Modelling and prediction of surface roughness

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME

599

MODELLING AND PREDICTION OF SURFACE ROUGHNESS, CUTTING

FORCE AND TEMPERATURE WHILE MACHINING NIMONIC-75 AND

NICROFER C-263 SUPER ALLOYS USING ARTIFICIAL NEURAL

NETWORK (ANN)

P. SUBHASH CHANDRA BOSE1, C S P RAO

2

1(Department of Mechanical Engineering, National Institute of Technology- Warangal, India,

[email protected]) 2(Department of Mechanical Engineering, National Institute of Technology- Warangal, India,

[email protected])

ABSTRACT

In the present investigation the influence of process parameters like speed, feed, depth of

cut in dry machining, are studied as surface roughness, cutting force and temperature as the

output. An artificial neural network (ANN) model was developed for the analysis and prediction

of the relationship between input and output parameters during high-speed turning of nickel-

based alloys like Nimonic-75. The input parameters of the ANN model are the cutting

parameters: speed, feed rate and depth of cut. The output parameters of the model are three,

measured during the machining trials namely surface roughness (Ra), cutting force (Fz) and

temperature (T). The model consists of a two layered feed forward back propagation neural

network. The network is trained with pairs of inputs/outputs datasets generated when machining

Nimonic-75 with TN6025 coated carbide tool. A highly efficient neural network, in agreement

with the experimental data, was achieved. The model can be used for the analysis and prediction

of the complex relationship between cutting conditions and the output parameters in metal-

cutting operations and for the optimization of the cutting process for efficient and economic

production.

Keywords: Artificial Neural Network; Nimonic-75; Surface Roughness; Cutting Force;

Temperature

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 3, Issue 3, September - December (2012), pp. 599-613

© IAEME: www.iaeme.com/ijmet.asp

Journal Impact Factor (2012): 3.8071 (Calculated by GISI)

www.jifactor.com

IJMET

© I A E M E



600

1. INTRODUCTION

Machining super alloys has become an important manufacturing process, particularly in the

automotive, aerospace and gas turbine manufacturing industries.

Metal cutting is one of the important and widely used manufacturing processes in engineering

industries. The study of metal cutting focuses among others on the features of tools, input work materials,

and machine parameter settings influencing process efficiency and output quality characteristics (or

responses). The quality of a machined surface is always an important parameter in the component

performance and reliability. While machining any component, it is necessary to satisfy the surface

technological requirements in terms of high product accuracy, good surface finish and minimum of

drawbacks that may arise as a result of possible surface alterations by the machining process.

Finish hard turning is an emerging machining process which enables manufacturers to machine

hardened materials having hardness greater than 45 HRC using a single point cutting tool without any aid

of cutting fluid on a rigid lathe or turning center. This process has been developed as an alternative to the

grinding process in a bid to reduce the number of setup changes, product cost and lead time without

compromising on surface quality to maintain competitiveness. For successful implementation of hard

turning, selection of suitable cutting parameters for a given cutting tool work piece material and machine

tool are important and need to be developed through experimentation as the suitable cutting parameter

are not available.

Machining of super-alloys is a challenging task. Nickel based super-alloys like Nimonic, Nicrofer

and Niobium have got wide application in missile technology and other defense applications due to its

strength at high temperatures. Hence evaluation of machining parameters is the need of the hour for

manufacturing of super-alloy components.

Hence an attempt is made by the author to evaluate surface finish, cutting force and temperature in

machining of Nimonic-75 and Nicrofer C-263 alloys. Several experiments were conducted using DOE

and ANOVA analysis was done on the output parameters. The process is simulated by modeling with

ANN for prediction of surface finish, cutting force and temperature at different values of speed, feed and

depth of cut.

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model

or computational model that is inspired by the structure and/or functional aspects of biological neural

networks. A neural network consists of an interconnected group of artificial neurons and it processes

information using a connectionist approach to computation. In most cases an ANN is an adaptive system

that changes its structure based on external or internal information that flows through the network during

the learning phase. Modern neural networks are non-linear statistical data modeling tools. They are

usually used to model complex relationships between inputs and outputs or to find patterns in data.

Fig 1 Dimensions of the Carbide Tool



601

2. MODEL DESCRIPTION

There has been in an increase in the research interest in the applications of ANN’s modeling the

relationship between the cutting conditions and the process parameters during the machining process.

The input parameters of the neural networks being the speed, feed and depth of cut and the output

parameters are three of the most important process parameters namely, surface roughness (Ra), Cutting

forces (Fz) and Cutting Temperature (T).

The five basic steps used in general application of the neural networks are adopted in the development

of the model: assembly or collection of data; analysis and pre-processing of the data; design of the

network object; training and testing of the network; and performing simulation with trained network and

post –processing of results.

2.1 Experimentation/collection of input/output data set

Rigid, high power, 12kW VDF retrofitted CNC lathe with a speed of 56-3550 rpm. 38 mm

diameter and 250 mm long cast solution treated, annealed and hardened, nickel chromium alloy with

controlled additions of titanium and carbon, Nimonic 75, alloy bars were used. The chemical

composition and physical properties of the work pieces are given in Table 1 and 2, respectively. Before

conducting the machining trails, up to 2 mm thickness of the top surface of each bar was cleaned in order

to eliminate any skin defect that can adversely affect the machining results.

Multi-layered PVD coated cemented tungsten carbide inserts were used for the turning tests. The

coated carbide grade was TN6025; the dimensioning of the tool is given in fig 1, which is designed for

light and medium turning operations of high temperature alloys. TN6025 inserts are Nano-multilayered

TiAlN coated insert, having very high wear resistance and also good toughness.

Table 1

Chemical composition of Nimonic 75 (wt %)

Element C Cr Cu Fe Mn Si Ti Ni

Wt (%) 0.08 18 0.5 5 1.0 1.0 0.2 Balance*

Table 2

Physical Properties of Nimonic 75

Hardness

(HRC)

Density

(mg/m3)

Melting Point

(0C)

Thermal conductivity

(W/mK)

Electrical Resistivity

(µΩ.m)

28 8.37 1340 11.7 1.09

The measurements of average surface roughness (Ra) were made on HANDYSURF E 35 B. Three

measurements of surface roughness were taken at different locations and the average value is used in the

analysis. It directly gives the value in digital format.

Infrared thermometer Kiray 300 was used for temperature measurement, while conducting the

experiments. This is a thermometer used to diagnose, inspect and check any temperature. Thanks to its

elaborated optical system with a dual laser sighting, it allows easy and accurate measurements of little

distant targets. The KIRAY 300 instrument has an internal memory which can save up to 100

measurements. Compatible with thermocouple K probe.

Four-component dynamometer was used to measure the cutting force components. This

dynamometer can be used for measuring a torque Mz and the three orthogonal components of a force.

The dynamometer has a great rigidity and consequently a high natural frequency. Its high

resolution enables the smallest dynamic changes in large forces and torques to be measured.

Compact and robust multi-component force measuring instrument



602

2.2 Preprocessing of input/output datasets

The working ranges of the parameters, for the subsequent design of experiment, were selected

based on tool manufacturer recommendations and machine tool capabilities. In the present experimental

study, three parameters such as speed, feed and depth of cut have been considered as process variables

with 5, 4, 3 levels respectively. The levels have been so selected based on the intuition of the affect of

these parameters on the output process parameters. The values are listed in Table 3 below.

TABLE 3 Cutting Level

Controllable factors Level 1 Level 2 Level 3 Level 4 Level 5

Speed 30 36.25 42.5 48.75 55

Feed 0.08 0.12 0.16 0.20 -

Depth of cut 0.5 1.0 1.5 - -

Experiments have been carried out using full factorial experimental design which consists of 60

combinations of speed, feed and depth of cut. Out of which 50 have been used for training the neural

networks and 10 have been used for testing the networks. The design of experiment has been shown in

Table 4.

TABLE 4 Designs of Experiments S. No Speed Feed Doc

1 42.5 0.12 1.5

2 42.5 0.12 0.5

3 55 0.12 1

4 30 0.12 1

5 42.5 0.16 1

6 55 0.08 1

7 48.75 0.2 0.5

8 36.25 0.16 1

9 48.75 0.12 1

10 36.25 0.08 1

11 36.25 0.2 1.5

12 36.25 0.12 1.5

13 30 0.2 0.5

14 48.75 0.16 1

15 36.25 0.12 1

16 30 0.08 1.5

17 36.25 0.2 0.5

18 48.75 0.08 1.5

19 55 0.16 1

20 30 0.08 1

21 55 0.12 0.5

22 48.75 0.08 1

23 30 0.08 0.5

24 48.75 0.08 0.5

25 42.5 0.2 0.5

26 48.75 0.2 1.5

27 30 0.16 1.5

28 42.5 0.2 1.5

29 36.25 0.08 0.5

30 42.5 0.08 1.5

31 42.5 0.08 1

32 55 0.08 0.5

33 36.25 0.16 1.5

34 30 0.16 1

35 55 0.08 1.5

36 30 0.2 1.5

37 55 0.2 1

38 55 0.16 1.5

39 42.5 0.08 0.5

40 48.75 0.2 1

41 36.25 0.2 1

42 48.75 0.12 0.5

43 36.25 0.08 1.5

44 48.75 0.16 1.5

45 36.25 0.12 0.5

46 30 0.12 0.5

47 30 0.16 0.5

48 42.5 0.16 1.5

49 55 0.2 0.5

50 55 0.12 1.5

51 55 0.16 0.5

52 42.5 0.2 1

53 30 0.12 1.5

54 55 0.2 1.5

55 36.25 0.16 0.5

56 48.75 0.12 1.5

57 30 0.2 1

58 42.5 0.12 1

59 48.75 0.16 0.5

60 42.5 0.16 0.5



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2.3 Neural network design and training

The network architecture or feature such as number of neurons and layers are very important

factors that determine the functionality and generalization capability of the network. For this model,

standard multilayered feed-forward back propagation hierarchical neural networks were designed with

MATLAB 7.7 Neural network toolbox. The network consists of 4 layers, one input, two hidden layers

and one output layer. In general, the networks have three neurons in the input, corresponding to each of

the three cutting parameters and one neuron in the output, corresponding to each of the process

parameter. For all networks, linear transfer function ‘purelin’ has been used for input and output layers

and tangent transfer sigmoid transfer function ‘tansig’ were used in the hidden layers. Three different

neural network models are used, one for the prediction of surface roughness, second for the prediction of

cutting force and third for the prediction of temperature.

The networks were trained with Levenberg-Marquardt algorithm. This training algorithm was

chosen due to its high accuracy in similar function approximation [3, 15]. In order to improve the

generalization of the network, a ‘regularization’ scheme was used in conjunction with the Levenberg-

Marquardt algorithm. The automatic Bayesian regularization was used.

For training with Levenberg-Marquardt combined with Bayesian regularization, the input/output

dataset was divided randomly into two categories: training dataset, consisting of 50 of the input/output

dataset and the remaining as the test dataset.

2.4 Testing and performance of the network

In order to determine the optimum number of neurons in the hidden layers, the testing was done

taking 5, 10, 15, 20, 25 neurons in each hidden layer and for each iteration the best performance error

was calculated and then compared. Table 5 shows the combinations of neurons in the first and second

hidden layers and the best performance error calculated for all the iterations for the surface roughness

process parameter. The values of the error with 20 neurons in the second hidden layer are lower than the

other error values for all the other combinations. Table 6 gives us the values of best performance error

for surface roughness parameter by iterating the neurons in the 1st hidden layer with fixed neurons in the

second hidden layer. Table 7 and 8 show us the iterations through which the final set of neurons have

been selected. Thus, the network having two layers of 20 neurons and 19 neurons, respectively, trained

with Levenberg-Marquardt algorithm and Bayesian regularization has been chosen as the optimum

network and used for development of this model. The performance of the model for prediction of the

surface roughness with an error of 0.00262 has been developed. The combination which gave the least

Fig 2 Picture Depicting the Layers in a Neural

Network



604

error was selected for the testing of the neural networks. The predicted values from the neural network

are then compared to the experimentally obtained values. Three inputs are taken for all the three

networks, which are speed, feed and depth of cut. In this, linear function was used for input layer/ output

layer and sigmoid function is used for hidden layers. This demonstrated that the models have high

accuracy for predicting the process parameters. Acceptable results have were also obtained for all the

process parameters. The analysis of variance (ANOVA) was performed to statistically analyze the

results. An ANOVA summary table is commonly used to summarize the test of the regression

model, test of the significance factors and their interaction and lack-of-fit test. If the value of

‘Probability > F’ in ANOVA table is less than 0.05 then the model, the factors, interaction of

factors and curvature are said to be significant. Finally, % contribution column is added in ANOVA

summary table and it often serves as a rough but an effective indicator of the relative importance of each

model term.

TABLE 5 Best Performance Error for Surface Roughness parameter by iterating the neurons in the

second hidden layer

No of Neurons

in 1st Hidden

Layer

No of Neurons

in 1st Hidden

Layer

Best Performance

Error

5 5 0.0242

5 10 0.379

5 15 0.0386

5 20 0.0142

5 25 0.0268

10 5 0.0468

10 10 0.0486

10 15 0.0562

10 20 0.013

10 25 0.142

15 5 0.0565

15 10 0.0642

15 15 0.0541

15 20 0.0124

15 25 0.0329

20 5 0.0142

20 10 0.0263

20 15 0.0281

20 20 0.00834

20 25 0.0142

25 5 0.0163

25 10 0.024

25 15 0.029

25 20 0.0381

25 25 0.024

TABLE 6 Best Performance Error for Surface Roughness parameter by iterating the neurons in the first

hidden layer with fixed neurons in the second hidden layerNo of Neurons in 1

st

Hidden Layer

No of Neurons in 1st Hidden

Layer Best Performance Error

5 20 0.0142

10 20 0.013

15 20 0.0124

20 20 0.00834

25 20 0.0381

TABLE 7 Best Performance Error for Surface Roughness parameter, to fix the neurons in the first hidden layer

No of Neurons in 1st

Hidden Layer

No of Neurons in 1st Hidden

Layer Best Performance Error

15 20 0.0124

17 20 0.0139

19 20 0.0432

20 20 0.00834

21 20 0.0328

23 20 0.056

25 20 0.0381



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TABLE 8 Best Performance Error for Surface Roughness parameter, to fix the neurons in the

second hidden layer


Hidden Layer


Hidden Layer

Best Performance

Error

20 15 0.0281

20 17 0.0174

20 18 0.0382

20 19 0.0262

20 20 0.0834

20 21 0.0943

20 23 0.0262

20 25 0.0142

3. Results and Discussions

3.1 Results

The predicted values and experimental values of surface roughness, cutting force and

temperature both training and testing are shown in the fig 3, 4 and 5, respectively. From the

above said figures it is evident that network responded well for the testing data as well.

Fig. 3 Experimental and predicted Ra values



606

Fig. 4 Experimental and predicted Cutting Force values

Fig. 5 Experimental and predicted Temperature values



607

Table 9 Ra values

Table 10 Cutting Force values

Exp no Measured

values

Predicted

values

Percentage

of error

1. 1.31 1.25 4.58

2. 1.71 1.65 3.50

3. 0.82 0.75 8.53

4. 0.97 0.90 7.21

5. 0.72 0.62 13.88

6. 0.8433 0.733 13.07

7. 1.15 1.02 11.30

8. 2.18 2.08 4.58

9. 1.36 1.25 8.08

10. 0.97 0.80 17.52

Exp

no

Measured

values

Predicted

values

Percentage

of error

1. 407.3 423.715 4.1301

2. 555.9 549.0548 1.231

3. 119.3 107.41 9.96

4. 88.72 79.72 10.14

5. 164.2 150.24 8.501

6. 617 610.55 1.134

7. 477.7 487.7 2.093

8. 275.3 276.0625 0.276

9. 375.2 377.8248 0.699

10. 184.5 175.15 5.067



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Table 11 Temperature values

Exp

no

Measured

values

Predicted

values

Percentage

of error

1. 154.5 152.1884 1.49

2. 148.5 145.9225 1.73

3. 70.4 74.708 6.11

4. 73.1 70.2374 3.91

5. 168.3 160.45 4.66

6. 170.2 165.02 4.01

7. 96.6 89.6 7.24

8. 92.2 80.6 12.58

9. 137.7 125.922 8.55

10. 82.8 75.7979 8.45

3.2 Evaluation of performance of the network

From different runs of the program, the best architectures are found to be that training 10

neurons in the hidden layer and regression R=0.8661 and no of epochs=69for the prediction of

Ra. 20 in the hidden layer and the R=0.9065 and no of epochs=93 for the prediction of

temperature, 44 neurons in the hidden layer and the R=0.90581 and no of epochs=89for the

prediction of cutting forces.

Mean relative error is used for evaluation of performance of the networks. The calculated

mean relative errors of the network used for predicting surface roughness, temperature and

cutting forces found to be 0.92% ,5.873% and4.32% respectively. These values show that the

accuracy of neural network is good.

3.3 Response tables for surface roughness, cutting force and temperature without interactions.

Table 12 Response table for Ra

Level Speed Feed Doc

1 1.3283 0.973 1.0905

2 1.0192 1.1693 1.3640

3 1.1342 1.2707 1.2202

4 1.1775 1.4860 -----

5 1.4653 ----- -----

Max-Min 0.4461 0.513 0.2735

Rank 2 1 3



609

Table 13 Response table for Cutting Force


1 106.10 114.13 87.89

2 114.45 115.51 116.86

3 111.15 108.67 127.73

4 103.68 105.00 -----

5 118.76 ----- -----

Max-Min 15.08 10.51 39.84

Rank 2 1 3

Table 14 Response table for Temperature


1 285.6 235.6 155.0

2 327.9 285.8 305.6

3 318.9 346.8 467.1

4 309.6 368.7 -----

5 304.1 ----- -----

Max-Min 42.3 133.1 312.1

Rank 2 1 3

3.4 Analysis of variance tables for surface roughness, cutting force and temperature

Table 15 ANOVA table for Ra

S. No Source DOF Sum of squares Mean squares F Value % contribution

1 Speed 4 1.4554 0.3639 1.51 8.91

2 Feed 3 12.0480 0.6827 2.88 73.82

3 Doc 2 0.7487 0.3743 0.59 4.58

4 Error 50 1.6652 0.2413 ----- -----

5 total 59 16.3192 ----- ----- -----

Table 16 ANOVA table for Cutting Force

S. No Source DOF Sum Of Squares Mean Squares F Value % Contribution

1 Speed 4 12319 3080 0.55 8.6

2 Feed 3 163632 54544 9.72 11.4

3 Doc 2 974660 487330 86.87 68.10

4 Error 50 280507 5610 ----- -----

5 Total 59 1431118 ----- ----- -----



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Table 17 ANOVA table for Temperature

S.No Source DOF Sum Of Squares Mean Squares F Value % Contribution

1 Speed 4 16966.5 448.9 0.96 39.16

2 Feed 3 1070.7 356.9 0.76 2.47

3 Doc 2 23483.9 8483.2 18.06 54.21

4 Error 50 1795.6 469.7 ----- -----

5 Total 59 43316.7 ----- ----- -----

From the analysis of table 15, we can observe that the cutting factors speed (p= 8.91%), feed

(p= 73.82%), doc (4.58%) have statistical significance on the surface roughness obtained,

especially the feed. From the analysis of the table 16, we can observe that the cutting factors speed

(P=8.6%), feed (P=11.9%) and Doc (P=68.01%) have statistical significance on the cutting force

obtained, especially the Doc. From the analysis of the table 17, we can observe that the cutting factors

speed (P=.21%), feed (P=2.47%) and Doc (P=39.1654%) have statistical significance on the

temperature obtained, especially the Doc.

3.5 Analysis of variance tables for surface roughness, cutting force and temperature with

interactions.

Table 18 ANOVA table for Ra with interactions

S.No Source DOF Sum of squares Mean square F Value % contribution

1 Speed 4 1.4554 0.3639 1.63 8.91

2 Feed 3 5.3677 0.6827 3.05 32.10

3 Doc 2 0.7487 0.3743 1.67 4.5

4 Speed*feed 12 2.8356 0.2363 1.06 17.37

5 Speed*doc 8 1.2415 0.1552 0.69 7.60

6 Feed*doc 6 2.6223 0.4370 1.95 16.06

7 Error 24 2.0480 0.2337 ----- -----

8 Total 59 16.3192 ------ ----- -----

Table 19 ANOVA table for Cutting Force with interactions

S.No Source Dof Sum of squares Mean square F Value % contribution

1 Speed 4 12319 3080 0.68 0.8

2 Feed 3 163632 54544 12.06 11.43

3 Doc 2 974660 487330 107.73 68.10

4 Speed*feed 12 55041 4587 1.01 3.84

5 Speed*doc 8 74288 9286 2.05 5.19

6 Feed*doc 6 42613 7102 1.57 2.97

7 Error 24 108565 4524 ----- -----

8 total 59 1431118 ----- ----- -----



611

Table 20 ANOVA table for Temperature with interactions

S.No Source Dof Sum of squares Mean square F Value % contribution

1 Speed 4 14323.5 448.9 0.75 33.06

2 Feed 3 1070.7 356.9 0.60 2.47

3 Doc 2 16966.5 8483.2 14.21 39.16

4 Speed*feed 12 3256.6 271.4 0.45 7.518

5 Speed*doc 8 3990.1 498.0 0.84 4.416

6 Feed*doc 6 1913.1 319.0 0.53 4.419

7 Error 24 1797.6 596.0 ----- -----

8 total 59 43316.7 ----- ----- -----

From the analysis of table 18, we can observe that the cutting factors speed (P=8.91%),

feed (P=32.10%), doc (P=4.5%), speed*feed (P=17.37%), speed*doc (P=7.6%), feed*doc

(P=16.06%) have statistical significance on the surface roughness obtained, especially the feed.

From the analysis of table 19, we can observe that the cutting factors speed (P=8.6%), feed

(P=11.43%), doc (P=68.10%), speed*feed (P=3.84%), speed*doc (P=5.19%), feed*doc

(P=2.97%) have statistical significance on the cutting force Fz obtained, especially the doc. From

the analysis of table 20, we can observe that the cutting factors speed (P=39.16%), feed

(P=2.47%), doc (P=33.06%), speed*feed (P=7.518%), speed*doc (P=4.416%), feed*doc

(P=4.419%) have statistical significance on the temperature obtained, especially the speed and

doc.

As we increase the speed, the input parameter, from figure 6, the Surface roughness

initially decreases and later on increases more or less reaching to the same initial value of surface

roughness within the range of input parameters taken. With increase in feed, from figure 7, the

surface roughness does on increasing. Whereas with increase in depth of cut, the surface

roughness has no significant effect, figure 8.

For the cutting forces as the output parameter, from figure 9, as we increase the speed, the

cutting forces initially increase forming a peak and later on decrease. With increase in the feed,

from figure 10, the cutting forces increase rapidly. Similarly with increase in depth of cut the

cutting forces are increasing, from figure 11.

With increase in speed, the Temperature have seen a initial increase in temperatures with a

local minimum and then a increasing trend as we increase the speed, from figure 12. From figure

13, with increase in feed the temperature first increased and later on decreased. With increase in

depth of cut, the temperature goes on increasing, from figure 14.



612

Fig. 6 Speed Vs ‘Ra’ mean Fig.7 Feed Vs ‘Ra’ mean

Fig. 8 D.O.C Vs ‘Ra’ mean Fig.9 Speed Vs ‘Fz’ mean

Fig. 10 Feed Vs ‘Fz’ mean

Fig.11 D.O.C Vs ‘Fz’ mean

Fig. 12 Speed Vs Temperature mean Fig.13 Feed Vs Temperature mean

Fig. 14 D.O.C vs. Temperature mean



613

4. CONCLUSIONS

1. The plot of experimental and predicted values from neural network shows that the network has

been trained well and has good generalization ability.

2. The network having two layers of 20 neurons and 19 neurons, respectively, trained with

Levenberg-Marquardt algorithm and Bayesian regularization has been chosen as the optimum

network and used for development of this model.

3. The performance of the model for prediction of the surface roughness with an error of 0.00262

has been developed.

4. The optimum cutting speeds at which the minimum process parameters were obtained were is

35, 55 and 48.75 for surface roughness, cutting forces and temperature respectively.

5. With increase in feed, the output parameters surface roughness and cutting forces have

increased consistently, while temperatures have decreased.

6. Depth of cut has had the same effect, more or less, on all the parameters. With increase in the

depth of cut all the output parameters have increased.

REFERENCES

1. E.O. Ezugwu, D.A. Fadare, J. Bonney, R.B. Da Silva, W.F. Sales - Modeling the correlation

between cutting and process parameters in high speed machining of Inconel 718 alloy using an

artificial neural network.

2. E.O. Ezugwu, Advances in the machining of nickel and titanium base superalloys, Keynote paper

presented at the Japan Society for Precision Engineering Conference 2004, pp. 1–40.

3. S. Malinov, W. Sha, J.J. McKeown, Modelling the correlation between processing parameters and

properties in titanium alloys using artificial neural network, Comput. Mater. Sci. 21 (2001) 375–394.

4. E.O. Ezugwu, K.A. Olajire, J. Bonney, Modelling of tool wear based on component forces, Tribol.

Lett. 11 (1) (2001).

5. E.O. Ezugwu, J. Bonney, Effect of high-pressure coolant supply when machining nickel-base,

Inconel 718, alloy with coated carbide tools, Proceedings of AMPT 2003, 8–11 July 2003, Dublin,

Ireland, pp.787–790.

6. E.O. Ezugwu, J. Bonney, Effect of high-pressure coolant supply when machining nickel-base,

Inconel 718, alloy with coated carbide tools, J.Mater. Process. Technol. 153–154 (2004) 1045–1050.

7. J. Bonney, High-speed machining of nickel-base, Inconel 718, alloy with ceramic and carbide cutting

tools using conventional and high-pressure coolants, PhD Thesis, London South Bank University,

2004.

8. E.O. Ezugwu, A.R. Machado, I.R. Pashby, J. Wallbank, The effect of high-pressure coolant supply

when machining a heat-resistant nickel-based superalloy, Lubr. Eng. 47 (9) (1991) 751–757.

9. E.O. Ezugwu, S.J. Arthur, E.L. Hines, Tool-wear prediction using artificial neural networks, J. Mater.

Process. Technol. 49 (1995)255–264.

10. D.E. Dimla Sr., Application of perceptron neural networks to tool-state classification in a metal-

turning operation, Eng. Appl. Artif.Intell. 12 (1999) 471–477.

11. H. Demuth, M. Beale, Neural Network Toolbox User’s Guide, Version 4 (Release 12), The

Mathworks, Inc., 2000.

12. R.S. Pawade a, Suhas S. Joshi a, P.K. Brahmankar b, M. Rahman “An investigation of cutting forces

and surface damage in high-speed turning of Inconel 718” (Journal of Materials Processing

Technology 192-193 (2007) 139-146).

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Modelling and prediction of surface roughness