Milling machining GFRP composites using grey relational analysis and the response surface methodology approach

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 613
Milling machining GFRP composites using grey relational analysis and
the response surface methodology approach
Ajeet Singh 1, Purushottam Sahu2
1Research Scholar BM College of Technology, Indore RGPV, BHOPAL
2Professor BM College of Technology, Indore RGPV, BHOPAL
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Abstract - In thisstudy, aGRA-based mathematicalapproach
is used to perform multi-functional optimization. This GRA is
used when several variables are optimized for more than one
response characteristic at the same time. The primary focus is
on solving the non-linear problem of multi-objective
optimization with limited data.
The results of the confirmationexperimentatoptimallevelsa2
b2 c1 d3 and e1 show that the machiningforce decreasedfrom
17 19 to 15 92 n, the delamination factor decreased from 1
0063 to 1 0045, and the improved from 5 288 to 5 971
mm3/min. It explains how the DOE taguchi technique can be
used to improve process outcomes in productionplanningand
analysis. The analysis was conducted to evaluate the
influences of the machining factors on the responses in order
to determine the significant effect of the parameters on the
quality characteristics of the test. This analysis was carried
out with a 5% level of significance, i.e. with a 95% level of
confidence. Surface roughness ANOVA result This study was
carried out with a 5% level of significance, i.e. witha95%level
of confidence. Surface roughness ANOVA result The Cutting
Tool (P=0.003) (47.73%%) has the greatest influence on the
machining force. Spindle speed (P= 0.048) (18.30%) was
followed by Feed rate (0.098) (13.30%) and Depth of cut
(0.136) (11.27%). Surface roughness is a significant factor in
the parameters of the current study.
Key Words DOE taguchi method, MRR, Grey relation
analysis, glass fiber reinforced polymer, surface roughness
and machining force
1. INTRODUCTION
GRA-based Taguchi optimization technique using
experimental design assists in controlling/reducing various
errors during manufacturing of GFRP composite gear.
(3) Selection of various parameters for ranking has been
done through response table. The p-value is below 0.02 and
hence it is found all the process parameters have significant
effect on the performance characteristics of GFRP. Rotary
feed, cutting speed, cutting fluid ratio and cutting fluid flow
rate are identified as key significant parameters of gear
shaper machine which control the performance
characteristics. The optimum machining parameters are
calculated as rotary feed 0.15 mm/stroke, cuttingspeed 240
strokes/min, cutting fluid ratio 12% and cutting fluid flow
rate 30 ml/min. The significant parameters effect the
performance characteristics areat96%confidencelevel.The
variation in predicted and experimental grey relational
grade is very less (1.5%) and hence the resultsarevalidated.
From the review of existing literature it is apparent that
many research have been carried out on polymer composite
gears performance based on milling machine by varying
reinforcement, process routes, gear pair combinations of
different materials but no study available on single
optimization of cutting parameters of milling machine for
minimum variation/deviation of surface roughness of tooth
which affects the noise, vibration and load carryingcapacity.
Root diameter deviation affects the root fillet radius,
responsible for tooth beam strength; Tooth thickness
variation controls the proper meshing of teeth, responsible
for noise, vibration; Surface roughness affects the friction
and life of teeth in wear.
Due to anisotropic and heterogeneous structures of
composites machining of such materials with conventional
machining processes often results in material failures such
as matrix cracking fiber pull out swelling and delamination
hole surface failure.[2]
1.1 Effect of input process parameters on machining
force and surface roughness
In milling, machining force is regarded as one of the most
important parameters that affects overall machiningandhas
a significant impact on the surface quality of the milled
surface. To achieve appropriate machining results, it is
critical to minimize machining forces; this is influenced
directly or indirectly by all milling process input variables
such as spindle speed, feed rate, and depth of cut, cuttingtool
geometry, workpiece material, tool-workpiece interface
characteristics, tool temperature, and machining vibrations.
1.2 Machining of composites
More research has been conducted in the field of machining,
which includes both traditional procedures such as turning,
milling, and drilling and unusual processes such as laser
ablation and water jet cutting.[14]

The current work focuses on investigating the effects of
spindle speed, feed rate, depth of cut, and cutting tool type,
i.e., geometry, on milled surface performance in terms of Fm
and Ra. Table 2 contains information about all of the
combinations tested, as well as the machining force and
surface roughness values for each experiment [10].
2.
Fig. 2 Types of polymers Vs composites [2]
1.2 LITERATURE REVIEW
This paper has provided a comprehensive literature review
on cfrp machining covering scientific and technological
achievements over the past 30 yr. in terms of machining
process conditions and characteristics cutting theories and
thermal/mechanical responses.
From literature minimize all the output factors during
drilling model optimization is carried out by setting the
objectives and a desirability based approachisusedtoselect
the optimum condition The objective function/optimizing
variable bounds and optimum values of the factors are
presented in table 7 a spindle speed of 1070 rpm feed of 0 1
mm/rev tool angle of 900 and tool diameter of 8 mm is
optimum conditions for drilling[16]
The results of ANN concluded that experiment no3 obtained
relatively more error than remaining the deviation between
experimental values and prediction values are found in the
range of 3 7% Depth of cut factor has moderate significance
22 92% .
The fit summary recommended that the quadratic model is
statistically significant foranalysisofdelaminationtheresult
of the quadratic model for delaminationintheformofAnova
The associated p value for the model is lower than 0 05 i e
a¼ 0 05 or 95 per cent confidence which shows that the
model is considered to be statistically significant further
factor a b c and b2 only have significant effects on
delamination.[17]
In this experimental study conducting the Anova and
considering to checking the results of the machining of
customized carbide tipped tool 2 why because it gives the
better performance among the other three tools. There was
good reliability made between the prediction and
experimental values for both of two performance
characteristics the machining force and the surface
roughness. As per taguchi optimum machining parameters
availed and according to s/n ratios in comparison with
actual parameters there was improvement of machining
force and surface roughness are 0 63db and 0 60db[18]
1.3 METHODOLOGY
1.3.1 Materials and Procedure
A total of 25 tests were scheduled. The L25 orthogonal array
architecture developed by Taguchi was used. Four critical
parameters were considered when organizing the tests:
spindle speed, feed rate, depth of cut, and type of millingtool
used in the milling process; each parameter was adjusted at
five levels. During the Taguchi study, the average value of
experimental response and its related signal-to-noise (S/N)
ratio for each run were obtained to investigate the effects of
various machining process parameters.
The S/N ratio was chosen for the Taguchi analysis because it
aids in describing the trial data's average (mean) and
variables (standard deviation). The goal of this study was to
achieve the lowest machining force and surface roughness
possible by optimizing the input process parameters. As a
result, the S/N ratio will fall into the category of "lower is
better." Experiments were carried out on a universal milling
machine with a spindle power of 10 kW and a maximum
speed of 3000 rpm. To keep the workpiece central andavoid
vibrations, a specially designed fixture was used.

Fig 3.2 Flow Chart Steps for grey relational analysis [2]

The Taguchi Orthogonal Array (OA) technique, which was
used to design the experimental layout, was used to obtain
the response values of Machining force and Surface
roughness "Ra." Experiment 24 was determined to have the
highest value of grey relational grade and was ranked first.
The factors for experiment number 24 are machining force
"Fm" (N) 20.6, surface roughness "Ra" (m) 2.88.
Hence the grey relational analysis based on taguchi method
for the optimization of the A particularly important tool is
multi-responseproblems.ForpredictingtheMachiningforce
and surface roughness.
The improvement of grey relational grade from initial
parameter combination (A1–B1–C1 D1 to the optimal
parameter combination (A5 B4 C3 D2) is found to be 0.0051
CONCLUSION
1. The spindle speed and feed rate have no effect on the SR
and Machining force of CFRP composite laminates.
2. The Grey-Taguchi method can be used to simplify and
improve the optimization of numerous common
performance criteria. According to the response table, the
maximum setting for the GRG for spindle speed is 4000rpm,
feed rate is 200 mm/min, and cut depth is 0.5mm (A5 B4 C3
D2).
3. The depth of cut is the most important factor influencing
the SR and DLF. To determine the SR and DLF, cutting depth
is a critical parameter that interacts heavily with spindle
speed.
4. Machining force ANOVA result TheCutting Tool (P=0.003)
(47.73%%) has the greatest influence on the machining
force. Spindle speed (P= 0.048) (18.30%), Feed rate (0.098)
(13.30%), and Depth of cut (0.136) (11.27%) are the next
most significant factors for surface roughness in the current
study.
5. The R -sq value is 91.51%, indicating good agreement
between the input and output relationships.Itdemonstrates
that the input and output variables have a strong
relationship. Now, the R-Sq (adj) value is 74.52%, indicating
that the data are well fitted for the new sets of variables.
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