DOI : 10.17577/IJERTV8IS080148
- Open Access
- Total Downloads : 84
- Authors : Sajan George , Dileeplal. J
- Paper ID : IJERTV8IS080148
- Volume & Issue : Volume 08, Issue 08 (August 2019)
- Published (First Online): 23-08-2019
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Experimental Investigations of Process Parameters on CNC Turning of AISI 304l
Sajan George PG Scholar,
Department of Mechanical Engineering College of Engineering and Management Punnapra
Alappuzha, Kerala
Dileeplal. J
Associate Professor Department of Mechanical Engineering
College of Engineering Perumon Kollam, Kerala
AbstractThis paper focuses on the effect of selected input turning parameters like spindle speed, depth of cut, feed, and tool nose radius on the output characteristics like surface roughness (SR), material removal rate (MRR), and roundness error (RE). In this research, stainless steel AISI 304L is used as the work piece with carbide insert tool. The optimum process parameters and corresponding output responses are found out using Taguchi analysis. The multi-objective optimization based on grey relational analysis (GRG) is used to attain maximum material removal rate simultaneously with minimum surface roughness and roundness error. Artificial neural network (ANN) model is developed using back propagation algorithm to predict the performance characteristics and found that the experimental values are closely related to the ANN predicted model
KeywordsSurface roughness, material removal rate, roundness error, grey relational grade, artificial neural network.
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INTRODUCTION
Turning operation is a basic type of metal machining operation that is widely used in industries and research & development. With the increasing demand for a quality product as well as for higher productivity, turning operation needs to be performed more efficiently. Therefore, the optimization of process parameters and modeling is essential to achieve a high-quality product with the reduction of manufacturing cost.
Lavanya et. al (2013) optimized the process parameters such as speed, feed, and depth of cut in turning operation of AISI- 1016 alloy steels using CBN insert. It was noted that feed was the most influenced cutting parameter on the surface roughness followed by speed and depth of cut. Rajendra and Deepak (2015) studied the effect of process parameters like feed rate cutting speed and depth of cut for material removal rate on Al6061. It was found that the feed rate was the most influential parameter that affects the material removal rate while machining Al6061 aluminum alloy. Jaina et. al (2015) explained the effect of machining parameters for turning operation on Inconel-625 in CNC machining with the help of Taguchi Method. The experiment showed that the insert spindle speed and feed rate that effects mostly on the material removal rate in turning smart alloy Inconel-25. Sangwana (2015) presented an approach for determining the optimum turning machining parameters leading to minimum surface roughness by integrating artificial neural network (ANN) and genetic algorithm (GA) on titanium alloy Ti-6Al-4V. It has also been observed that the increase in depth of cut and cutting speed decreases the surface roughness and the predicted results using ANN and integrated (GA) indicated close
agreement between the predicted values and experimental values. Raykar (2015) optimized the turning process parameters for surface roughness, power consumption, material removal rate and cutting time on AL 7075 aluminum alloy with coated carbide insert and dry machining condition. Saha and Majumder (2016) described the effect of turning parameters speed, feed, depth of cut on ASTM A36 mild steel bar for the frequency of tool vibration and average surface roughness in turning. Asilturk (2016) presented a study about the effect of process parameters spindle rotational speed, feed rate, depth of cut and tool tip radius for surface roughness values (Ra and Rz) on Co28Cr6Mo material. Response surface methodology based on the Taguchi method is used for the experiment. It was found that tool tip radius was the most influential parameter on surface roughness. Niranjan and Shivashankar (2017) optimized the cutting process parameters cutting speed, feed, depth of cut on AL6061 using ANOVA and Taguchi method.
From the literatures, it is found that limited works were done in the optimization of the turning process of stainless steel AISI 304L. Considering the above gap, it is decided to investigate the influence of various process parameters on performance measures in the turning process of stainless steel AISI 304L. It is understood that spindle speed, depth of cut, feed rate and tool nose radius are important turning input parameters. Material removal rate, surface roughness and roundness error have a significant role in the quality of the part machined. Material removal rate has to be maximum with minimum surface roughness and roundness error. In the literature, Taguchis grey relational method is most commonly used to find multiple optimum combinations and artificial neural network (ANN) is used for performance prediction of various machining operations.
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DESIGN OF EXPERIMENTS
The process parameters and its levels are listed in the Table 1.
Table 1: Machining Parameters and Levels
Sl.
No.
Process Parameter
Level 1
Level 2
Level 3
1
Spindle speed (rpm)
500
1000
1500
2
Depth of cut (mm)
0.2
0.4
0.6
3
Feed (mm/rev)
0.05
0.1
0.15
4
Tool nose radius (mm)
0.2
0.4
0.8
The designed combinations of input parameters based on L9 orthogonal array are shown in Table 2.
Sl.
No.
Spindle speed (rpm)
Depth of cut (mm)
Feed (mm/rev)
Tool nose radius (mm)
1
500
0.2
0.05
0.2
2
500
0.4
0.1
0.4
3
500
0.6
0.15
0.8
4
1000
0.2
0.1
0.8
5
1000
0.4
0.15
0.2
6
1000
0.6
0.05
0.4
7
1500
0.2
0.15
0.4
8
1500
0.4
0.05
0.8
9
1500
0.6
0.1
0.2
Sl.
No.
Spindle speed (rpm)
Depth of cut (mm)
Feed (mm/rev)
Tool nose radius (mm)
1
500
0.2
0.05
0.2
2
500
0.4
0.1
0.4
3
500
0.6
0.15
0.8
4
1000
0.2
0.1
0.8
5
1000
0.4
0.15
0.2
6
td>
1000
0.6
0.05
0.4
7
1500
0.2
0.15
0.4
8
1500
0.4
0.05
0.8
9
1500
0.6
0.1
0.2
Table 2: Combination of Process Parameters
The response table of S/N ratio for SR are shown in the Table 5.
Table 5: Response Table of S/N Ratios for SR
Level
Spindle speed
Depth of cut
Feed
Tool nose radius
1
0.67368
0.08727
-0.74217
-8.00583
2
-6.09808
-2.76436
-1.40203
1.05404
3
-1.98954
-4.73686
-5.26975
-0.46215
Delta
6.77176
4.82413
4.52758
9.05987
Rank
2
3
4
1
-
RESULTS AND DISCUSSIONS
The results of 9 experiments with three replications are shown in Table 3.
Table 3: Experimental Results
Sl. No.
SR
(m)
MRR (gm/min)
RE
(mm)
1
1.14
7.52
0.032
1.06
7.86
0.034
1.00
8.12
0.031
2
0.54
16.61
0.021
0.55
13.98
0.023
0.6
12.78
0.025
3
1.32
22.25
0.036
1.40
23.59
0.035
1.22
23.73
0.031
4
1.04
9.09
0.030
1.12
9.39
0.031
1.00
10.15
0.030
5
5.42
14.71
0.052
5.58
15.21
0.055
5.34
14.71
0.051
6
1.36
22.73
0.036
1.29
22.73
0.033
1.62
24.61
0.039
7
0.92
5.84
0.029
0.80
6.76
0.028
0.86
6.83
0.028
8
0.72
9.74
0.026
0.84
12.30
0.028
0.96
11.36
0.029
9
2.74
24.51
0.045
2.86
22.33
0.047
2.58
21.95
0.043
The optimum combinations of process parameters are obtained from the S/N ratios. Lower the better characteristic is used to calculate S/N ratio for lower surface roughness & roundness error and larger the better characteristic is used for getting good material removal rate. The S/N ratios for SR, MRR and RE are given in Table 4.
Sl. No.
SR
MRR
RE
1
-0.5731
17.8660
29.8005
2
4.9735
23.0499
32.7436
3
-2.3811
27.2949
29.3529
4
-0.4610
19.5663
30.3605
5
-14.7241
23.4469
25.5648
6
-3.1091
27.3501
28.8539
7
1.2960
16.1594
30.9528
8
1.4557
20.8093
31.1520
9
-8.7203
27.1778
26.9300
Sl. No.
SR
MRR
RE
1
-0.5731
17.8660
29.8005
2
4.9735
23.0499
32.7436
3
-2.3811
27.2949
29.3529
4
-0.4610
19.5663
30.3605
5
-14.7241
23.4469
25.5648
6
-3.1091
27.3501
28.8539
7
1.2960
16.1594
30.9528
8
1.4557
20.8093
31.1520
9
-8.7203
27.1778
26.9300
Table 4: S/N Ratios for SR, MRR, and RE
It can be seen that tool nose radius has the highest delta value and hence tool nose radius has the highest influence on SR. It is also clear that the optimum process parameters for getting the optimum SR is spindle speed 500 rpm, depth of cut 0.2 mm, feed 0.05 mm/rev and nose radius 0.4 mm (Figure 1).
Figure 1: Main Effects Plot for SN Ratios of SR
The regression equation for SR is found as follows; SR=0.27+0.000494 spindle speed+2.07 depth of cut
+14.30 feed rate-2.830 tool nose radius (1)
The response table of S/N ratios for MRR are shown in the Table 6.
Table 6: Response Table of S/N Ratios for MRR
Level
Spindle speed
Depth of cut
Feed
Tool nose radius
1
22.74
17.86
22.01
22.83
2
23.45
22.44
23.26
22.19
3
21.38
27.27
22.30
22.56
Delta
2.07
9.41
1.26
0.64
Rank
2
1
3
4
Here depth of cut has the highest delta value and hence influences the material removal rate the most. The optimum process parameters are spindle speed 1000 rpm, depth of cut
0.6 mm, feed 0.10 mm/rev and nose radius 0.2 mm (Figure 2). The regression equation for MRR is found as follows;
MRR=0.98 -0.001647 spindle speed+38.02 depth of cut
+7.40 feed rate-0.89 tool nose radius (2)
Figure 2: Main Effects Plot of SN Ratios for MRR
The response table of S/N ratio for RE are shown in the Table 7.
Table 8: Optimum Combination of Process Parameters
Process Parameter
SR
(m)
/td>
MRR
(gm/min)
RE
(mm)
Spindle speed (rpm)
500
1000
500
Depth of cut (mm)
0.2
0.6
0.2
Feed (mm/rev)
0.05
0.10
0.10
Tool nose radius (mm)
0.4
0.2
0.4
The optimum output responses are found using regression analysis and confirmation experiment as shown in Table 9.
Sl.
No.
Output Response
Optimum Value
Regression Equation
Confirmation Experiment
1
Surface roughness
(m)
0.5574
0.56
2
Material removal rate (gm/min)
22.707
21.60
3
Roundness error (mm)
0.0239
0.025
Sl.
No.
Output Response
Optimum Value
Regression Equation
Confirmation Experiment
1
Surface roughness
(m)
0.5574
0.56
2
Material removal rate (gm/min)
22.707
21.60
3
Roundness error (mm)
0.0239
0.025
Table 9: Optimum Output Responses
Table 7: Response Table for S/N Ratios of RE
Level
Spindle speed
Depth of cut
Feed
Tool nose radius
1
30.63
30.37
29.94
27.43
2
28.26
29.82
30.01
30.85
3
29.68
28.38
28.62
30.29
Delta
2.37
1.99
1.39
3.42
Rank
2
3
4
1
Here tool nose radius has the highest delta value and hence influence the roundness the mostly. The optimum process parameters are spindle speed 500 rpm, depth of cut 0.2 mm, feed 0.10 mm/rev and nose radius 0.4 mm (Figure 3). The regression equation for RE is found as follows;
RN=0.02433+0.000004 spindle speed+0.02000 depth of cut
+0.0633 feed rate – 0.01754 tool nose radius (3)
Figure 3: Main Effects Plot of SN Ratios for RE
The results obtained from Taguchi optimization technique to get the minimum surface roughness, maximum material removal rate and minimum roundness error are shown in the Table 8.
-
MULTI OBJECTIVE OPTIMIZATION
Grey relational analysis (GRA) is used for multi optimization of three performance characteristics simultaneously. The initial experimental data are normalized in the range between zero and one and deviation sequences are calculated. Subsequently, grey relational coefficient for each performance characteristics are calculated with this pre- processed data and finally grey relational grades are calculated for multi-objective optimization. The grey relational coefficients (GRC) and grey relational grades (GRG) are given in Table 10.
Table 10: GRC and GRG
Sl. No.
GRC (SR)
GRC (MRR)
GRC (RE)
GRG (yi)
Rank
1
0.808
0.354
0.607
0.590
21
2
0.829
0.359
0.567
0.585
23
3
0.846
0.363
0.630
0.613
19
4
1.000
0.540
1.000
0.847
1
5
0.996
0.469
0.895
0.787
2
6
0.977
0.442
0.810
0.743
4
7
0.764
0.799
0.531
0.698
9
8
0.746
0.902
0.548
0.732
5
9
0.788
0.914
0.630
0.777
3
10
0.834
0.377
0.654
0.622
18
11
0.813
0.381
0.630
0.608
20
12
0.846
0.394
0.654
0.631
16
13
0.341
0.487
0.354
0.394
26
14
0.333
0.500
0.333
0.389
27
15
0.344
0.487
0.362
0.398
25
16
0.754
0.833
0.531
0.706
8
17
0.771
0.833
0.586
0.730
6
18
0.700
1.000
0.486
0.729
7
19
0.869
0.333
0.680
0.627
17
20
0.906
0.345
0.708
0.653
12
21
0.887
0.345
0.708
0.647
14
22
0.933
0.387
0.773
0.698
10
23
0.894
0.433
0.708
0.678
11
24
0.857
0.415
0.680
0.651
13
25
0.534
0.989
0.415
0.646
15
26
0.521
0.805
0.395
0.574
24
27
0.553
0.779
0.436
0.589
22
The response table of S/N ratios for GRG are shown in the Table 11.
Table 11: Response Table of S/N Ratios for GRG
Level
Spindle speed
Depth of cut
Feed
Tool nose radius
1
-3.084
-4.167
-3.584
-5.675
2
-5.029
-4.525
-3.546
-2.915
3
-3.897
-3.318
-4.880
-3.419
Delta
1.945
1.206
1.334
2.761
Rank
2
4
3
1
The optimum combination for multiple objective grey relational analysis is spindle speed 500 rpm, depth of cut 0.6mm, feed 0.10 mm/rev and tool nose radius 0.4mm (Figure 4).
Figure 4: Main Effects Plot of SN Ratios for GRG
The regression equation for GRG is found as follows;
GRG=0.6260-0.000068 spindle speed+0.168 depth of cut- 0.739 feed+0.1942 nose radius (4)
Optimum GRG are calculated based on regression equation as well as confirmation experiment and the obtained values of GRG are 0.66298 and 0.651 respectively.
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ARTIFICIAL NEURAL NEWORK
As the machining process is non-linear and time-dependent, it is difficult for traditional identification methods to provide an accurate prediction model. To address this difficulty, non- traditional techniques such as artificial neural networks (ANN) have been introduced. The complete details of the ANN model development were well reported in several studies. Network structure 4-20-3 ((4 neurons in the input layer), (20 neurons in hidden layer) and (3 neurons in the output layer)) is found to be the most confidence and optimal ANN model. The feed forward back propagation algorithm is used for training the data. Then 60 percentage experimental data are used for training and 20 percentage for both validation and testing. Transfer function for the hidden layer is used as Trainslm. The MSE of training, validation and testing are 0.1916, 0.2857 and 0.124 respectively. The performance of the developed network examined based on the correlation coefficient (R-value) for both training and testing data for response measures prediction in the ANN model are determined as 0.99594 and 0.99926 respectively (Figure 5). The correlation coefficient for the validation data
set was found to be 0.99919 which is very close to 1, thus, indicating a strong correlation between the experimental results and network predictions.
The test data of responses of surface roughness, material removal rate and roundness are given in Table 12, Table 13, and Table 14. Mean square error of surface roughness, material removal rate and roundness are found as 0.02806,
-
and 0.0000114 respectively and total mean square error is found as 0.127.
Figure 5 Correlation Graph of Training, Testing and Validation
Table 12: Test Data of Surface Roughness
Exp no
Exp. Value
ANN Value
Error
2
1.14
1.0701
0.0699
7
1.32
1.31
0.01
15
5.34
5.50
0.16
17
1.29
1.4901
0.2001
26
2.86
2.5959
0.2641
Table 13: Test Data of Material Removal Rate
Exp no
Exp. Value
ANN Value
Error
2
7.86
7.81
0.05
7
22.25
23.16
0.91
15
14.71
14.96
0.25
17
22.73
23.6699
0.93
26
22.33
22.2057
0.1243
Table 14: Test Data of Roundness Error
Exp no
Exp. Value
ANN Value
Error
2
0.034
0.0315
0.0025
7
0.036
0.0330
0.003
15
0.051
0.0535
0.0025
17
0.033
0.0375
0.0045
26
0.047
0.0432
0.0038
-
-
CONCLUSION
An experimental study to analyze the effect of process parameters on the turning process of stainless steel AISI 304L has been carried out using carbide insert tool. The turning was carried out on SHAUBLIN 125 CCN CNC turning machine using L9 orthogonal array. The optimum combinations of
spindle speed, depth of cut, feed rate and tool nose radius to achieve minimum surface roughness, maximum material removal rate and minimum roundness error are found out. Grey relational analysis was used to identify optimum parameters under multi- objective criteria. The artificial neural network (ANN) model was developed and found effective for performance prediction of turning operation on stainless steel AISI 304L.
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