Research Article SURFACE ROUGHNESS PREDICTION MODEL …

International Journal of Advanced Engineering Research and Studies E-ISSN2249 – 8974

IJAERS/Vol. I/ Issue I/October-December, 2011/102-113

Research Article

SURFACE ROUGHNESS PREDICTION MODEL USING ANN &

ANFIS S. Hari Krishna

1, K.Satyanarayana

2, K. Bapi Raju

3

Address for Correspondence 1PG Student, Mechanical Department, JNTU, Kakinada, (A.P), India

2Ph.D Scholar, EEE Department, Vignan University, Guntur, (A.P), India

3Assistant Professor, Department of Mech., Swarnandhra Engineering College, Andhra Pradesh, India

ABSTRACT Now a days the general manufacturing problem can be described as the achievement of a predefined product quality with

given equipment, cost and time constraints. There is a rapid development in the quality of advanced aero space materials

like aluminum and its alloys with improved properties. The difficulties in machining of these materials economically and

effectively are limiting their applications. The development of the new cutting tool materials is reaching an optimum level.

Some quality characteristics of product such as surface roughness are hard to ensure and play an important factor in

determining the quality of the product. Three cutting parameters viz., speed, feed, depth of cut are considered with constant

nose radius. Experiments are carried out on aluminum alloy, AA 6351 and machined on Computer Numerical Control Lathe

(CL 20 TL5) Turning Machine. Surface roughness of the machined piece was measured by using surface test stylus

instrument with diamond tip and the effect of each cutting parameter over surface roughness was studied. Two models have

been developed to predict the surface roughness. This paper utilizes two computational methods that is Adaptive-neuro fuzzy

inference system (ANFIS), modeling and Artificial neural network (ANN) to predict surface roughness of work piece for

variety of cutting conditions in hard turning. These models are developed in order to capture process specific parameters and

predict surface roughness.

KEYWORDS ANN, ANFIS, Surface roughness, CNN, MATLAB

I. INTRODUCTION

The important goal in the modern industries is to

manufacture low cost, high quality products in short

time. An automated and computerized system can be

used to achieve this goal. Turning and milling are the

most common methods employed for metal cutting

and especially for finishing of machined parts. It is

widely employed in a variety of manufacturing units

such as aerospace and automotive sectors. Surface

roughness is a measure of the quality of a product and

a factor that greatly influences manufacturing cost. It

can be generally stated that the lower the desired

surface roughness the more the manufacturing cost

and vise versa. The measured surface roughness

describes the geometry of the machined surface. The

surface roughness combined with the surface texture,

which is process dependant, can play an important

role on the operational characteristics of the part.

Surface roughness has received serious attention for

many years. It has formulated an important design

feature in many situations such as parts subject to

fatigue loads, precision fits, fastener holes, and

aesthetic requirements. In addition to tolerances,

surface roughness imposes one of the most critical

constraints for the selection of machines and cutting

parameters in process planning. The quality of the

surface plays a very important role in the performance

of machining as a good-quality product significantly

improves fatigue strength, corrosion resistance, and/or

creep life. Surface roughness also affects several

functional attributes of parts, such as contact causing

surface friction, wearing, light reflection, heat

transmission, and ability of distribution and holding a

lubricant, coating, or resisting fatigue. Therefore, the

desired finish of surface is usually specified and the

appropriate processes are selected to reach the

required quality. Several factors influence the final

surface roughness in any machining operation. The

final surface roughness might be considered as the

sum of two independent effects: (i) the ideal surface

roughness is a result of the geometry of tool and feed

rate and (ii) the natural surface roughness is a result of

the irregularities in the cutting operation.

II. LITERATURE REVIEW

Surface roughness has received serious attention for

many years. It has been an important design feature

and quality measure in many situations such as parts

subject to fatigue loads, precision fits, fastener holes

and esthetic requirements. Further more, surface

roughness in addition to tolerances imposes one of the

most critical constraints for cutting parameter

selection in manufacturing process planning. A

considerable amount of studies has been investigated

on the general effects of the speed, feed, and depth of

cut and constant nose radius on the surface roughness.

A popularly used model of estimating the surface

roughness value is as fallows [1] (Groover 1996, p.

634 and Boothroyd and Knight 1989, p.166)

r

fRi

32

2= ... (1)

WhereRi = ideal arithmetic average (AA) B surface roughness (in.

or mm), 2f = feed (in. /rev or mm/rev), and r = cutter nose radius

(in. or mm).

The above model assumed a relatively large noses

radius and slow feed.

For a zero nose radius and a relatively large feed, the

fallowing model is recommended [2] (Boothroyd and

Knight 1989, p.168):



Ri = ( )βα cotcot4

+f

(2)

Where α and β is the major and end cutting edge angle, respectively.

Shaw (1984) [3] presented a case when the feed lies

between the above two. Denoting the peak-to-valley

roughness by thR , then

βββ cotcos1sin2

2

+−++

−=

r

R

r

R

r

R

r

f ththth

(3)

Feng (2001) [4] compared the application Equations

(1) and (2) as well as the GE modified procedure out

lined. They concluded that equation (1) is almost

always more accurate than equation (2) when compare

to the observed values of surface roughness. Equation

(2) is virtually useless, since in more than 75 % of the

cases among 96 experiments the absolute percent error

(APE) of computed values from Equation (2)

compared to their respective observed once was

between 500% and 4092%.Also equation (1) modified

by the GE ratio has been found always more accurate

than Equation (1) used alone. Since our previous

sequential studies (Feng 2001) have identified the

factors and factor interaction that are important in

determining the surface roughness in finish turning.

This paper focuses on the modeling methodology and

the related model validation and comparison

procedures.

Gershenfeld, N. (1999) [5] Termed mechanistic

models as analytical models and empirical models as

observational models.In general empirical models tend

to be more specific than analytical models. requires

recalculation as new data becomes available,

intelligent systems have the ability to quickly

incorporate this new data And

Additionally, intelligent systems such as fuzzy logic,

neural networks, genetic algorithms, probabilistic

reasoning, and hybrids between these systems have

been found to be superior to regression due to their

ability to learn from experience in a complex system

such as turning (Zilouchain & Jamshidi, 2001) [6].

Where regression requires recalculation as new data

becomes available, intelligent systems have the ability

to quickly incorporate this new data and use it to tune

itself. One type of intelligent system that has found

popularity recently is a hybrid called fuzzy-nets (FN).

Also known as Neuro-fuzzy systems or fuzzy neural

networks (FNN), FN is ideal because it combines the

more efficient learning capability of neural networks

and the advanced reasoning capability of fuzzy logic.

Training data in a real- world application tends to be

sparse, thus limiting the ability of a neural network by

it self (as well as regression, for that matter), so the

inclusion of fuzzy logic to shorten the training and

learning time of a neural network makes this

combination an excellent solution[7] (Badiru &

Cheung,2002). Various methods are available, with

most variations found in training schemes, fuzzy logic

membership schemes, and input/output data types. At

the percent time, the application of FN systems to

surface roughness prediction in a turning operation is

somewhat limited. Jioa (2004) [8] utilized a similar

fuzzy adaptive network to create a prediction model

using spindle speed, feed rate, and depth of cut. While

limited in scope,

This study did illustrate some advantage of such a

system over regression modeling of complex systems

such as turning. Abburi and Dixit (2006) [9]. Did a

similar study, comparing the use of a standard neural

network system to that of combined artificial neural

network and fuzzy-sets system. The prediction

systems created in this study confirmed that the

combination of fuzzy logic and artificial neural

network is more capable and manageable than neural

networks alone. The researchers involved in both of

these studies conclude that their results indicate that

these types of systems are well suited to turning

operations and further studies of wider scope are

prescribed.

Chang-Xue (jack) Feng (2006) [10], inn this paper

analysis of variances is used to examine the impact of

turning factors and factor interactions on surface

roughness. Y.Kevin Chou and Hui Song [11] analyzed

tool nose radius effects on finish turning of hardended

AISI 52100 steels have been investigated results show

that large tool nose radii only give finer surface finish.

E.Daniel Kirby*, Joseph C. Julle Z. Zhang [12]

developed Fuzzy-nets models for prediction of surface

roughness using feed rate, spindle speed, tangential

vibrations. Al-Ahmari (2001) [13] developed

empirical models of tool life, surface roughness and

cutting forces for turning operations.

Ship-Peng Lo(2003) [14] developed Network based

fuzzy inference system (NFIS) that was used to

predict the work piece surface roughness using spindle

speed, feed rate, and depth of cut. He considered two

different membership functions of NFIS. Triangular

and trapezoidal, and were adopted during the training

process in order to compare the prediction accuracy of

surface roughness. Surajya K. pal & Debabrata

Chakraborty (2005) [15] in this work developed a

back propagation neural network model for the

prediction of surface roughness in turning operation

using feed and the cutting forces as inputs to the

neural network model. Shirashi and Sato (2002) [16]

suggested an optical technique using He-Ne laser

beam to measure the surface roughness, and job, and

dimension. The methodology is very difficult to

implement in machining because of the constant

production of chips, which, protrude in the direction

of the sensing beam, and also because of the presence

of the cutting fluid. Chien and Tsai (2003) [17] used

the back propagation neural network for predicting the

tool wear and determining the optimum cutting

condition in turning operations.



Risbood (2005)[18] developed a neural network based

model to predict surface roughness and dimensional

accuracy through the measurement of cutting forces

and vibration. The model was developed in the

MATLAB environment. One of the major drawbacks

with the ANN method, a kind of empirical modeling

method, is that these studies did not apply the factorial

experimentation approach to design the experiments.

Therefore, the data and conclusions obtained were

biased and factor interactions were not clearly

examined. Another drawback is that each study

limited to only a small number of parameters.

Furthermore, the regression analysis (RA) and

Artificial neural network method in surface roughness

modeling possesses the general drawbacks of an

empirical modeling method as described previously.

This research features the following contributions.

First, it applies the factorial experimentation approach

to design several rounds of experiments following the

sequential experimentation strategy. The impact of

each individual factor and factor interactions on

surface roughness are clearly examined with a

reasonably small amount of time and cost. Second,

with the improved accuracy of today’s machine tools

and surface roughness measuring devices and the

increased computing power of today’s computers and

software, the research is able to include more

parameters simultaneously with more accurate

experimental data. Third, this research is able to use

the computational neural networks (CNN) in addition

to the RA method in developing the empirical models

for surface roughness prediction. These two methods

have been recently termed data mining techniques

[19] (Written and Frank 2001). The appears to be the

only research in the literature contributed to the

application of CNN in surface roughness study, but

they focused on the development of a surface

roughness measuring system. They did not compare

the CNN results with the RA method. Also, they did

not apply the fractional factorial experimentation

method for design and analysis of the experiment.

Based on the above literature survey, an attempt is to

be made to develop empirical models with Artificial

Neural Network (ANN) method and with some data

mining techniques, such as Adaptive Neuro-Fuzzy

Inference System (ANFIS) to develop prediction

models which helps the selection of cutting

parameters and the improvement of surface roughness.

III. ARTIFICIAL NEURAL NETWORKS

A. Operation of an Artificial Neural Network

Neural network architecture is made up of an input

layer, one or more hidden layers and an output layer.

The hidden and output layers have processing

elements and interconnections called neurons and

synapses respectively. Each interconnection has an

associated connection strength or weight. The number

of hidden layers and that of the nodes in each layer

have to be decided very carefully, because the system

cannot model the given information if it has too few

hidden layer units. However, too many hidden units

limit the network’s ability to generalize the results, so

that the resulting model would not work well for new

incoming data. Each processing elements first

performs a weighted accumulation of the respective

input values and then passes the result through an

activation function. Except for the input layer nodes

where no computation is done, the net input to each

node is the sum of the weighted output of the nodes in

the previous layer.

The output of node j in layer k is

∑ −= 1k

i

k

ji

k

j ownet

(4)

)(1

1)(

kjnet

k

j

k

j

enetfo

−+== (5)

Fig.1 Typical Neural Network Mode

Where weight kjiW is the between the ith neuron in the

(k-1) th

layer and the jth neuron in the k

th layer, f(x) is

the activation function and Oth

is the output of the jth

neuron in the kth layer.

B. Adaptive Nero Fuzzy Inference System(ANFIS)

In this section, the use of the function ANFIS is

discussed. ANFIS is nothing but combination of

fuzzy logic and neural net works[20]. These tools

apply fuzzy inference techniques to data modeling.

The acronym ANFIS derives its name from adaptive

Neuro-fuzzy inference system. Using a given

input/output data set, the toolbox function ANFIS

constructs a fuzzy inference system (FIS) whose

membership function parameters are tuned (adjusted)

using either a back propagation algorithm alone, or in

combination with a least squares type of method. This

allows the fuzzy systems to learn from the data they

are modeling. In general, this type of modeling works

well if the training data presented to ANFIS for

training (estimating) membership function parameters

is fully representative of the features of the data that

the trained FIS is intended model. This is not always

the case, however. In some cases, data is collected

using noisy measurements, and the training data

cannot representative of all the features of the data



that will be presented to the model. This is where

model validation comes in to play. This training and

testing will be clearly explained in results and

conclusions module. Adaptive Neuro-fuzzy inference

system models provided with better prediction

capabilities because they generally offer the ability to

model more complex non-linearities and interactions

than linear and exponential regression models can

offer. Good prediction capabilities are required on

machining for to days industries. Adaptive Neuro-

fuzzy inference system can be adopted in predicting

machining response like surface roughness from given

input conditions where there may be much non

linearity relation among the various process

parameters.

IV. RESULTS AND DISCUSSION

Two different approaches are used for prediction of

surface roughness of turned work pieces and evaluated

in this work. First one is a ‘ANN model’ based on

Multilayer perception using back propagation

algorithm using speed (N), feed (ƒ) and depth of cut

(DOC) as three independent variables to predict the

surface roughness (Ra). Another one is ‘Adaptive

neuro fuzzy inference system is based on hybrid grid

partition network After training the network a set of

weights are generated and then used to infer the

surface roughness Ra values by inputting the new

values of three independent variables. The criterion to

judge the efficiency and the ability of the model is to

predict surface roughness values is taken as

percentage deviation (∆) which is defined in

equation(6) with this criterion it would be much easier

to see how the proposed models fit and how the

predicted values are close to the actual ones.

(6)

A. Prediction of surface roughness using neuro

solution model

Neuro Solution Software for Excel software is used

for the development of Neural Network model. Neural

networks are non linear mapping systems that consist

of simple processors, which are called neurons linked

by weighted connections. Each neuron has inputs and

generates an output that can be seen as the reflection

of treat information that is stored in connections. The

output signal of neuron is fed to the other neurons as

input signals via synapses (inter connections). Since

the capability of single neuron is limited, complex

function are realized by connecting many processing

elements network structure, representation of data,

normalization of inputs and outputs and appropriate

selection of activation function etc., are the factor that

have strong influence on the performance of the

network. The Network selected a Multilayer

perceptron multilayer perceptron (MLP) consists of

two layers. The activation function in the neural

network used hyperbolic (Tanh) function, which is

non linear function

B. Neuro Solution

The following steps are involved to solve the problem

in Neuro Solutions after completion of Experimental

works.

Step1: Select the input and output data. Tag the

input data and output data.

Step2: To select the data for training, cross

validation and for testing. Tag the training, cross

validation and testing data.

Step3: Create the customized network, network

type, number of input processing elements, output

processing elements, number of hidden layers and the

each hidden layer properties.

Specify Number of processing elements, Transfer

function, learning rule and Specify Step size and

momentum, Output layer properties, number of

epochs, Termination Criteria and Weight update.

Step4: Train the Network N times.

Step5: Results Estimation Methods, Sensitivity

analysis

Test the Network

C. Training of the Neural Network

The Neural Network is trained using input data with

corresponding output data of experimental results as

shown in the Table 5.5. Input data contains the

cutting condition i.e. Speed (N), Feed (ƒ) and Depth of

cut (DOC) and the output contain response of

machining, that is surface roughness Ra, which is a

function of N, ƒ and DOC. All the pattern were

normalized in the intervals -1 to 1 (which is done by

the software automatically) to fit for the hyperbolic

(Tanh) function.

D. Training error

The training error i.e. MSE (Mean Square Error) is the

criterion for obtaining optimum training parameter

and network performance. The back propagation of

error is continued for a number of iterations epochs

unified an acceptable error level is achieved. A large

number of iterations are required to back propagate

the error from output to input layer. Such process is

carried out to adjust the values of weights to achieve

certain estimation accuracy. The Mean Square Error is

the function of iterations as shown in Figures 6.3 and

6.4. It is seen that while that error is too high at low

epochs it is decreased rapidly with increase in number

of epochs.

E. Learning Curve

The learning curve (The Mean Squared Error across

the time) is a plot between MSE and Epochs. If the

number of epochs are less MSE value is high else its

values is decreases with increase of epochs. After

certain epochs its values is constant. The results are

predicted at 1000 epochs and shown in Table 1. The

details of the learning curve are shown in Table 3



Fig.2 MSE Training& Cross validation with single run

TABLE 1: EXPLANATION OF THE LEARNING CURVE NETWORKS

Fig.3 MSE Training& Cross validation with three run

TABLE 2 THE NETWORKS AVERAGE MSE

TABLE 3 THE EXPLANATION OF THE LEARNING CURVE NETWORKS



Fig. 4 Variation of MSE for Training data with three run

Fig.5 Variation of MSE for Cross validation data with three run

From above Tables 1, 2 and 3 best training values

obtained in first run at epoch number is 185 and the

Average Mean Square error is. For 0.0032112 cross

validation run-3, epoch number is 9135 and the

Average minimum MSE is 0.001543308. The

experimental and predicted values of surface

roughness for the training data using ANN model are

shown in the Fig 2 and 3 The percentage deviation

between experimental and predicted values obtained

by the developed ANFIS model is shown in Table 9.

The average percentage deviation of surface

roughness is also shown in this table. The developed

ANN model has been cross validated with the

corresponding data and the results are shown in the

Table 5 and the graph is drawn for the experimental

and predicted surface roughness values for cross

validation data and is shown in the Fig 9

F. Testing of the Network

Finally the ANN model trained and cross validated

has been tested with the new testing data. The

experimental and predicted values of surface

roughness for the test data using cross validated ANN

model are shown in the Fig 8

Fig.6 Training data Desire output Vs actual network



TABLE 4THE MSE PERFORMANCE

Fig 7 Experimental Vs Predicted Ra of ANN (Train Data)

TABLE 5 PERCENT DEVIATIONS OF EXP & PRE OF ANN (CROSS VALIDATION DATA).

Fig.8 Cross validation data Desire output Vs actual network

TABLE 6 THE MSE PERFORMANCE



Fig 9 Experimental Vs Predicted Ra of ANN (Cross validation Data)

TABLE 7 PERCENT DEVIATION OF EXPERIMENTAL & PREDICTED OF ANN (TEST DATA)

Fig.10 Testing data Desire output Vs actual network

Table 8 The MSE performance

Fig 11 Experimental Vs Predicted Ra of ANN (Test Data)



G. Prediction of surface roughness using anfis

MATLAB software is used to predict the surface

roughness by ANFIS tool, initial all data (testing&

training data) prepare for work space window was

displayed as shown in figure 13

Fig 13 MATLAB Window Train & Test data on

work space

By giving anfisedit command, ANFIS window was

displayed as shown in figure 14

Fig 14 ANFIS window in MATLAB

From load data portion of anfis window, the user has

to enter training data by selecting training and

workspace as shown in figure 14, and then click load

data. Once training data is loaded in to anfis, it will

plot the training data in anfis window as shown in

figure 15.

Fig 15 ANFIS window after loading training data

After loading the training data, the user has to select

the type of membership function and number of

membership functions for each input. To generate FIS

portion from anfis window, click on generate FIS.

Now it will display another window as shown in

figure 16. Select trapezoidal membership function

from MF type to input and linear membership function

to output. Assign three member ship functions for

each input as shown in number of MFs. Close the

window by selecting ok.

Fig 16 Generate FIS window of ANFIS

Now select method and number of epochs from train

FIS portion of anfis window. Then click on train now

to train anfis with training data. After training was

completed for 300 epochs, training curve will display

as shown in figure 17. This curve shows the reduction

of error for each epoch, and it was observed in figure

17 The curve reached the minimum error target and

there was no further reduction of error possible.

Fig 17ANFIS window after training

Now load the test data to anfis as a testing data, and

select the testing data in test FIS portion of anfis

window and click test now. Then two different types

of points, square points (in blue color) which

represents the experimental results and points (in red

color) which represents the predicted outputs for given

training data as shown in figure 18 It can be observed

from this figure that all predicted outputs for testing

data are very close to the experimental outputs of

testing data. The predicted Ra values are found in

Ruler Viewer of anfis as shown in the figure 20. In

the figure 20, for the given first testing speed, feed and

depth of cut values (150 0.09 0.75), the predicted

output was shown as 0.612. This is the complete

training and testing procedure of anfis for prediction

of surface roughness. The network is used three

inputs(Speed (N) ,Feed ( F ),Depth of cut (DOF ) (The

entire system architecture consists of five layers,

namely, the fuzzy layer, product layer, normalized

layer, de-fuzzy layer and total out put

layer).corresponding logical operations was done

internal it related output as shown in figure 19



Fig 18 ANFIS window after testing

Fig 19 ANFIS model structure window

Fig 20 ANFIS window for predicted values

The 3D graphs for all input parameters versus surface

roughness are shown in figure 21, 22 and 23. By

opening view menu from anfis window and select

surface viewer by entering input parameters (eg N, F),

it is found a new window as shown in figure 21 for

Speed, Feed and Surface Roughness. Similarly the

Figure 22 is drawn for Speed, Depth of cut and

Surface Roughness and figure 23 is drawn for Feed,

Depth of cut and Surface Roughness.

Fig 21 3D plot of Speed, Feed Vs Surface roughness

Fig 22 3D plot of Speed, Depth of cut Vs Surface

roughness

Fig 23 3D plot of Feed, Depth of cut Vs Surface

roughness

In the above 3D surface plots, the blue color surface

indicates the low surface roughness values i.e. the

better surface finish can be obtained. The yellow color

surface indicates the high surface roughness values.

From the Figure 21, it observed that the surface

roughness values are low in the speed range of 200 to

250 and the feed range of 0.09 to 0.12. However the

surface roughness values are high in the same speed

range of 200 to 250 but the feed range of 0.06 to

0.08.Fig 6.20 and Figure 23 shows that the surface

roughness values are high in the depth of cut range of

0.5 to 0.7, feed range of 0.06 to 0.08 and speed range

of 150 to 170. The surface roughness values are low in

the feed range of 0.1 to 0.15 as shown in Figure 23.

Finally the trained ANFIS model has been tested

with the new testing data. The experimental and

predicted values of surface roughness for the test data

using ANFIS model is shown in the fig 24

Fig 24 Experimental Vs Predicted Ra of ANFIS

(Test Data)



Table 9 Percent Deviation of Experimental &

Predicted of ANFIS (Test Data)

H. Comparison of developed models

Based on the results of the experimentation, the two

prediction models were developed for the three input

parameters viz., Speed, Feed, Depth of cut and the

response variable as surface roughness of machined

part. The following are the main points drawn based

on these prediction models:

Artificial Neural Networks technique was used to

develop the prediction model and the data required for

training the model was used from the experimental

values conducted. The developed Artificial Neural

Networks predicting the surface roughness with

average percentage deviation 4.72 % in train data,

3.021% in cross validation data and 3.868% in test

data.

ANFIS software has been used to develop the

prediction model. Various networks were trained with

different membership functions to arrive at the

suitable model. Finally, the network was trained for

300 epochs and the percentage deviation between the

experimental and predicted values is calculated and

shown in Table 9. The developed ANFIS model is

predicting the surface roughness with average

percentage deviation of 3.39 % with test data. The

values of average percentage deviation of surface

roughness predicted by the two developed models are

shown in Table 10.

Table 10 Comparison of the developed Models

The ANFIS model provided better prediction

capabilities than Artificial Neural Networks model

because they generally offer the ability to model more

complex nonlinearities and interactions. The Adaptive

Neuro Fuzzy-Inference System gives closed

correlation with the experimental values.

V. CONCLUSIONS

In this work, the effect of cutting speed, feed and

depth of cut on surface finish is studied on aluminum

alloy AA 6351. The input parameters are such

selected as speed in three levels, feed and depth of cut

in four levels. Different levels of input conditions are

derived from factorial Design of Experiments. The

experiments are conducted on CNC Turning (CL 20T

L5) Machine. Totally 24 samples of 50 mm length and

32 mm diameter work pieces were prepared. Two

experiments were carried on each specimen. Using

the experimental results, two models viz., the

Artificial Neural networks Model and Adaptive Neuro

Fuzzy-Inference System model are constructed. The

main conclusions based on these two prediction

models are:

ANN Model

• ANN model has given the training error as 4.72 %

• ANN model has given the Cross Validation error

as 3.021%

• ANN model has given the testing error as 3.868 %

• The tool disadvantages are that they train slowly

and require lots of train data

• Typically using more data for training network

process have been taken large time but not ANN

gives better prediction capabilities

• Based on the Sensitivity Analysis, only the feed

rate is a dominant parameter to decrease the

surface roughness rapidly.

• The effect of depth of cut on the surface

roughness is not regular and has a variable

character.

• In this work a back propagation neural network

model is developed for the prediction of surface

roughness in turning operation using feed and the

cutting forces as inputs to the neural network

model.

• Generate network in ANN back propagation

method with a kind of empirical model gives poor

result.

• A large number of iterations are required to back

propagate the error from output to input layer was

more.

ANFIS Model

• ANFIS model has given the testing error as 3.39

%.

• The ANFIS model provided better prediction

capabilities than Artificial Neural networks

Model.

• The prediction accurcy of ANFIS is high as

95.43%.

• The tool advantages are that they train fast and

accuracy.

• The increase train data and at medium feed rate

ANFIS model has given the minimum testing

error.

• The reduced test data and at different Speed,

Feed, Depth of cut gives best result than ANN.

• Generate (FIS) networwk grid partition with

hybrid optimum method that gives best result

than ANN (back propagation).

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Research Article SURFACE ROUGHNESS PREDICTION MODEL …