influence of process parameters on surface … · 2018. 10. 3. · agreement with the regression...

International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.5, No.1, February 2016

DOI : 10.14810/ijmech.2016.5104 47

INFLUENCE OF PROCESS PARAMETERS ON

SURFACE ROUGHNESS AND MATERIAL REMOVAL

RATE DURING TURNING IN CNC LATHE – AN

ARTIFICIAL NEURAL NETWORK AND SURFACE

RESPONSE METHODOLOGY

Amber Batwara and Prateek Verma

Department of Mechanical Engineering, RIET, Jaipur, 302033.

ABSTRACT

Optimization of machining parameters is very valuable to maintain the accuracy of the components and

obtain cost effective Machining.MRR (material removal rate) and surface roughness is playing primary

role in manufacturing using contemporary CNC (computer numerical controlled) machines, in the case of

mass manufacturing. In present study experimental and work is done for optimization of process

parameters. In experimental work total 32 experiments are designed according DOE method “Mixed

taguchi”. Three factors are selected for experimental work. Depth of cut, speed and feed rate is selected

factors for experimental work. All experiments are carried out in CIPET, Jaipur. Two responses are find

out in this work and are following: first one is material removal rate (MRR) and second response is surface

roughness (Ra) measurement. An artificial neural network is ‘Feed Forward Back Propagation’ type

model of developing the analysis and prediction of surface roughness and MRR with relationship between

all input process parameters.

KEYWORDS

Turning CNC, DOE, ANOVA, model equation, ANN

1. INTRODUCTION

Today, CNC machining has grown to be an indispensible part of machining industry. CNC

machines having good accuracy, precision, good surface finishing achieved by compression than

conventional manufacturing machines. Surface finish plays a significant role during machining of

any of the component. A highly surface finish improves fatigue strength, creep failure, corrosion

resistance and better finished components increase also the productivity & economics of any

industry [9]. CNC machine performance and product characteristics are depends on the process

parameters. Out of the various parameters we select material removal rate (MRR) and surface

roughness for study in the present work as considered also the manufacturing goal. These two

factors directly affect the cost of machining and the machining hour rate. The machining

parameters namely cutting speed, feed rate and depth of cut were considered. The main objective

is to find the optimized set of values for maximizing the MRR and achieve good surface finish

[5]. L32 Mixed taguchi was used for experimentation. All the response graph and analysis of

variance (ANOVA) shows that the feed rate has strongest effect on surface roughness and MRR

is dependent on RPM and depth of cut. Surface response methodology developed between the

machining parameters and responses and confirmation experiments reveal that the good


48

agreement with the regression models. Artificial neutral network is applied to experimental

results to find prediction results for two response parameters.

The complexity of the machining process performing optimization of a machining process is very

difficult. Therefore ANN is use for mapping the input/output relationships and as well as also

doing computing. To implement the general functions of human brain artificial neural network

model is developed. Artificial neural network (ANN) is doing works like a human brain for the

implementation of the functions such as association, self-organization and generalization. It can

approximate any functions more efficiently, thus it is suitable for modelling of any non-linear

process. It can capture complex input–output relationships and having the good learning ability,

generalization ability. [2]

2. EXPERIMENTAL WORK

The experiment was carried out in a ‘VX-135 Junior’ CNC Lathe. The experiments were

performed in dry environment without any cutting fluid. CNC control system is Fanuc Oi mate-

TD.CNC part programs were used for doing the turning operation. Surface roughness measure

with help of 3D profilometer . In this study effect of process parameters on turning of MS test

piece is experimentally analysis using design of experiment method. Total 32 experiments are

designed using surface response special class named “mixture DOE method”. All experiments are

done in CPET, Jaipur CNC lathe centre. Table1 show levels and factors which are used in this

study. Mixture based surface response method is used for complex experiments results. In figure

1 shown simple turning operation and figure 2 shown CNC lathe installed at CIEPT, Jaipur.

Figure 1. Turning operation

Figure 2. CNC lathe installed at CIEPT Jaipur

3. DESIGN OF EXPERIMENT AND RESEARCH METHODOLOGY It was R.A Fisher who at first introduced DOE in 1920 in England. It’s a powerful statistical

technique which assists in studying multiple variables and in maximization of learning using a


49

minimum of resources.DOE highlights the important causes and variables with determination of

main effects reducing the variation and cost reduction for the opening up the tolerance on

unimportant variables. [6]

The effects of process parameters were studied by various researchers from last decades. Design

of experiments is very difficult to for any type of research and for resolving this problem

researchers use scientific approach, which is known as “DESIGN OF EXPERIMENT”. With help

of D.O.E. techniques any researcher can determine important factors which are responsible for

output result variation of experiments. DOE can found optimum solution for particular

experiments. In this study mixture taguchi methods are used for ANOVA analysis. The entire

task performs in MINITAB software.

Table 1. Levels and factors

Level Depth of Cut (mm) RPM Feed Rate (in

mm)

Low 0.25 350 0.25

High 0.50 1400 1.0

Total 32 experiments are show in table 1. In this method factor 1 is divided in two levels and

remaining others is divided in 4 levels which are presented in table 2.

Table 2. Total 32 Experiments according DOE Surface Response

Experiment No. F1 (Depth of Cut) F2 (RPM) F2 (Feed)

1 0.25 350 0.25

2 0.25 350 0.5

3 0.25 350 0.75

4 0.25 350 1

5 0.25 700 0.25

6 0.25 700 0.5

7 0.25 700 0.75

8 0.25 700 1

9 0.25 1050 0.25

10 0.25 1050 0.5

11 0.25 1050 0.75

12 0.25 1050 1

13 0.25 1400 0.25

14 0.25 1400 0.5

15 0.25 1400 0.75

16 0.25 1400 1

17 0.5 350 0.25

18 0.5 350 0.5

19 0.5 350 0.75

20 0.5 350 1

21 0.5 700 0.25


50

Experiment No. F1 (Depth of Cut) F2 (RPM) F2 (Feed)

22 0.5 700 0.5

23 0.5 700 0.75

24 0.5 700 1

25 0.5 1050 0.25

26 0.5 1050 0.5

27 0.5 1050 0.75

28 0.5 1050 1

29 0.5 1400 0.25

30 0.5 1400 0.5

31 0.5 1400 0.75

32 0.5 1400 1

All experiments are conducted in CNC lathe turning machine. Tool is made of high carbide steel

and constant for this study. After all experiments completion recorded data is presented in table 3.

Table 3. Experimental data record during research work

Experi

ment

No.

F1

(Depth

of Cut)

F2

(RP

M)

F2

(Fee

d)

Initial

Weight

(gm)

Final

Weight

Turning

Operation

Time (sec)

MRR

(in3/sec)

Ra

(um)

1 0.25 350 0.25 191 180 20 0.07 4.94

2 0.25 350 0.5 191 180 18 0.08 4.46

3 0.25 350 0.75 187 175 15 0.11 3.99

4 0.25 350 1 192 175 12 0.18 3.52

5 0.25 700 0.25 190 180 23 0.06 3.84

6 0.25 700 0.5 189 175 12 0.15 3.37

7 0.25 700 0.75 188 180 8 0.14 2.90

8 0.25 700 1 192 180 7 0.22 2.43

9 0.25 1050 0.25 189 180 15 0.08 2.75

10 0.25 1050 0.5 380 345 8.7 0.51 2.27

11 0.25 1050 0.75 380 345 7 0.64 1.80

12 0.25 1050 1 380 340 7.6 0.67 1.33

13 0.25 1400 0.25 380 345 8.8 0.51 1.65

14 0.25 1400 0.5 380 345 7.2 0.62 1.18

15 0.25 1400 0.75 380 345 7.3 0.61 0.71

16 0.25 1400 1 380 345 8.9 0.50 0.24

17 0.5 350 0.25 380 350 24.3 0.16 4.59

18 0.5 350 0.5 380 335 17.9 0.32 4.12

19 0.5 350 0.75 380 360 14.4 0.18 3.65

20 0.5 350 1 380 360 12.8 0.20 3.18

21 0.5 700 0.25 330 300 17 0.22 3.49

22 0.5 700 0.5 330 305 11 0.29 3.02


51

Two responses are solved in present study; first one is material removal rate and second is surface

roughness.

Material removal rate is the volume of material removed in per unit time from the surface of work

piece. We can also calculate material removal rate as the volume of material removed divided by

the time taken to cut. The volume removed is the initial volume of the work piece minus the final

volume.

MRR (in3/sec) = Initial Weight (gm) - Final Weight / 7.85* Turning operation time (sec.)

Roughness is a measure of the texture of a surface. It is quantified by the vertical deviations of a

real surface from its ideal form. If these deviations are large, the surface is rough; if they are

small the surface is smooth. Roughness is typically considered to be the high frequency, short

wavelength component of a measured surface. Surface measurement is also measured for all 32

cases using manual surface roughness measurement device, available in local company (Ganesh

hardware and Sheet Metal Products, Sitapura) in Jaipur. All though surface roughness for all 32

cases is in good condition because of CNC machine standard accuracy. But some variations are

seen after results so DOE analysis is done for Ra also. In table 3 MMR and surface roughness is

presented for all 32 experiments.

4. RESULT AND DISCUSSION

All experiments were designed according to DOE technique (Mixed taguchi), which were

presented in table 2 and experimental results in term of MRR and surface roughness is presented

in table .Main outcomes focused in this study are following: [ Surface response methodology,

ANOVA Analysis, , Model equations generation and ANN approach ].

4.1 Surface response methodology for surface roughness

The analysis of variance (ANOVA) is applied for this study and results are shown in table 4

respectively. In this analysis F-Test is conduct to compare a residual variance and a model

variance. F value was calculated from a model mean square divided by residual mean square

value. If the value of f was approaching to one, its means both variances were same according F

value highest was best to find critical input parameter.

23 0.5 700 0.75 330 345 7.5 0.08 2.55

24 0.5 700 1 330 315 6.8 0.28 2.08

25 0.5 1050 0.25 330 315 14.3 0.13 2.40

26 0.5 1050 0.5 330 305 9.8 0.32 1.93

27 0.5 1050 0.75 330 305 7 0.45 1.46

28 0.5 1050 1 330 320 6 0.21 0.98

29 0.5 1400 0.25 330 300 11.3 0.34 1.30

30 0.5 1400 0.5 350 345 8.9 0.07 0.83

31 0.5 1400 0.75 330 315 6.4 0.30 0.36

32 0.5 1400 1 330 315 5.3 0.36 0.02


52

Table 4. Analysis of Variance for surface roughness

According to result of Table 4 is list out the F value for regression models are very high and P

value is very less (approx 0.0000) .It means that all cases were significant. Various researchers

found that if p value was very small (less than 0.05) then in terms of regression model have a

significant effect to the response from literature review.

ANOVA analysis is also tell that all three factor has very low p value three and have acceptable p

value so it can concluded that surface roughness are affected by mainly three factor, this.

Analysis of variance is calculated for 95% Confidence interval (CI) for linear, product and square

analysis using Minitab software. Model equations for surface roughness are presented in below

Model Equation

Ra (um) = 6.9681 -1.5558 F1(Depth of cut) -0.003257 F2(RPM) -2.0625 F3(Feed)+0.000000

F2(RPM)* F2(RPM) +0.0652 F3(Feed)* F3(Feed) +0.000112 F1(Depth of cut)* F2(RPM)

+0.156 F1(Depth of cut)* F3(Feed) +0.000067 F2(RPM)* F3(Feed)

Normal probability plot and versus fits and versus order plot for surface response are shown in

Fig 3, 4. Regression models adequacy shall be inspected to confirm that the all models have

extracted all relevant information from all simulated cases. If distribution of residuals were

normal, then the Regression equations results should be adequate

For normality test, the Hypotheses are listed below -

Null Hypothesis: the residual data should follow normal distribution

Alternative Hypothesis: the residual data does not follow a normal distribution

Source DF Adj SS Adj MS F-Value P-Value

Model 8 57.1607 7.1451 17626.73 0.000

Linear 3 57.1560 19.0520 47000.75 0.000

F1(Depth of cut) 1 0.9252 0.9252 2282.48 0.000

F2(RPM) 1 47.5469 47.5469 117296.85 0.000

F3(Feed) 1 8.6839 8.6839 21422.94 0.000

Square 2 0.0011 0.0005 1.31 0.289

F2(RPM)* F2(RPM) 1 0.0005 0.0005 1.31 0.264

F3(Feed)* F3(Feed) 1 0.0005 0.0005 1.31 0.264

2-Way Interaction 3 0.0036 0.0012 2.99 0.052

F1(Depth of cut)*

F2(RPM) 1 0.0010 0.0010 2.36 0.138

F1(Depth of cut)*

F3(Feed) 1 0.0010 0.0010 2.36 0.138

F2(RPM)* F3(Feed) 1 0.0017 0.0017 4.24 0.051

Error 23 0.0093 0.0004

Total 31 57.1700


53

Figure 3. Normal probability for surface roughness.

Figure 4. Versus fits and versus order for surface roughness

4.2 Surface response methodology for MRR

The analysis of variance is calculated for this study and results are shown in table 5 respectively

Table 5. Analysis of Variance for MRR

Source DF Adj SS Adj MS F-Value P-Value

Model 8 0.73380 0.091725 5.84 0.000

Linear 3 0.47635 0.0158783 10.10 0.000

F1(Depth of cut) 1 0.04685 0.046851 2.98 0.098

F2(RPM) 1 0.36179 0.361792 23.02 0.000

F3(Feed) 1 0.06771 0.067706 4.31 0.049


54

Square 2 0.01517 0.007583 0.48 0.623

F2(RPM)* F2(RPM) 1 0.00050 0.000502 0.03 0.860

F3(Feed)* F3(Feed) 1 0.01466 0.014663 0.93 0.344

2-Way Interaction 3 0.24229 0.080763 5.14 0.007

F1(Depth of cut)*

F2(RPM) 1 0.21206 0.027019 13.49 0.001

F1(Depth of cut)*

F3(Feed) 1 0.02702 0.003214 1.72 0.203

F2(RPM)* F3(Feed) 1 0.00321 0.015716 0.20 0.655

Error 23 0.36146

Total 31 1.09527

ANOVA analysis is also tell that RPM and feed factor has very low p value, and has acceptable p

value in all three factors. So it can conclude that MRR are affected by mainly RPM and feed

factor. Analysis of variance is calculated for 95% Confidence interval (CI) for linear, product and

square analysis using Minitab software. Model equations for surface roughness are presented in

below

Model Equation -Regression Equation

MRR =0.720+ 1.670 F1(Depth of cut) +0.000782 F2(RPM) +0.824 F3(Feed)+0.000000

F2(RPM)* F2(RPM) -0.342 F3(Feed)* F3(Feed) – 0.001664 F1(Depth of cut)* F2(RPM) -0.832

F1(Depth of cut)* F3(Feed) +0.000092 F2(RPM)* F3(Feed)

Normal probability for MRR is shown in Fig 5.

Figure 5. Normal probability for MRR


55

Table 6.Regression Prediction results for Ra (um) and MRR (in3/sec) for all experiments

4.3 Artificial Neural Network for Surface Roughness (Ra)

In this study ANN method is also used for prediction of outcome data gained by experimental

work. MATLAB software is used for ANN method. Neural networks (NNs), have been widely

used many applications include data fitting, clustering, pattern recognition, function

approximation, optimization, simulation, time series expansion and dynamic system modeling

and controlling [2]. Neural network also overcome the limitations of the conventional approaches

by extracting the desired information by using the input data. It can continuously be re-trained, so

that it can give a new data. An ANN has been deal with the problems involving imprecise or

incomplete input information. The selection of ANN is most important for good quality

prediction. As there are 3 input variables with 1 output variable which shown in figure 6. A

MATLAB R2013 version is used to convert the earlier developed ANN model.


56

Figure 6. MS error for Surface roughness

Figure 7. Histogram for Ra

Figure 7 represent histogram diagram which can give an indication of outliers. Performance

Epoch diagrams shown in figure 6 which represent that the validation and test curves are very

similar. Figure 8 represent the training, validation, and testing data. The perfect result – outputs =

targets represents in each plot with the dashed line.

0 1 2 3 4 5 6 7

10-20

10-15

10-10

10-5

100

Best Validation Performance is 0.022513 at epoch 5

Me

an

Sq

ua

re

d E

rro

r (m

se

)

7 Epochs

Train

Validation

Test

Best

0

5

10

15

20

Error Histogram with 20 Bins

Ins

tan

ce

s

Errors = Targets - Outputs

-0.3

078

-0.2

855

-0.2

633

-0.2

411

-0.2

189

-0.1

967

-0.1

745

-0.1

523

-0.1

3

-0.1

078

-0.0

8561

-0.0

6339

-0.0

4117

-0.0

1896

0.0

03257

0.0

2547

0.0

4769

0.0

699

0.0

9212

0.1

143

Training

Validation

Test

Zero Error


57

Figure 8 Regression Results for Ra(um)

4.4 Artificial Neural Network for MRR

Figure 9. Function Fitting Neural Network Diagram

Figure 10. Histogram for MRR Figure 11. MS error for MRR

Figure 10 represent histogram diagram which can give an indication of outliers. The data points

where the fit is significantly worse than the majority of data. Performance Epoch diagrams

shown in figure 11 which represent that the validation and test curves are very similar.

1 2 3 4

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Target

Outp

ut ~

= 1*

Targ

et +

-0.0

003

Training: R=1

Data

Fit

Y = T

1 2 3 4

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Target

Outp

ut ~

= 0.

88*T

arge

t + 0

.49

Validation: R=0.99337

Data

Fit

Y = T

1 2 3 4

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Target

Outp

ut ~

= 0.

96*T

arge

t + 0

.18

Test: R=0.98701

Data

Fit

Y = T

1 2 3 4

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Target

Outp

ut ~

= 0.

98*T

arge

t + 0

.079

All: R=0.99753

Data

Fit

Y = T

0

1

2

3

4

5

6

7

Error Histogram with 20 Bins

Ins

tan

ce

s

Errors = Targets - Outputs

-0.1

441

-0.1

137

-0.0

832

-0.0

5273

-0.0

2226

0.0

08214

0.0

3868

0.0

6915

0.0

9962

0.1

301

0.1

606

0.1

91

0.2

215

0.2

52

0.2

824

0.3

129

0.3

434

0.3

739

0.4

043

0.4

348

Training

Validation

Test

Zero Error

0 1 2 3 4 5 6 7 8 9

10-15

10-10

10-5

100

Best Validation Performance is 0.0026086 at epoch 5

Me

an

Sq

ua

red

Err

or

(m

se

)

9 Epochs

Train

Validation

Test

Best


58

Figure 12 represent the training, validation, and testing data. The meaning of dashed line in each

plot is the targets = perfect result – outputs

.

Figure 12. Regression Results for Ra( um)

Table 7. ANN Prediction results for Ra (um) and MRR (in3/sec) for all experiments

Experiment

No.

F1

(Depth

of Cut)

F2

(RPM)

F2

(Feed)

Ra

(um)

Ra

(Predica

ted)

MRR

(in3/se

c)

Predicated

MRR

1 0.25 350 0.25 4.94 4.94 0.07 -0.1363

2 0.25 350 0.5 4.46 4.40 0.08 -0.3700

3 0.25 350 0.75 3.99 3.97 0.11 -0.2168

4 0.25 350 1 3.52 3.51 0.18 0.1405

5 0.25 700 0.25 3.84 3.83 0.06 0.0350

6 0.25 700 0.5 3.37 3.36 0.15 0.1138

7 0.25 700 0.75 2.90 2.90 0.14 0.1447

8 0.25 700 1 2.43 2.52 0.22 0.2319

9 0.25 1050 0.25 2.75 2.97 0.08 0.1305

10 0.25 1050 0.5 2.27 2.26 0.51 0.4704

11 0.25 1050 0.75 1.80 1.80 0.64 0.6309

12 0.25 1050 1 1.33 1.33 0.67 0.6309

13 0.25 1400 0.25 1.65 1.65 0.51 0.5486

14 0.25 1400 0.5 1.18 1.18 0.62 0.6436

15 0.25 1400 0.75 0.71 0.70 0.61 0.6579

16 0.25 1400 1 0.24 0.55 0.50 0.6574

17 0.5 350 0.25 4.59 4.58 0.16 0.2959

18 0.5 350 0.5 4.12 4.11 0.32 0.3238

19 0.5 350 0.75 3.65 3.64 0.18 0.2688

20 0.5 350 1 3.18 3.40 0.20 0.1934

21 0.5 700 0.25 3.49 3.48 0.22 0.2192

-0.2 0 0.2 0.4 0.6

-0.2

0

0.2

0.4

0.6

Target

Outp

ut ~

= 0.

98*T

arge

t + 0

.019

Training: R=0.98748

Data

Fit

Y = T

-0.2 0 0.2 0.4 0.6

-0.2

0

0.2

0.4

0.6

Target

Outp

ut ~

= 0.

81*T

arge

t + 0

.067

Validation: R=0.92845

Data

Fit

Y = T

-0.2 0 0.2 0.4 0.6

-0.2

0

0.2

0.4

0.6

Target

Outp

ut ~

= 2.

1*Ta

rget

+ -0

.34

Test: R=0.89148

Data

Fit

Y = T

-0.2 0 0.2 0.4 0.6

-0.2

0

0.2

0.4

0.6

Target

Outp

ut ~

= 1.

2*Ta

rget

+ -0

.059

All: R=0.8884

Data

Fit

Y = T


59

22 0.5 700 0.5 3.02 3.01 0.29 0.3097

23 0.5 700 0.75 2.55 2.54 0.08 0.1201

24 0.5 700 1 2.08 2.30 0.28 0.2982

25 0.5 1050 0.25 2.40 2.47 0.13 0.1977

26 0.5 1050 0.5 1.93 1.92 0.32 0.3387

27 0.5 1050 0.75 1.46 1.45 0.45 0.4205

28 0.5 1050 1 0.98 0.97 0.21 0.2480

29 0.5 1400 0.25 1.30 1.29 0.34 0.3742

30 0.5 1400 0.5 0.83 0.70 0.07 0.1263

31 0.5 1400 0.75 0.36 0.35 0.30 0.2456

32 0.5 1400 1 0.02 0.23 0.36 0.3669

5. CONCLUSIONS

1.Model equations for response MRR and surface roughness was predict accurately with Minitab

software and show 90% good prediction for responses and can be used by any cutting based

machining process manufacture.

2.MRR and surface roughness also was predicted by ANN approach. This paper has successfully

established the new process model to predict the surface roughness and MRR in different

practical applications. Model equations gives values of the process parameters for controlled

process models in better way if they are employed in different industrial applications.

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influence of process parameters on surface … · 2018. 10. 3. · agreement with the regression...

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