Prediction of Surface Roughness in turning process ...

ISSN: 1748-0345 (Online)

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Prediction of Surface Roughness in turning process by using artificial neural network model 1

B.Radha Krishnan, 2R.Giridharan, 3S.Rajaram, 4R.Sanjeevi, 5M.Ramesh 1,2,3,4,5 Assistant Professor, Mechanical Engineering K.Ramakrishnan College of Engineering [email protected]

Abstract This paper proposed a method for predicting the surface roughness using the machine vision system, which is influenced through an artificial network. Images at captured from the turning work piece and it will be extracted by using algorithm. Then the frequency extraction process has done by the Fourier transformation, which are major frequency F1 and principal component magnitude squared value F2. Experimental variable speed, depth of cut, feed rate and extracted image variables major frequency principal component magnitude squared value and gray level feed as input data and the output values are surface roughness as measured by the stylus probe. the ANN was trained by the input and output values, then it was tested by some input variables .based on the input the machine vision surface roughness values was getting as output values. Finally machine vision roughness value compared with stylus probe value for prediction of accuracy.

1. Introduction Surface roughness value influence the quality inspection parameter in many industries industries. Through proper optimum cutting parameters (tool feed rate, spindle speed, depth of cut) which is obtain the minimum surface roughness value. Artificial neural network is the first choice for the researcher to predict the surface roughness value. After the testing result comparison with stylus probe value there is attain the high accuracy level [1]. Computer vision is set new approach the for the inspection system. Machine vision has performed not only for the surface roughness and it also explore the many other fields, like welding inspection and shop floor. In this process the workpiece image captured by the computer vision system and it has extracted as the format of gray level co-occurrence matrix. Finally predict the surface roughness value based on the image extraction. Compare with the manual method it has used to rapid manufacturing process [2]. Manual measuring system has used for the measure for past half century. Due to the high production rate the conventional system has used as the wide range in production industries. Main disadvantage of the Conventional measurement is damage the probe tip by continuous usage in inspection process. Manual method does not fulfill the recent year automation development especially for rapid manufacturing. So that only most of the researcher concentrate in the machine vision inspection system to replace the manual inspection and measuring methods [3]. Before the 15 years ago the machine vision system appeared in research. The cost of the equipments and rough guide was creating the problem to further implementation. At the time the cost of the robot welding was very high. After the successful research and implement now it was used to improve the production range and automotive applications and etc.[4].Sub pixel value has extracted in gray scale image and it was used to predict the gray level range (Ga). In this process used the Olympus DSLR camera foe captured the specimen images. Speed, feed rate, depth of cut and frequency values feed into the input to the ANN. stylus probe roughness value train as the output. Finally the testing output was compared with the machine vision roughness value to analyze the accuracy level.

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2. Methodology a) System configuration The basic elements of the machine vision system consist with a positioning mechanism, backlighting and 18-megapixel DSLR camera (model: canon 1300d) having a picture resolution of 3,872 2,592 pixels. The camera was fitted with a 6X close-up lens and connected through USB cable to the computer. The camera was arranged on an X–Y-axis motion camera mount. Image quality and focus can ensure by adjusting the X-Y .The machine tool used is the Optimum Vario D320 × 630 conventional lathe, and the work piece material is Al 6063 of 14-mm diameter. The camera was set to a maximum shutter speed of 1/4000 seconds, so that blurring caused by the rotation of work piece can be prevented. Work piece was produced by turning at a feed rate of 0.1 mm/ revolutions and, 0.5 mm depth of cut. After that machining process surface roughness values of the work piece has been measured by using stylus probe method. Then the image was captured by the DSLR camera and import to the MAT LAB software for the grey scale conversion. As the output of the DSLR camera is in pixels and the surface roughness of work piece must be determined in micro meter. b) Cutting parameters and feature extraction The constant cutting parameters were selected for the Al 6063 Material, the cutting speed range 2000 rpm, the feed rate is 0.1 mm per revolution, the depth of cut is 0.5 mm. The average value of the surface roughness Ra is the mostly used as the output parameter in various industries. Fig.1 below the image captured by the canon 1300D DSLR camera for surface extraction

Fig .1. Machining surface of the Turning Work piece

Frequency extracted as two types 1. Major peak frequency – FR 1 2. Principal component magnitude squared – FR 2 The major peak frequency derived from the machining surface of the turning workpiece in Figure 1. Analyze and derived the data and various gray level for each surface images. Image texture was derived through calculation by using the FR 1 and FR 2 .Then predicting the average gray level for each surface texture by using mathematical calculation , Totally 40 training set of data feed as the input and output to the Artificial neural network.


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Table. 1. Training database

Frequency Speed (rpm)

Depth of Cut (mm/rev)

1

2000

2

Stylus Probe Surface roughness value (Ra)

Feed Rate (mm)

Gray Level (Ga)

F1

F2

0.5

0.1

125.236

121.56

32.65

0.869

2000

0.5

0.1

134.564

65.89

38.45

0.759

3

2000

0.5

0.1

164.466

64.89

39.46

1.236

4

2000

0.5

0.1

94.464

102.65

40.56

0.987

5

2000

0.5

0.1

123.445

73.58

41.16

0.852

6

2000

0.5

0.1

100.564

95.66

38.45

0.875

7

2000

0.5

0.1

105.464

90.65

37.49

0.863

8

2000

0.5

0.1

105.464

85.45

33.29

1.895

9

2000

0.5

0.1

104.44

100.85

35.08

1.987

10

2000

0.5

0.1

115.564

118.65

36.89

0.876

11

2000

0.5

0.1

112.754

95.87

41.89

0.985

12

2000

0.5

0.1

135.454

62.32

40.89

0.963

13

2000

0.5

0.1

118.645

74.23

40.89

0.987

14

2000

0.5

0.1

114.454

88.55

36.48

0.856

15

2000

0.5

0.1

115.845

75.44

34.89

0.845

16

2000

0.5

0.1

118.454

95.11

32.78

0.962

17

2000

0.5

0.1

117.842

75.49

36.48

1.358

18

2000

0.5

0.1

95.275

65.11

35.98

1.025

19

2000

0.5

0.1

98.844

88.44

40.48

1.269

20

2000

0.5

0.1

126.445

86.56

33.25

0.987

21

2000

0.5

0.1

99.425

64.89

34.89

0.852

22

2000

0.5

0.1

109.454

97.55

39.75

0.965

23

2000

0.5

0.1

104.421

67.49

37.29

1.265

24

2000

0.5

0.1

105.874

109.4

32.56

0.896

25

2000

0.5

0.1

125.942

100.48

35.69

0.965

26

2000

0.5

0.1

118.845

98.45

36.59

0.875

27

2000

0.5

0.1

108.854

83.15

42.56

0.856

28

2000

0.5

0.1

101.154

94.22

34.98

0.836

29

2000

0.5

0.1

100.541

97.46

37.59

1.165

30

2000

0.5

0.1

95.788

94.56

31.59

1.036

S.NO


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31

2000

0.5

0.1

121.45

90.65

38.69

1.115

32

2000

0.5

0.1

120.545

89.46

42.15

1.111

33

2000

0.5

0.1

114.856

80.46

35.25

0.987

34

2000

0.5

0.1

102.864

84.46

36.33

0.874

35

2000

0.5

0.1

107.858

73.6

37.98

0.875

36

2000

0.5

0.1

100.965

79.49

33.65

0.965

37

2000

0.5

0.1

98.121

74.46

37.45

0.987

38

2000

0.5

0.1

100.884

69.4

35.69

1.125

39

2000

0.5

0.1

135.484

67.49

41.22

0.965

40

2000

0.5

0.1

128.875

66.48

35.99

1.104

In the experiments, Single specimen was operated at the constant cutting parameter range. The above table showed the data for training the neural network. The architecture of the artificial neural network show in fig 2. Back propagation method used to train the neural network for prediction of surface roughness. Here that derived the number of input hidden layer and neurons count also. The neural networks model can modified based on the requirement and accuracy level. The sequence arrangement of the ANN structure was 6-10-1.

Fig . 2. Structure of ANN

Execution of training procedure, analyze the validation performance for each neuron by using the performance graph. Artificial neural network performance Graph has shown in Fig. 3. Real time surface roughness can perform by the artificial neural network


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Fig. 3. Performance of ANN (6-10-1)

3. Experimental verification Reviewing the neural network to identify the surface roughness of Turning at single specimens using constant cutting parameter has performed. Input of the ANN is the spindle speed, tool feed rate, depth of cut, major FR 1, FR 2 and the Gray level (Ga), then check the output roughness value based on the testing input parameter it was displayed in MAT LAB work space sheet. Below Table 2 show the results about the manual surface roughness and machine vision surface roughness method. Table . 2. Experimental Testing Results Frequency

Stylus Probe Surface roughness value (Ra)

Machine vision roughness value

S.NO

Speed (rpm)

Depth of Cut (mm/rev)

Feed Rate (mm)

Gray Level (Ga)

F1

F2

1

2000

0.5

0.1

114.854

80.46

34.88

1.103

1.084

2

2000

0.5

0.1

110.454

97.49

31.99

0.978

1.104

3

2000

0.5

0.1

104.754

108.49

33.45

1.045

1.104

4

2000

0.5

0.1

98.456

88.49

38.46

1.236

1.051

5

2000

0.5

0.1

97.205

90.49

41.33

0.963

1.022

6

2000

0.5

0.1

100.201

98.95

40.88

0.875

1.064

7

2000

0.5

0.1

104.568

90.46

42.69

0.852

1.019

8

2000

0.5

0.1

107.894

97.46

32.59

0.963

1.114

9

2000

0.5

0.1

132.845

99.56

33.45

1.364

1.022

10

2000

0.5

0.1

128.892

106.49

36.38

1.045

1.033

Formula for calculate the percentage of error: % of Error ₌ (Predicted value - Experimental) / Experimental value


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Fig 4. Comparison between stylus probe and machine vision roughness value

The machine vision surface roughness value evaluated by manual stylus probe surface roughness value for remaining sets. Comparison result between manual and machine vision shown in fig 4.based on the comparison value to predict the percentage of error and accuracy of the vision system. The predicted roughness values through machine vision result validated by the ten sets of testing data from the experimental value of the surface roughness in end turning in Fig. 4. The result show the average error of the prediction of surface roughness in turning using ANN is 2.46%, i.e., the accuracy is 97.54%.

4. Conclusion This proposed method not only for surface roughness prediction, it also used to the welding and damage inspection also. When increase the feature extraction and number of testing samples as used to improve the accuracy of the machine vision system. In this paper by using machine vision system achieved average error percentage was 2.46 % and 97.54% accuracy. Apart from the machining process the machine vision can utilize in assembly and medical care and automotive applications REFERENCES 1.

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