Prediction of Surface Roughness in End Milling Process by Machine Vision Using Neuro Fuzzy Network Palani S
Kesavanarayana Y
Department of Mechanical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Avadi, Chenna-62, India
[email protected]
Department of Mechanical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Avadi, Chenna-62, India
[email protected]
Abstract—The roughness of the machined surface is a main concern because product fitness depends on surface roughness. The monitoring of roughness on the workpiece in end milling process by applying machine vision method is presented in this research work. The captured machined image of the work piece is extorted by image processing method. A neuro fuzzy model is used to relate the actual and predicted roughness at various cutting parameters in end milling operation. Measurement of milled surface is monitored with less error when the extracted milled image and milling parameters are fed into the model. The values of the surface roughness predicted by neuro fuzzy model are then verified with experiments and are compared. The prediction accuracy motivating that computer vision technique could be used to various on-line automated manufacturing sectors. Keywords—Milling operation; Computer vision; Non-contact inspection; Surface roughness; Neural fuzzy Network
I. INTRODUCTION In the production sector, milling is one of the important and frequently handling for metal removing purpose. Now a day's quality of the parts is important concern in the competitive world and on-line monitoring of rough of the work piece is essential[1]. Roughness of the work piece can be measured by contact and non-contact techniques. In contact technique the probe of the measuring tool contact directly on the machined surface caused scratches form on the work piece. The on-line measurement method for measuring machined surface roughness in milling was introduced [2]. In the automated production environment, computer vision technique a vital role for prediction of machining processes in earlier [3]. The continuous investigation was performed on computer vision techniques in industries, The measurement of roughness on machined work piece in line process could be performed without touching or scratching and also quicker than the traditional measurement [4]. In the present years, computer vision monitoring method overcomes the limitation of traditional probe instrument technique for on-line prediction of machined surface roughness of the work piece. Galante et. al. proposed machine vision application in turning process for prediction of
roughness in machine surface[5]. P.G.Benardos applied a Taquchi's design of experiments for prediction of surface roughness in CNC face milling process.[6]. B.Y.Lee built a method for monitoring roughness of the machines surface roughness in turned parts [7]. In this paper, neuro fuzzy logic is applied to correlate the actual and predicted roughness of the work piece in different machining operation. Finally, neuro fuzzy model has been constructed, for on-line monitoring of roughness of the work piece with the integration of machine vision technique. In this research manuscript, initially expose the extraction of image of the work piece and followed the methodology of the neuro fuzzy network is explained. Then depict the evidence of the neuro fuzzy network correlation to the actual and predicted roughness of the work piece in various machining operation. At the end of the manuscript cover the result and discussion of the work followed by a possible practical approach in future to provide a new dimension to optimize time consumed while manufacturing and milling. II. CALCULATION OF ROUGHNESS OF CAPTURED IMAGE ON THE WORKPIECE At 1960's imaging technique was introduced for space investigation to generate map of the earth. Then the development of computer techniques participates in the manufacturing and industrial sector through machine vision technique for on-line monitoring [8]. In literature manuscript [9], Kurada et. al. describe the use of machine vision method for on-line measurement of tool. As Mentioned by Bradly [10], On-line measurement of machined work piece average roughness by relating imaging technique is a classical method. In this work, machine vision technique is used to predict machined surface roughness on the work piece in milling operation as depicted in figure 1. Initially lighting sources are fall on the work piece then surface of the image is captured by machine vision system and sent to personal computer work station via frame grabber.
Surf coder is used to measure the roughness of the machined surface of the work piece at 8 mm length with speed 0.5 mm/s. The arithmetic mean of the surface roughness (Ra) is calculated by the following formulae:
Here, Yi = roughness height from mean n = number of experimental data Ga = mean gray value of machined surface image of the work piece.
Here, Gi = mean gray value of the machined surface image. III. MODELING OF SURFACE ROUGHNESS BASED ON THE NEURO FUZZY LOGIC When the model developed with complex parameters, the problem would be arising to generate fuzzy rules and function of membership. The neuro fuzzy model overcome these difficulties by deals explicit background of fuzzy and deals implicit learning capability of neural networks [11] The neural network provides experts neurons for generating fuzzy control if-then rules with membership to obtain correct formulations [12]. It takes advantage to minimize the computation time and manufacturing prize.
Figure 1: Rapid – I Model of Computer vision A. Specifications Magnification Range: 11X to 67X Imaging : up to 1600 x 1200 pixels Lighting System: 5 Zones with LED Built in Rapid I software
Figure 3. Neuro Fuzzy model structure
V= 28 m/min F= 0.002 mm/rev D= 0.6 mm R= 0.6371 µm G = 109.563
V= 28 m/min F= 0.0466 mm/rev. D= 0.8 mm R= 0.8517 µm G = 153.845
Figure 2: Captured Images of the work piece The arithmetic average of the gray level Ga can be expressed as:
The benefits of both conventionally fuzzy expert and neural networks to combined the computer practice for obtaining computational performance through the layer in hidden neurons and learning capacity respectively. IV. EXPERIMENTATION The experimental view of the CNC milling operation is shown in figure 4.
TABLE I. EXPERIMENTAL DATA OF MILLING OPERATION Sl. No
V m/min
F mm/rev
D mm
Ga
Ra microns
1
28
0.002
0.6
0.6607
0.6371
2
28
0.002
0.8
0.8498
0.5705
3
28
0.002
1.0
0.7506
0.5039
4
28
0.0333
0.6
0.8420
0.777
5
28
0.0333
0.8
0.6621
0.67
6
28
0.0333
1.0
0.6679
0.644
7
28
0.0466
0.6
1.0000
0.9183
8
28
0.0466
0.8
0.9278
0.8517
9
28
0.0466
1.0
0.7445
0.687
10
38
0.002
0.6
0.8651
0.37
11
38
0.002
0.8
0.7073
0.3025
A. Specifications of the Milling Machine Working table surface: 425 x 150 mm Longitudinal travel : 200 mm Cross travel : 120 mm Head travel : 115 mm Spindle speed programmable: 0 – 4000 rpm Power supply: 220 V / 50 cycles M.1 PH Spindle motor :variable speed 0.370 kW PMDC motor Machine weight : 170 kgf
12
38
0.002
1.0
0.8339
0.2359
13
38
0.0333
0.6
0.7592
0.5097
14
38
0.0333
0.8
0.9069
0.4431
15
38
0.0333
1.0
0.8803
0.3765
16
38
0.0466
0.6
0.6744
0.6503
17
38
0.0466
0.8
0.8694
0.5837
18
38
0.0466
1.0
0.8743
0.587
B. Cutting Tool Specifications Type : End mill cutter Diameter : 6 mm
19
47
0.002
0.6
0.8097
0.0827
20
47
0.002
0.8
0.1574
0.0469
21
47
0.002
1.0
0.1460
0.0321
22
47
0.0333
0.6
0 .8543
0.2417
23
47
0.0333
0.8
0.8342
0.236
24
47
0.0333
1.0
0.4935
0.01321
25
47
0.0466
0.6
0.8938
0.3823
26
47
0.0466
0.8
0.7381
0.3157
27
47
0.0466
1.0
0.7754
0.286
Figure 4: CNC milling operation
C. Work Materials Specification The work material used in the milling is aluminium alloy. D. Input Variables and Conditions Cutting speed: 28, 38, 47 m/min Feed : 0.002, 0.0333, 0.0466 mm/rev Depth of cut: 0.6, 0.8, 1.0 mm Gray Intensity levels of machined surface E. Output Characteristics Surface roughness in microns F. Experimental Results Twenty seven milling experiments from various machining conditions are taken for the purpose of training the neuro-fuzzy to monitor the roughness of the machined surface. The experimental values were tabulated in Table 1.
V. SURFACE ROUGHNESS PREDICTION A. Normalisation of input variables The normalization is the method to obtain the experimental parameters from -1 to +1. The essential of the normalization is to train the parameter eventually and meet the equal interval of training parameters for improving the performance of neuro fuzzy technique and minimize the computation time.
Figure 5. Training of Neuro Fuzzy model To train the neuro fuzzy network a suitable network topology is selected based on the trial and error approach. After the different topologies are tried the 4- 4- 1 network is selected to train the neuro fuzzy network. The trained network creation and network is shown in the figure 5. Finally the trained network is simulated with new testing inputs for the predictions of surface roughness. B. Input Testing Data and Validation (test for the Developed Model) Excluding 5 sets of data from 27 sets in the milling experiments were utilized for validation the model. The experimental values of input and output variables were shown in the table 2. The comparison of the predicted values and experimental data of the surface roughness for the same of testing cases in the milling are given. The % Error between the Predicted value and Experimental value are calculated by the given formula.
Figure 6. Validation test output of Neuro Fuzzy Finally the trained network is simulated with new testing inputs for the predictions of surface roughness are shown in figure 6. VI. RESULT AND DISCUSSIONS The models were developed using the Neuro Fuzzy. An experiment containing 27 different cutting conditions was shown in the table 1; the prediction of surface roughness could be performed to the milling operations.
Predicted Value -Experimental value % of Error = ------------------------------------------ *100 Experimental value TABLE 2 : COMPARISON OF MEASURED AND PREDICTED SURFACE FINISH USING NEURO FUZZY MODEL
Figure 7.Comparison of the Experimental and predicted values
V m/m in
F mm/ rev
D mm
1
1
0.0429
0.6
2
1
0.0429
1.0
Sl. No
3
1
0.7146
1.0
4
1
0.0429
0.6
5
1
0.7146
0.6
Ga
Ra in microns
Error %
Experi
Predic.
0.809 7
0.0511
0.0493
3.523
0.146
0.0349
0.0311
10.888
0.0132
0.0130
1.665
0.0901
0.0832
7.658
0.2632
0.2599
1.253
0.493 5 0.809 7 0.854 4
Average Absolute Error = 4.997 %
The average absolute percentage error for this model developed by Neuro Fuzzy is found to be 4.997%. i.e. the accuracy is 95.003%. VII. CONCLUSION This investigation has been proposed an on-line measuring system through computer vision technique to classify milled surface roughness of the work piece by neurofuzzy network.
Although the performance of the experiments is based on standard comparison specimens, the neuro Fuzzy technique could be directly implemented for automated industrial applications if the sample of normal machined parts can be obtained in advance for training. The surface roughness values predicted by the Neuro Fuzzy model are matching well with the measured experimental values. The primary advantage is to provide a new dimension towards online machining. With the help of image processing system and knowledge of various parameters, milling operation can be controlled and accurate surface roughness can be obtained to the required units. The results of the experiment proved that the neuro Fuzzy model closely predicted the input machining parameters and output response parameter. Outputs of the real experiment have been compared with the results of the proposed system which shows that. can produce accurate results. REFERENCES [1] G.A. A1. Kindi, R.M., K.F. Gill, “An Application of machine vision in the automated inspection of engineering surfaces” International Journal of Production Research Vol.30, No.2,pp,241-253,1992. [2] M.B. Kiran, B, Ramamoorthy, B.Radhakrishnan, “ Evaluation of surface roughness by vision system “, International Journal roughness by vision system”, International Journal of machine Tools & Manufacture Vol.38, No 56, pp 685-690, 1998 [3] M.Gupta, S. Raman, “Machine vision assisted characterization of machined surfaces”, International Journal of Production Research Vol.39,No pp.759-784,2001. [4] K. Venkata Ramana, B. Ramamoorthy, “Statistical methods of compare the texture features of machine surfaces”, Pattern Recognition. Vol29, No9 pp, 1447-1459, 1996 [5] Galant G, piacentini M. Ruisi VF. Surface roughness detection by tool image processing Wear 1991;148:211-20. [6] P.G.Benardos, G.C. Vosniakos, Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments, Robotics and Computer Integrated manufacturing 18 (5-6) (2002) 343-354 [7] B.Y.Lee, S.F.Yu, H.Juan “The model of surface roughness inspection by machine vision system in turning, Mechatronics 14 (2004) 129-141. [8] Dudas I and Varga G 2001 Metrological use of CCD Camera microCAD 2001 Int. Scientific Conference March 1-2 Univ. of Miskole, Hungary 35-40 [9] Kurada S Bradley C (1997) A review of machine vision sensors for tool condition monitoring, Comput Ind 34(1)55-72 [10] Bradly C Wong YS (2001) surface tecture indicators of tool wear – a machine vision approach. Int. J Adv Manuf Technol 17(6):435-443 [11] Stamotios V. Kartalopoulos, (1996) “Understanding Network and Fuzzy Logic” Prentice-Hall of India Ltd, New Delhi [12] Jang, J.S. Sun, C.T. Mezuzah,E. (1997) “Neuro Fuzzy Logic and Soft Computing”. Prentice-Hall Englewood cliffs
