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SURFACE ROUGHNESS PREDICTION BASED ON CUTTING
PARAMETERS FOR TURNING OPERATION- REVIEW 1Ashish P. Gamit,
2Bhavesh V. Patel,
3Mehul Chaudhari
1, 2 Lecturer, Department of Mechatronics Engineering, B & B Institute of Technology, V V Nagar, Gujarat,
India 3Assistant Professor, Department of Mechanical Engineering, Knowledge Institute of Technology &
Engineering, Bakrol, Gujarat, India
ABSTRACT:
Due to the widespread use of highly automated machine tools in the industry,
manufacturing requires reliable models and methods for the prediction of output performance
of machining processes. The prediction of optimal machining conditions for good surface
finish and dimensional accuracy plays a very important role in process planning. The present
research deals with the study of different types of surface roughness prediction models used
in turning. In machining operation, the quality of surface finish is an important requirement
for almost all turned work pieces.
The goal of modern machining industries is mainly focused on achieving high quality,
in term of part/component accuracy, surface finish, high production rate and increase the
product life with lesser environmental impact. The surface roughness obtained depends on
the cutting tool, the cutting conditions, the machine characteristics, the surrounding vibrations
and the work piece material. In this way to get the best results of surface roughness, it is
important to control the process parameter in any manufacturing procedure. From the
Optimization techniques and surface roughness models it is found that the greatest influence on the
surface roughness is exhibited by the feed rate, followed by depth of cut and cutting speed.
KEYWORDS: Surface Roughness, Cutting parameters, Genetic Algorithm, Response
Surface Methodology, Artificial Neural Networks, Multi Regression Analysis, ANOVA
INTRODUCTION
The biggest challenge of modern machining industries is mainly focused on the
achievement of high quality, in term of work dimensional accuracy, surface finish. The
quality of a surface is significantly important factor in estimating the productivity of machine
tool and machined parts. Due to the increasing demand of higher precision components for its
functional aspect, surface roughness of a machined part plays an important role in the modern
manufacturing process. In metal cutting and manufacturing industries, surface finish of a
product is very crucial in determining the quality. Good surface finish not only assures
quality, but also reduces manufacturing cost, operation time, assembly time and leads to
overall cost reduction. Besides, good-quality turned surface is significant in improving
fatigue strength, corrosion resistance, and creep life.
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The surface roughness also effects on some functional attributes of parts, such as,
contact causing surface friction, wearing, light reflection, ability of distributing and also
holding a lubricant, load bearing capacity, coating and resisting fatigue. There are many
factors which affect the surface roughness and material removal rate (MRR) i.e. cutting
conditions, tool variables and work piece variables. Cutting conditions include speed, feed
and depth of cut and also tool variables include tool material, nose radius, rake angle, cutting
edge geometry, tool vibration, tool overhang, tool point angle etc. and work piece variable
include hardness of material and mechanical properties. It is very difficult to take all the
parameters that control the surface roughness and material removal rate for a particular
process. In a turning operation, it is very difficult to select the cutting parameters to achieve
the high surface finish and material removal rate. Therefore, the desired surface finish is
usually specified and the appropriate processes are selected to reach the required quality.
In the field of manufacturing, especially In engineering, the exact degree of roughness
can be of considerable importance, affecting the functioning of a component, and possibly its
cost. Therefore, we need to determine or predict, in numerical terms, how rough a surface
will be.
LITERATURE REVIEW
Zahia Hessainia et al. [1]
investigated the effects of cutting parameters and cutting tool
vibrations on surface roughness parameters, and established correlation between them. They
experimented on 42CrMo4 hardened steel by Al2O3 mixed ceramic cutting tool and used
Response Surface Methodology (RSM)-to find optimum values of cutting parameters and
tool vibration. ANOVA-for data analysis and to find combined effects of cutting parameters
and tool vibration on surface roughness. They found that, the feed rate and cutting speed
affecting largely on surface roughness, whereas vibrations have a low effect on it. RSM
combined with factorial design of experiment is useful for predicting machined surface
roughness with very less number of experiments.
Vikas Upadhyay et al. [2]
performed Turning of Ti–6Al–4V alloy using uncoated cemented
carbide inserts to determine whether only vibration signals can be used in in-process
prediction of surface roughness.
In the first stage, only acceleration amplitude of tool vibrations in axial, radial and tangential
directions were used to develop first and second order multiple regression models were
developed, but they were not found accurate enough (maximum percentage error close to
24%). In the second stage, initially a correlation analysis was performed to determine the
degree of association of cutting speed, feed rate, and depth of cut and the acceleration
amplitude of vibrations in axial, radial, and tangential directions with surface roughness.
Subsequently, based on this analysis, feed rate and depth of cut were included as input
parameters aside from the acceleration amplitude of vibrations in radial and tangential
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directions to develop a refined first order multiple regression model for surface roughness
prediction. This model provided good prediction accuracy (maximum percentage error
7.45%) of surface roughness. Finally, an artificial neural network model was developed and
found suitable for in-process prediction of surface roughness.
K.A.Risbood et al. [3]
found that, using neural network, surface finish can be predicted
within a reasonable degree of accuracy by taking the acceleration of radial vibration of tool
holder as a feedback
Different Neural network models were generated by carrying out a number of experiments
involving dry and wet turning of mild steel rods using HSS and Carbide tools and found that
Increased cutting speed and presence of coolant helps in improving the surface finish,
whereas increased feed, depth of cut and vibrations deteriorate the surface finish. With TiN
coated carbide tool, surface finish improves with increasing feed up to some feed where from
it starts deteriorating with further increase of feed. This type of behaviour is not observed in
turning with HSS tool. It was considered that less than 20% error is reasonable, With HSS
tool, maximum error in the prediction is 18.21%, while with TiN coated carbide tool it is
within 20% except in two cases.
O. B. Abouelatta and J. Madl [4]
derived correlation between surface roughness and cutting
vibrations in turning of Mild Carbon Steel using cemented carbide tool. They use FFT
analyzer with accelerometer for tool vibration measurement, Surtronic 3+ measuring
instrument for surface roughness measurement and MATLAB, BC++ and SPSS statistical
software package to analyze the collected measured results. Four models were used to predict
the roughness parameters as function of cutting parameters and tool vibration parameters.
From all mathematical models and analysis they finds that the predicted models that depend
on both cutting parameters and tool vibrations are more accurate than those depending on
cutting parameters only.
G. Petropoulos et al. [5]
aimed on the impact of cutting conditions on surface roughness in
turning of polyethertherketone (PEEK) composites. Experiments were carried out for
unreinforced PEEK, reinforced PEEK with 30% of carbon fibres and reinforced PEEK with
30% of glass fibres. Machine Tools used were polycrystalline diamond (PCD) & K15
cemented carbide and Cutting Parameters examined were cutting speed and feed. By
applying statistical multi-regression analysis and analysis of variance (ANOVA) to the
experimental data a predictive model is developed and found that feed has the strongest
influence on roughness, while cutting speed has a secondary effect and they also found that
unreinforced PEEK presents the smallest roughness values, presence of glass fibers increases
roughness more than carbon fibers, and PCD tool tends to provide lower roughness than K15
cemented carbide tool.
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Ilhan Asiltürk and Mehmet Çunkas [6]
implemented Full factorial experimental design to
increase the confidence limit and reliability of the experimental data. Artificial neural
networks (ANN) and Multiple Regression approaches are used to model the surface
roughness of AISI 1040 steel using TiCN Carbide inserts and this two approaches are
compared using statistical methods.
The greatest influence on the surface roughness is exhibited by the feed rate (f), followed by
depth of cut (a) and cutting speed (V). The ANN model estimates the surface roughness with
high accuracy compared to the multiple regression model and also found that determination
coefficient (R2) is 99.8% for training data and 99.4% for the testing data in neural network
model, while it is achieved as 98.9% for multiple regression models. ANN compared to
multiple regression are simplicity, speed, and capacity of learning, the ANN is a powerful
tool in predicting the surface roughness.
M. Subramanian et al. [7]
developed a second order mathematical model using RSM to
predict the surface roughness in turning operation in terms of geometrical parameter, nose
radius of cutting tool TNMG carbide insert and machining parameters, cutting speed and
cutting feed rate for machining AL7075-T6, These process parameters are optimized using
genetic algorithm to obtain minimum surface roughness and adequacy of the model was
checked by employing ANOVA.
From main effect of parameters graph, they found that surface roughness increases with
increasing cutting speed and increasing feed rate. From interaction graphs of input parameters
on surface roughness, they found that nose radius and feed rate has maximum impact on
roughness. From observed values Vs. predicted values graph, they found that predicted
roughness values match closely with the observed values to a reliability of 99.69%.
Doriana M et al. [8]
They determined optimal machining parameters cutting speed, feed rate,
and depth of cut during a turning process of cast steel with HSS tool, that minimize the
production time without violating any imposed cutting constraints. 10 initial individual
obtained by Using Genetic Algorithm with 10 generations using the crossover operator and
the mutation operator. From generated graphs i.e, production time vs no. of generations,
cutting speed vs no. of generations, feed rate vs no. of generations, depth of cut vs no. of
generations for crossover operator and mutation operator they found that minimum
production time obtained by crossover operator is higher that by mutation operator also they
concluded that proposed methodology will lead to reduction in production time and cost,
flexibility in machining parameter selection and improvement of product quality.
B.C.Routara et al. [9]
applied Response Surface Methodology to determine the optimum
cutting conditions leading to minimum surface roughness in CNC turning operation on EN-8
steel. The second order mathematical model in terms of machining parameters was developed
for surface roughness prediction using RSM on the basis of experimental results. They
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performed experiment with coated carbide tool for machining of EN-8 steel. The model
selected for optimization has been validated with F-test and the adequacy of the models on
surface roughness has been established with ANOVA. They also optimize the surface
roughness prediction model using GA and found the optimum cutting parameters. They also
found that the surface roughness parameters decrease with increase in depth of cut and
spindle speed but increases with increase in feed.
N. Zeelan Basha et al. [10]
They observed Effect of process parameter i.e. Spindle speed,
Feed rate and Depth of cut on surface roughness prediction in CNC turning of Aluminium
6061 using coated carbide tool. A second order mathematical model is developed using
regression technique of Box-Behnken of Response Surface Methodology (RSM) in design
expert software 8.0 and optimization carried out by using genetic algorithm in matlab8.0.
Using genetic algorithm they found that optimal solution of the cutting conditions achieved
on spindle speed (rpm)= 1999.999, feed rate (mm/min)= 0.041 and depth of cut (mm)=0.6 for
giving the minimum value of surface roughness(μm)=0.611 using genetic algorithm and from
confirmatory test they found that the percentage of error within 0.32%.
Eyup Bagci and Birhan Işık [11]
carried out orthogonal cutting tests were on unidirectional
glass fibre reinforced plastics (GFRP), using cermet tools. During the tests, the depth of cut,
feed rate, cutting speed were varied, using artificial neural network (ANN) and response
surface (RS) model were developed to predict surface roughness on the turned part surface
and for surface roughness measurement, portable Mitutoyo Surftest 211 contact profilometer
used.
A three-level full factorial design, 3˄3= 27 experimental runs were performed and using
ANN and Response Surface Methodology surface roughness prediction was done, and this
predicted values were compared with experimental data it was observed that, feed has the
greatest influence on the surface roughness and RS is better than ANN in predicting the
values of surface roughness. In ANN, increasing the number of nodes increases the
computational cost and decreases the error and in surface roughness calculation, ANN model
took about 3 hours of CPU time to create whereas the RS model took just a couple of
seconds. The maximum test errors for ANN and RS model are about 6.36% and 6.30%
respectively.
Poornima, Sukumar [12]
optimized surface roughness of the martensitic stainless steel
(SS40) to study the effect of speed, feed and depth of cut while machining and compared it
with real practice. The machining parameter ranges were analysed and them the
experimentation was carried out according to the optimization approaches. The model was
developed using RSM and GA and then compared with the actual data. The results obtained
from RSM matches 99.9% with experimental surface roughness data, which indicates that
selected parameters affects significantly the surface roughness. The best ranges of cutting
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parameters were obtained by using GA and optimal surface roughness from GA was 0.74
microns.
M. Durairaj and S. Gowri [13]
carried out machining of Inconel 600 alloy with Titanium
Carbide Coated tool in DT-110 integrated multiprocessor micro machine tool. They
simultaneously optimize the two conflicting objectives i.e. tool wear and surface roughness
using GA. They found the optimal combination of process parameters to obtain better surface
finish and controlled tool flank wear through GA technique for machining. From
optimization they also recommended that low cutting speed, low feed and low depth of cut
gives best surface roughness values and better tool life. They also concluded that the
optimized surface roughness and tool life results were nearly equal to the experimental
results.
K. Palanikumar [14]
An attempt has been made to model the surface roughness through
response surface method (RSM) in machining GFRP composites. Four factors five level
central composite design (CCD), rotatable design matrix was employed to carry out the
experimental investigation. Analysis of variance (ANOVA) was used to check the validity of
the model. For finding the significant parameters student’s t-test was used.
From the analysis of the influences of the entire individual input machining parameters on the
response results found were,
The surface roughness decreases with the increase of cutting speed.
The surface roughness increases with the increase of feed rate.
The surface roughness increases with the increase of fiber orientation angle.
The surface roughness decreases with the increase of depth of cut.
Technique used found convenient to predict the main effects and interaction effects of
different influential combinations of machining parameters.
The procedure can be used to predict the surface roughness for turning of GFRP
composites within the ranges of variable studied. However, the validity of the
procedure was mostly limited to the range of factors considered for the
experimentation
Yusuf Sahin and A. Riza Motorcu [15]
developed surface roughness model in terms of
cutting speed, feed and depth of cut, using Response Surface Methodology. They developed
first order and second order model for predicting the surface roughness equations using
experimental data and found that surface roughness increases with increase in feed rate but
decreases with increase in cutting speed and depth of cut, respectively. They also found that
the predicted values and measured values were fairly close, which indicates that the
developed surface roughness prediction model can be effectively used to predict the surface
roughness from the cutting operation, with 95% confident intervals. Using Such models, a
remarkable saving and cost can be obtained.
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As from literature work it was found that Surface Roughness of any machined part by means
of any type of machining process plays vital role for finished products. The surface roughness
is a quality indicator of surface characteristics of machined parts and influences many
properties of material. Surface roughness can be predicted using many optimization
techniques. Prediction of such models can save time and cost.
CONCLUSION
From the above literature review we found that most of researchers have used genetic
algorithm and ANN. Few researchers have used RSM and Multi regression analysis. So it can
be concluded that Surface roughness prediction can be predicted using number of
optimization techniques. From the review it can also be found that the all the predicted
models give the factor effects of the individual process parameters viz, feed, depth of cut,
speed.
It can also be found that the models predicted using ANN are the most accurate
models with the actual experimental data, followed by the RSM, Fuzzy Logic, GA and Multi
regression analysis. It can also be found that with the help of DOE and ANOVA the
adequacy and accuracy of the models are increased and also they decreased the number of
experiments trials and cost. We also found that speed, feed and nose radius are most
significant parameters for the surface roughness and least significant parameter is DOC.
FUTURE SCOPE
Surface roughness does not depend solely on the feed rate, the tool nose radius and
cutting speed; the surface can also be deteriorated by excessive tool vibrations, the built-up
edge, the friction of the cut surface against the tool point, and the embedding of the particles
of the materials being machined.
To get best results of surface roughness selection of optimum machining conditions,
parameters, and materials can be done using various optimization techniques such as
Artificial Neural Networks, RMS, Genetic Algorithm, Taguchi, ANOVA etc.
Generally most of research work is concentrated on optimization of surface
roughness, machining and production cost, material removal rate but only a few researchers
worked on other parameters like cutting temperature, torque, geometrical accuracy, Heat
affected zone, Tool geometry. Also there is large number of research is done on metallic
components, but there is vast field of composites and alloys remain untouched.
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REFERENCES
1. Zahia Hessainia , Ahmed Belbah , Mohamed Athmane Yallese , Tarek Mabrouki , Jean-
François Rigal, On the prediction of surface roughness in the hard turning based on
cutting parameters and tool vibrations, Measurement, vol. 46, pp. 1671-1681, 2013
2. Vikas Upadhyay, P.K. Jain, N.K. Mehta, In-process prediction of surface roughness in
turning of Ti–6Al–4V alloy using cutting parameters and vibration signals, Measurement,
vol. 46, pp. 154-160, 2013
3. K.A. Risbood, U.S. Dixit, A.D. Sahasrabudhe, Prediction of surface roughness and
dimensional deviation by measuring cutting forces and vibrations in turning process,
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and tool vibrations in turning operation, Journal of Materials Processing Technology, vol.
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turning of peek composites , International Journal of Machine Tools & Manufacture, vol.
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Surface Roughness Based on Process Parameters in CNC Turning of AL7075-T6, IEEE
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Genetic Algorithm used to Optimize the Cutting Condition for Surface Roughness
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Parameters on ALUMINIUM 6061 Using Genetic Algorithm, International Journal of
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Advanced Manufacturing Technology, vol. 31, pp.10–17, 2006
12. Poornima, Sukumar,Optimization of machining parameters in CNC turning of martensitic
stainless steel using RSM and GA, International Journal of Modern Engineering
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13. M.Durairaj, S.Gowari, Parametric Optimization for Improved Tool Life and Surface
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coated carbide tool, Materials and Design, Vol.26, pp-321-326, 2005
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