Source
SS
dof
MS
F-value
p-value
Model
3.58
3
1.19
9.30
0.0055
A-Cutting velocity
0.3403
1
0.3403
2.65
0.1423
B-Feed
2.74
1
2.74
21.31
0.0017
C-DOC
0.5050
1
0.5050
3.93
0.0827
Residual
1.03
8
0.1285
Cor total
4.61
11
Significant
Table 7. ANOVA table for surface roughness.
It is well known that the motor requires more power to revolve the spindle at a faster spindle speed. The motor
needs more power to keep the spindle speed at the predetermined level when cutting occurs. It’s interesting to
remember that earlier research revealed that other cutting parameters, in addition to cutting speed, can affect
power consumption. The findings showed that the cutting speed and feed had a substantial impact on the dryturning process’s power usage. It has been previously established for turning steels and various other workpiece
materials26,27 that the relationship between the input factor and the machining reaction is proportionate, as
predicted. Cutting speed and feed have a considerable impact. However, they can be prioritized according to
importance. According to Table 9, cutting speed still has a greater F value than feed, indicating that it still has a
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Fig. 4. Response surface graphs for surface roughness.
Source
Sequential
p-value
Adj
R2
Pred
R2
Linear
02 × 10− 2
0.5405
0.2481
2FI
85 × 10− 2
36 × 10− 2
− 06 × 10− 2
Quadratic
98 × 10− 2
− 04 × 10− 2
− 3.5 × 10− 2
Insufficient fit p-value
Suggested
Aliased
Table 8. Suggested model for power consumption.
Source
SS
dof
MS
F-value
p-value
Model
0.5848
3
0.1949
5.31
0.0263
A-Cutting velocity
0.3085
1
0.3085
8.41
0.0199
B-Feed
0.2475
1
0.2475
6.74
0.0318
C-DOC
0.0288
1
0.0288
0.7850
0.4015
Residual
0.2935
8
0.0367
Cor total
0.8783
11
Significant
Table 9. ANOVA table for power consumption.
dominant effect. This is consistent with a study by Bhattacharya et al. that found 77.4% of the power usage was
caused by cutting speed. However, as feeds have a substantial impact on power consumption as well, they should
be taken into account when AISI 316 L power consumption is meant to be reduced.
ToolLife
Table 10 illustrates how the tool life fits the linear model for a range of cutting velocities, feeds, and depth of cuts.
Table 11 displays the results of the ANOVA for the tool life data. The linear model is viable because its Prob > F
is significantly smaller than 0.01. It was discovered that the tool life is greatly impacted by cutting velocity and
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Fig. 5. Response surface graphs for power consumption.
Source
Sequential
p-value
Adj
R2
Pred
R2
Linear
0.0007
81 × 10− 2
70 × 10− 2
2FI
94 × 10− 2
73 × 10− 2
29 × 10− 2
Quadratic
23 × 10− 2
82 × 10− 2
24 × 10− 2
Insufficient fit p-value
Suggested
Aliased
Table 10. Suggested model for tool life.
Source
SS
dof
MS
F-value
p-value
Model
1.287E + 07
3
4.292E + 06
17.66
0.0007
A-Cutting velocity
6.971E + 06
1
6.971E + 06
28.68
0.0007
B-Feed
5.903E + 06
1
5.903E + 06
24.28
0.0012
C-DOC
312.50
1
312.50
0.0013
0.9723
Residual
1.945E + 06
8
2.431E + 05
Cor total
1.482E + 07
11
Significant
Table 11. ANOVA table for tool life.
feed. Figure 6 shows the contour of the response surface and the 3D surface for Eq. (4) presents the empirical
equation of the tool life (T) as an actual factor obtained.
Tool life = 7569.75000 − 18.67000 Vc − 17180.00000 F + 31.25000 D(7)
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Fig. 6. Response surface graphs for tool life.
Lower cutting speeds and feed rates resulted in extended tool life. Considering that higher tool wear
progression results from both input components, the trend is as anticipated. These findings are consistent with
other research, which found that cutting speed has a more significant impact on tool life than feeds28,29.
Optimization
If one wants a certain set of machining responses, the best cutting parameters are found using the empirical
equations of the machining responses. To minimize cutting force, power consumption, and surface roughness
and to maximize tool life, the best cutting speed and feed must be determined. Figure 7 shows the graphical
optimization of the so-called desirability plot. A cutting speed of 199.865 m/min, feed of 0.10 mm/rev, and cut
depth of 0.21 mm are the ideal cutting parameters for such a target30,31.
Conformation test
The optimal set of cutting parameters for Cutting force (Fc), Surface roughness (SR), Power consumption (Pw),
and Tool life (T) that were identified from the RSM are verified through experimentation to confirm the efficacy
of the model. Combining a cutting velocity of 122.37 mm/min, feed of 0.13176 mm/rev, and depth of cut of
0.213337 mm yields the best results for minimal Fc, SR, and Pw and maximum T. Table 12 presents a comparison
of the expected and experimental results. Surface roughness, cutting force, power consumption, and tool have
percentage errors of 4.13, 3.64, 2.03, and 5.35 when utilizing RSM.
Conclusions
With the use of a coated carbide tool, AISI 316 L austenitic stainless steel was turned. Cutting rates of 100, 150,
and 200 m/min, 0.10, 0.15, and 0.20 mm/rev, and 0.2, 04, and 0.6 mm, respectively, were used to adjust the
feed and depth of cut. RSM was employed in the experiment design process. According to the results of the
ANOVA study, feed is correlated with cutting force and surface roughness, whereas cutting velocity is associated
with power consumption and tool life. This demonstrates that machinability research can incorporate power
consumption, which is a crucial component of sustainable manufacturing and other machining reactions. The
best combination for the lowest Fc, SR, and Pw and the maximum T is cutting velocity at 122.37 mm/min, feed at
0.13176 mm/rev, and cut depth at 0.213337 mm. For cutting force, surface roughness, power consumption, and
tool, the percentage errors using RSM are 4.13, 3.64, 2.03, and 5.35, respectively. For the lowest feasible values of
Fc, SR, Pw, and maximum T, the optimal settings are a cutting speed of 122.37 mm/min, feed of 0.13176 mm/
rev, and depth of cut of 0.213337 mm.
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Fig. 7. Desirability graphs showing the input factors for maximum tool life, minimum cutting force, surface
roughness, and power consumption.
Method
RSM
Responses
Cutting
velocity
Feed
DOC
Actual
Predicted
%Error
Cutting force
122.37
0.132
0.21
124.31
129.45
4.13
Surface roughness
122.37
0.132
0.21
0.55
0.57
3.64
Power consumption
122.37
0.132
0.21
1.131
1.154
2.03
Tool life
122.37
0.132
0.21
2112
2225
5.35
Table 12. Conformation test.
Data availability
All data generated or analyzed during this study are included in this article.
Received: 23 July 2024; Accepted: 4 November 2024
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Acknowledgements
GITAM UNIVERSITY, HYDERABAD.
Author contributions
Mulugundam Siva Surya: Conceptualization, Methodology, Experimentation, Result Analysis, Writing- Original draft preparation.
Declarations
Competing interests
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to M.S.S.
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