surface roughness prediction based on cutting … · 2017-11-26 · radius of cutting tool tnmg...

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.

International Journal of Scientific Research in Engineering

IJSRE October, Vol-1 Issue-9 www.ijsre.in

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



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.



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



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



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.



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.



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,

Journal of Material Processing Technology, vol. 132, pp. 203-214, 2003

4. O. B. Abouelatta and J. Madl, Surface roughness prediction based on cutting parameters

and tool vibrations in turning operation, Journal of Materials Processing Technology, vol.

118, pp. 269-277, 2001

5. G. Petropoulos , F. Mata , J. Paulo Davim, Statistical study of surface roughness in

turning of peek composites , International Journal of Machine Tools & Manufacture, vol.

43, pp. 1093–1106, 2003

6. Ilhan Asiltürk, Mehmet Çunkas, Modeling and prediction of surface roughness in turning

operations using artificial neural network and multiple regression method, Expert

Systems with Applications, vol. 38, pp. 5826–5832, 2011

7. M.Subramanian, R.Sivaperumal, M.P.Siva, M.Sakthivel, Using RSM and GA to Predict

Surface Roughness Based on Process Parameters in CNC Turning of AL7075-T6, IEEE

International Conference on Innovations in Engineering and Technology, issue 3, vol. 3,

pp. 2347-2355 , March 2014

8. Doriana M, D. Addona, Roberto Teti, Genetic algorithm-based optimization of cutting

parameters in turning processes, Forty Sixth CIRP Conference on Manufacturing Systems

2013, vol.7, pp. 323-328, 2013

9. B.C.Routara, A.K.Sahoo, A.K.Parida, P.C.Padhi, Response Surface Methodology and

Genetic Algorithm used to Optimize the Cutting Condition for Surface Roughness

Parameters in CNC Turing, International Conference on Modeling, Optimization and

Computing, Procedia Engineering, vol.38, pp.1893-1904, 2012

10. N. Zeelan Basha, G. Mahesh, N. Muthuprakash, Optimization of CNC Turning Process

Parameters on ALUMINIUM 6061 Using Genetic Algorithm, International Journal of

Science and Modern Engineering, Issue-9, vol. 1, pp. 2319-6386, August 2013

11. Eyup Bagci and Birhan Işık, Investigation of surface roughness in turning unidirectional

GFRP composites by using RS methodology and ANN, International Journal of

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

Research, Vol.2, Issue.2, pp-539-542,2012.



13. M.Durairaj, S.Gowari, Parametric Optimization for Improved Tool Life and Surface

Finish in Micro Turning using Genetic Algorithm, International Conference on Design

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14. K. Palanikumar , Modeling and analysis for surface roughness in machining glass fibre

reinforced plastics using response surface methodology, Materials and Design, vol. 28,

pp. 2611–2618, 2007

15. Yusuf Sahin, A. Riza Motorcu, Surface roughness model for machining mild steel with

coated carbide tool, Materials and Design, Vol.26, pp-321-326, 2005



surface roughness prediction based on cutting … · 2017-11-26 · radius of cutting tool tnmg...

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