The paper analyzes the effect of cutting parameters on surface roughness when turning Inconel 718 under minimum quantity lubrication conditions using an aluminum oxide nanolubricant. Taguchi experimental design was used to determine the optimal levels of cutting speed, depth of cut, and feed rate. The results identified a combination of 70 m/min, 0.05mm, and 0.05 mm/rev respectively as producing the best surface finish. However, the paper lacked details on variable selection and interactions. It was also criticized for technical writing inconsistencies and considering the data analysis as optimization rather than analysis.

1. By:
1. Youssef Negmeldin 201920543
2. Mohamed hosny 202010010
3. Mina Maher rashed 201920029
4. Mohamed Ramadan Megahed 202110136
5. Ahmed Abdelftah Ibrahem 202110086
6. Ahmed Saad Abdelhamid Ibrahim 202110082
Cairo University -Faculty of Engineering
Mechanical Design & Production Engineering Department
Statistical Analysis and Design of Experiments - MDP 660 -
Fall 2021
Professor Mohamed H. Gad Allah
Conference Paper

2.  Introduction
 Experiment procedures
 Results and analysis
 Conclusion
 Criticism and correction
• Youssef Nagmeldin
• Mohamed Hosny
• Mina Maher
• Mohamed Ramadan
• Ahmed Saad

3.  Into. to Objective :
1. Environmental concerns -taking the efforts to reduce the negative impacts –
2. Inconel 718 is a nickel-based alloy commonly used due to its excellent mechanical
properties at high temperatures and its elevated corrosion resistance. This material
however is difficult to machine due to the high temperature generated during
machining
3. The purpose of lubrication in machining is to prolong the life of mechanical tools,
reduce friction, flush away chips and slow down the wear of cutting tools.
4. Minimum quantity lubrication system (MQL) minimizes the usage of cutting fluids
during machining. The technique improves tool life and increases surface quality
while minimizing waste disposal at the same time
5. Nanofluids or Nano lubricants offers better thermal stability . Nano lubricants are
produced by dispersing small amounts of nano-sized (less than 100 nm) results a
greater reduction in friction and wear than base oil lubricants on its own.

4.  Currently, there are limited number of studies on the application of MQL using
Nano lubricant with added surfactant in the machining of hard-to-machine
materials.
 the objective of this paper is to optimize (analyse) the cutting parameters of
surface roughness when turning Inconel 718 by using Taguchi method under
surfactant-added minimum quantity Nano lubrication condition.

5.  Aluminum oxide nanoparticles, which
is commercially available and supplied
by Sigma Aldrich, were used for
preparing the nanolubricant with the
soluble cutting oil. The nanoparticles
have a particles size of <50 nm.
Ultrasonics liquid processor was used
to mix the particles in SolCut for a
period of 4 hours with 25 % amplitude.
1% of sodium dodecyl benzene
sulfonate or SDBS was added to the
lubricant in order to minimize
agglomeration in the mixture
(a) Preparation of Al2O3 Nanolubricant using
sonication process, (b) Experiments
conducted by using 3-axis CHEVALIER FCL-
608 CNC Turning Machine, (c) UNIST
Minimum quantity lubricant device.

9.  The aim of this experiment is to investigate the optimum cutting parameters of
surface roughness when turning Inconel 718 by using Taguchi experimental
design approach. The experimental runs were carried out in accordance to L9
Orthogonal Array design of experiment which consists of three independent
variables, namely; cutting speed, v, depth of cut, a, and feed rate, f at three levels;
Level 1, Level 2 and Level 3. In this experiment, smaller values of surface
roughness are preferred for the optimum cutting conditions. While Table 2 and 3
shows the cutting parameter details and L9 Taguchi orthogonal array
experimental design respectively.

10. T=102.5213

11.  SS A=
(𝐴1)2
𝑛
+
(𝐴2)2
𝑛
+
(𝐴3)2
𝑛
−
(𝑇)2
𝑁
A1=13.850+9.816+6.745=30.4113
A2=12.765+7.808+14.846=35.419
A3=12.765+14.289+12.505=36.691
SS A=
(30.4113)2
3
+
(35.419)2
3
+
(36.691)2
3
−
(102.5213)2
9
=7.349
SS B=
(𝐵1)2
𝑛
+
(𝐵2)2
𝑛
+
(𝐵3)2
𝑛
−
(𝑇)2
𝑁
B1=13.850+12.765+12.765=36.602
B2=9.816+7.808+14.289=31.913
B3=6.745+14.846+12.505=34.096
SS B=
(36.602)2
3
+
(31.913)2
3
+
(34.096)2
3
−
(102.5213)2
9
=3.529
SS T= (13.850)2
+ (12.765)2
+ (12.765)2
+ (9.816)2
+ (7.808)2
+ (14.289)2
+ (6.745)2
+ (14.846)2
+ (12.505)2
−
(102.5213)2
9
 SS T= 68.651
SS C=
(𝐶1)2
𝑛
+
(𝐶2)2
𝑛
+
(𝐶3)2
𝑛
−
(𝑇)2
𝑁
C1=13.850+14.846+14.289=42.985
C2=9.816+12.765+12.505=35.086
C3= 6.7455+ 7.808+9.816 =24.45
SS B=
(42.985)2
3
+
(35.086)2
3
+
(24.45)2
3
−
(102.5213)2
9
= 57.677

12. F2,2,90%=9.0
F2,2,95%=19.0
F2,2,99%=99.0

16.  Table 4 shows the values of surface roughness obtained from the Taguchi L9
Orthogonal Array experimental runs. Regardless of the category of the performance
characteristics, higher value of S/N ratio corresponds to better performance.
Therefore, the optimal level of the process parameters is the level with the greatest
S/N ratio. The difference between maximum and minimum values of S/N ratio are
shown in S/N ratio table. 230, while the maximum value of S/N ratio for both factors B
and C occur at level 1 at 12. Therefore, the combination of parameters for the better
surface finish can be observed at level A3, B1 and C1 (70 m/min, 0. Based on the
rank observed in table 6, it can be implied that the feed rate is the factor that
influences surface roughness most, followed by the cutting speed and the depth of cut
respectively. The delta value and the steep slope in figure 2 clearly shows that the
feed rate is the dominant factor influencing the value of surface roughness. This is
because, the rise of friction and contact between the workpiece and tool interface, will
eventually increases the temperature in the cutting zone. The changes of cutting tool
geometry due to friction and heat will affect the quality of surface finish [9].

17.  Investigation of values of cutting parameters that acting on surface roughness
 Optimal combination of parameters with nano lubricant by using MQL method
 Reach the best values of cutting parameters 70m/min cutting speed 0.05mm
depth of cut and 0.05 mm/rev feed rate
 Reduce coolant consumption and promote sustainable manufacturing with low
cost

18. The paper authors :
 Poor technical writing consistency, and concise more than required. (MQL
abbreviation)
 Did not consider variables interactions or mention them
 Using Fractionation without mentioning the FFD or justifying.
 Did not justify them variables selection.
 Did not justify their selection of number of levels, the level range, or using non-equally
spaced level. (variable 3)
 in spite of the last 2 points, the SS of error in ANOVA table is minor to the SS of
variable, which may imply for results uncertainty.
 Using ANOVA without commenting on the significance on variables @ Certain
Confidence level. (contribution)
 Considering the data analysis as optimization. (optimal setting)
missed
Literatur
e review

19.  Poor technical writing consistency, not concise abstract and the body is concise
more than required.
Correction
• Define an MQL once and then refer to it without repeating it
• Remove the details of the experiment from the abstract
• Added value to literature is missing and should be added

20.  Did not consider variables interactions or mention them
Correction
• Should justify the non mention of interactions or taking into account
variables interactions then making a REVISED ANOVA

21.  Using Fractionation without mentioning the FFD or justifying.
Correction
3^3 = L27 OA & the fractionation is L9 OA ( 1/3 FFD)

22.  Did not justify the variables
selection.
 Cutting speed, v (m/min),
 Depth of cut, a (mm),
 Feed, f (mm/rev),
Correction
It was necessary to talk about all the variables and determine what will
be is studying upon literature review ( that he didn’t make it )

23.  Did not justify their selection of number of levels, the level range, or using non-equally
spaced level. (variable 3)
Correction
• Determine the reason for choosing 3 levels, is it a Commercial reason ? or a reason due to
the power of the machine? or previous experience? Or upon literature review
• using equally spaced level for feed rate ;- 0.05 , 0.10 , 0.15

24.  in spite of the last points, the SS of error in ANOVA table is minor to the SS of the variable,
which may imply results uncertainty.
Correction
The small error might make the reader doubt about the certainty of the results,
so it's better to consider other factors and equidistant level variables and then remove the
least significant variable and construct a revised ANOVA table

25.  Using ANOVA without commenting on the significance on variables @ Certain Confidence level.
Correction
The significance variables @ Certain Confidence level must be stated by using the F table
F2,2,90%=9.0
F2,2,95%=19.0
F2,2,99%=99.0

26.  Considering the data analysis as optimization. (optimal setting)
Correction
• Care must be taken in choosing the words, as it does not perform any Optimization method, it is only the work of data
analysis
• optimization is to achieve the “best” design relative to a set of constraints. These include maximizing or minimizing values
of factors
• Optimal and optimum both mean “best possible” or “most favorable.”
> Rewrite the Paper title: Analyze the effect of Cutting Parameters for Surface Roughness under MQL, using Al2 O3
Nanolubricant, during Turning of Inconel 718