Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-6, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
Surface Roughness Evaluation of Turned
Surfaces Using Wavelet Packet Transform
Srivatsa T 1, Ravi Keerthi C 2, Dr. Srinivas H K3 , Ravi Kumbar4 &
T. Madhusudhan5
1
M.Tech Student, Department of Mechanical Engineering, SJBIT, Bengaluru, Karnataka,
India.
2
Asst. Prof, Department of Mechanical Engineering, GAT, Bengaluru, Karnataka, India.
3,5
Prof, Department of Mechanical Engineering, SJBIT, Bengaluru, Karnataka, India.
4
Asst. Prof, Department of Mechanical Engineering, SJBIT, Bengaluru, Karnataka, India.
Abstract: The surface roughness is a measure of the
deformation of a surface resulting from various
machining process. The measurement of surface
roughness is categorized in two ways, the contact
method and the non-contact method. The first method
involves the direct contact of the measuring
instrument with the specimen being measured. The
latter method avoids contact with the specimen being
measured such as vision system. In this paper using
the non-contact method the prediction of surface
roughness values of turned surfaces are explained
with the application of wavelet packet transform and
image processing. The paper also shows the
comparison of the measured value and the predicted
value.
Keywords: Wavelet Packet Transform (WPT), denoising, feature extraction, mother wavelet.
1. Introduction
Image processing is a method which gives detail
information of a digital image and is used to enhance
the quality. The operations used in the image
processing include de-noising and filter of images
etc. The objective is to remove the disruptions from
the image which improves the quality of the picture.
An image is a representation of pixels in a matrix
format. The images are of two types namely the RGB
image format and the gray scale image format. The
RGB image format consist of three channels of red,
green and blue whereas the gray scale image format
contains one channel Image de-noising is used to
obtain an image with better clarity and the objective
of removing the noise in an image is to obtain
important details and retaining the main
characteristics with complete removal from an
image. Most of the information are obtained from an
image by applying mathematical transformations.
Out of such transformations the wavelet packet
transform are selected. The wavelets being
mathematical functions are used to study data in
terms of resolution or scale. The selection of levels
Imperial Journal of Interdisciplinary Research (IJIR)
and the selection of mother wavelet for de-noising
and decomposing an image depend on the values of
peak signal to noise ratio in wavelet packet transform
In image processing the peak signal to noise ratio
value of an image is in the range of 30-50db for an 8
bit data. Higher the peak signal to noise ratio, better
is the quality of the image.
The acquisition of an image for the feature
extraction process is an important factor to be
considered. The conditions in using the vision system
such as camera settings and lighting arrangements
are important to obtain better results. [6]
The use of artificial neural network has been used
in image processing that can be applied to different
fields. The artificial neural network has provided
solutions in pattern related problems. The artificial
neural networks are models consisting of neural
networks used for evaluating the objectives which
are dependent on more number of inputs.
2. Literature review
The method of confining a coloured image using
the wavelet packet with best tree selection was
studied by Dr. G. K. Kharate. In this study a
method was proposed using the wavelet packet
technique with the threshold entropy. The
comparison of the image obtained from the wavelet
technique with the JPEG 2000 file format was
carried out. [1]
The regeneration of an image obtained between
wavelet transform and wavelet packet transform was
studied by Sanjeev Chopra. In this study the
different wavelets were chosen to confine the image.
The process of filtering for each wavelet was carried
out. Among the criteria’s the mean square error and
peak signal to noise ratio were important. The image
regeneration using the two criteria’s for both the
wavelet technique was studied. [2]
Anna Zawada-Tomkiewicz proposed the method
of determining the surface roughness using the vision
system with the wavelet packet transform and neural
network with a surface roughness estimator. The
Page 978
Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-6, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
outcome of the estimator proved to be efficient to
indicate the roughness quality for turned operations.
[3]
Paul Dan Cristea described the application of
artificial neural network in image processing and
concluded that the artificial neural network provide
faster and good results. [4]
Anna Zawada-Tomkiewicz proposed the
correlation of energy vector entropy of
decomposition tree coefficients and machined
surface criteria and tool wear using wavelet packet
transform. [5]
3. Wavelet packet transform
In wavelet packet transform, the image is
decomposed in to low frequency and high frequency
parts. The image is decomposed in to trees known as
packets. In each packet there is important
information that can be extracted. The wavelet
packet transform has an advantage over the wavelet
transform. The decomposition of image in to levels
give energy values based on the selection of level. In
wavelet packet transform, each packet gives
information on energy on the image. Also better
information of edges or textures of the image can be
achieved, hence a better detailed image can be
obtained. Figure 1 below shows the decomposition
structure of the trees at level 3. It is used for
approximations images and the detail images. The
energy of the packets will produce a better
reconstructed image.
corresponding level and the mother wavelet in the
de-noised with the corresponding level are used for
feature extraction.
4. Methodology
In this paper the surface texture of a mild steel
specimen is studied. Turning operations were carried
out on the specimen on a HMT lathe machine with
carbide tip tool. The parameters such as speed, feed
and depth of cut were varied for each operation. The
image of the surface texture of the specimen was
captured and used for image processing. The vision
system was used to capture the images. The vision
system consists of a Digital camera, tripod stand,
working table, lighting arrangements and a V-block
as shown in figure 3and figure 4. The specimen
dimensions of 40mm diameter and 95mm length.
Nikon Digital SLR Camera D5500 and Simpex 888
tripod stand were used. The image acquisition of the
images was carried out in a dark room
simultaneously after each operation.
Once the images were acquired, the images are
cropped to a size of 400x200 as shown in figure 5
using Picasa software. These images are then fed to
the decomposition procedure in which the selection
of mother wavelet and levels of decomposition are
carried out for both wavelet transform and wavelet
packet transform.
Fig 2. HMT Lathe Machine
Fig 3. Vision system
Fig 4. Specimen on V-Block
Fig 5. Surface texture
Fig1.Decomposition of wavelet packet transform with
level 3
3.1 Selection of mother wavelet:
The procedure is to perform the decomposition
process to determine the levels of decomposition and
selecting the mother wavelet for the images. Eight
mother wavelets have been used. Among the selected
mother wavelets, the mother wavelet with more peak
signal to noise ratio with respect to the level are
chosen. Four levels of decomposition were used. The
peak signal to noise ratio indicates the condition of
the original information present in the image during
the reconstruction process. The MATLAB software
2013Ra is used to carry out the procedure.
Based on the decomposition criteria, the mother
wavelet selection de-noising with level 1 in terms of
the peak signal to noise ratio is calculated. The
mother wavelet in the decomposition with the
Imperial Journal of Interdisciplinary Research (IJIR)
Haar wavelet was chosen as the mother wavelet
and the level of decomposition was fixed to level
1.The de-noised procedure was carried out using the
wavelet bior2.4 with level 1. The first step involved
was converting the RGB image in to greyscale
image. The grey scale image was then de-noised by
using the Gaussian noise.
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Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-6, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
Fig 6. Original image
Fig 7. Greyscale image
Mother Wavelet
Level 1
sym1
23.7235
haar
23.7235
bior2.4
23.7375
bio6.8
23.7307
bior2.2
23.7374
db4
23.728
db15
23.7194
db20
23.7194
Table 2. De-noised level peak signal to noise ratio
Fig 8. Noisy image
Fig 9. De-noised image
The above figures show the grey scale image, noisy
and de-noised image from the original image of
250rpm, 0.06mm/rev and 0.5mm depth of cut. In
figure 10 the decomposition level of the original
image is shown in terms of peak signal to noise ratio
for every level. The haar wavelet and level 1 were
selected based on the peak signal to noise ratio value.
Mother
Level 1
Level 2
Level 3
Level 4
haar
37.8336
37.2596
37.1379
37.3253
sym1
37.8336
37.2596
37.1379
37.3253
bior2.4
35.8937
35.0785
34.7666
34.2663
bior 2.2
35.6542
34.7821
34.3845
33.9181
bior 6.8
37.0715
36.5153
36.4095
36.2595
db4
37.5506
36.8316
36.6299
36.5257
db15
37.4314
36.7219
36.5889
36.4849
db20
37.4076
36.7291
36.501
36.4359
wavelet
Table 1. Decomposition level peak signal to noise ratio
In table 2 the den-noised table for level 1 with values
of peak signal to noise ratio are shown, the wavelet
used is bior2.4 was used.
Fig 10. Decomposition level 1
In figure 10 the decomposition for level 1 are shown,
which shows the tree decomposition. From the tree
shown below, the original image is split in to
approximation and detail images. The reconstruction
of images is based on the energy retention values of
the image. In table 3, the values in each tree is shown
with b1, b2, b3 and b4. b1 represents the
approximation image and b2, b3 and b4 are the
reconstructed images of the original image as shown
in figure 11. b3 is used to obtain the important
parameters as the energy value is more compared to
b2 and b3. The parameters such as kurtosis, mean,
variance, standard deviation and skewness are
obtained.
b1
98.0566
b2
0.4883
b3
1.1537
b4
0.3015
Table 3. Energy retention values
Imperial Journal of Interdisciplinary Research (IJIR)
Page 980
Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-6, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
Fig 11. Reconstructed images
5. Artificial neural network
The artificial neural network is a processing unit that
is used to train and retain knowledge from the inputs
and provide an efficient outcome. In this paper a
multilayer perceptron model is used to predict the
surface roughness values. The surface roughness
values are experimentally found out using a stylus
instrument called Mitutoyo surface roughness
measuring tester SJ-210. The multilayer perceptron
network consists of hidden layers of neurons in
which the inputs are passed from one layer to another
layer. The addition of one or more hidden layer
results in better results. In this model the conditions
such as the iterations and the number of neurons and
the selection of algorithm to execute are important
factors.
In this work 85% of images are used for training and
15% are used for testing. The model will predict the
surface roughness values in MATLAB 2013Ra
software. In figure 12 shows the model used for
prediction of surface roughness value.
Fig 12. Multilayer perceptron model
6. EXPERIMENTAL RESULTS
The results have been plotted with predicted versus
experimental values of surface roughness Ra and
necessary correlation has been obtained using the
least square method
Imperial Journal of Interdisciplinary Research (IJIR)
Fig 13. Correlation between experimental values of Ra and
predicted values of Ra for training data.
Fig 14. Correlation between experimental values of Ra and
predicted values of Ra for training data.
Fig 13 and Fig 14 presents the overall correlation
results for surface roughness prediction using WPT
based feature extraction for surface images using
ANN for Turned surfaces for both training and test
data respectively.
Sl. No.
1
2
3
4
5
6
7
8
9
10
11
Predicted Ra
3.461434
3.348804
2.707005
2.575648
2.581301
1.956348
1.988463
1.949999
1.614096
1.571566
1.583535
Experimental Ra
3.464
3.351
2.697
2.554
2.568
1.917
1.951
1.91
1.553
1.507
1.52
Table 4. Values of experimental and predicted surface
roughness for turned surfaces
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Imperial Journal of Interdisciplinary Research (IJIR)
Vol-2, Issue-6, 2016
ISSN: 2454-1362, http://www.onlinejournal.in
7. Conclusion
In this work the surface roughness parameter R a has
been predicted for turned surfaces using features
extracted from surface images. WPT has been used
to extract the features. ANN has been used to
correlate these statistical features with the surface
roughness parameter Ra measured using conventional
stylus based measurement. The developed ANN
model has been able to predict surface roughness
effectively. The use of vision system for surface
roughness evaluation is effective for turned surfaces.
Vol. 38, Nos 5-6. Page 685-690. 1998 i 1998
Elsevier Science Ltd.
[8] D.E.P. Hoy and F. Yu, “Surface Quality
Assessment Using Computer Vision Methods”,
Journal of Materials Processing Technology, 28
(1991) page 265-274 Elsevier.
.
8. Future work
Comparison of WPT results can be made with WT
techniques. Effective de-noising techniques with
thresholding can be used in order to obtain improved
results.
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for Image Compression at Decomposition Level 2”,
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Network Technology IACSIT Press, Singapore
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[3] Anna Zawada – Tomkiewicz “Estimation of
surface roughness parameter based on machined
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[4] Paul Dan Cristea, “Application of Neural
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[5] Anna Zawada – Tomkiewicz, “Machined surface
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[6] Omar Monir Koura, “Applicability of image
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Imperial Journal of Interdisciplinary Research (IJIR)
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