This document summarizes an experimental study that investigated the effects of machining parameters on surface roughness and material removal rate during CNC turning. 32 experiments were conducted varying depth of cut, spindle speed, and feed rate according to a mixed Taguchi design of experiments. The results found that feed rate had the strongest influence on surface roughness, while material removal rate depended most on spindle speed and depth of cut. Artificial neural networks were also used to model the relationships between input parameters and output responses. Analysis of variance confirmed that all input factors significantly affected the responses according to the developed regression models.

1. 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

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

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

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

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

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

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

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

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

10. International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.5, No.1, February 2016
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
10
0
Best Validation Performance is 0.022513 at epoch 5
MeanSquaredError(mse)
7 Epochs
Train
Validation
Test
Best
0
5
10
15
20
Error Histogram with 20 Bins
Instances
Errors = Targets - Outputs
-0.3078
-0.2855
-0.2633
-0.2411
-0.2189
-0.1967
-0.1745
-0.1523
-0.13
-0.1078
-0.08561
-0.06339
-0.04117
-0.01896
0.003257
0.02547
0.04769
0.0699
0.09212
0.1143
Training
Validation
Test
Zero Error

11. International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.5, No.1, February 2016
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
Output~=1*Target+-0.0003 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
Output~=0.88*Target+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
Output~=0.96*Target+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
Output~=0.98*Target+0.079
All: R=0.99753
Data
Fit
Y = T
0
1
2
3
4
5
6
7
Error Histogram with 20 Bins
Instances
Errors = Targets - Outputs
-0.1441
-0.1137
-0.0832
-0.05273
-0.02226
0.008214
0.03868
0.06915
0.09962
0.1301
0.1606
0.191
0.2215
0.252
0.2824
0.3129
0.3434
0.3739
0.4043
0.4348
Training
Validation
Test
Zero Error
0 1 2 3 4 5 6 7 8 9
10
-15
10
-10
10
-5
10
0
Best Validation Performance is 0.0026086 at epoch 5
MeanSquaredError(mse)
9 Epochs
Train
Validation
Test
Best

12. International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.5, No.1, February 2016
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
Output~=0.98*Target+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
Output~=0.81*Target+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
Output~=2.1*Target+-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
Output~=1.2*Target+-0.059
All: R=0.8884
Data
Fit
Y = T

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