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CHAPTER 1
INTRODUCTION
1.1. AIM OF THE PROJECT Online monitoring of grinding wheel loading and grinding wheel dressing To determine the percentage of loading at certain intervals of time To determine the optimum interval of grinding wheel dressing time based on
wheel loading and application.
1.2. NEED FOR THE PROJECTGrinding is an abrasive machiningprocess that uses a grinding wheel as the cutting
tool. It can produce very fine finishes and very accurate dimensions. Grinding is one of the
final machining processes that determine the surface quality of machined products. After a
long period cycles times of the grinding process, removed chips may stick in the space
between abrasive grains or weld on the top of cutting edges. Factors such as wheel loading and
wheel wear contribute to the deterioration of the working surface and also its cutting capacity.
When the loading and wear is severe, dressing of grinding wheel has to be carried out in order
to bring the wheel to its best state. Determining the timing to dress the grinding wheel is
extremely important in order to prevent flaws in products. Therefore monitoring the grinding
process for wheel loading and wheel dressing is critical to ensure the surface quality of the
machined component as well as the efficiency of the grinding process.
1.3. SCOPE OF THE PROJECTTo overcome the difficulties of experience-dependent dressing, a systematic dressing
method which can measure the status of the working wheel surface and evaluate the dressed
wheel surface is necessary. Much research concerning the monitoring of grinding process has
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been conducted using AE, a touch-trigger probe inspection method , an optical triangulation
method, Eddy currents, laser methods and ultrasound techniques.
Advances in the computer vision technology have led to the investigation of its
application in the monitoring of the grinding process. Visual information has the advantage,
that can be interpreted very easily and due to its high information content is the first choice to
investigate typical surface forms, which cannot be extracted from indirect measurement
signals.
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CHAPTER 2
LITERATURE SURVEY
Literature forms the basic backbone of all the work. Surveying the literature helps to
gain knowledge about the work done previously and steps to be taken to move forward in
particular fields of interest.
Stephane LaChance, Andrew Warkentin, and Robert Bauer (2003) measure the
wear flats by analyzing digital images. This system is mounted on the grinding machine and
automates wear flat measurement by using computer control to automatically position the
wheel and capture digital images of the wheel between grinding cycles. Image processing
software is used to automatically analyze the digital images and measure wear flat area in the
images. The proposed measurement system was validated using a scanning electron
microscope. Experiments were performed on a Brown & Sharpe Micromaster 824 surface
grinder to examine the relationship between wear flat area and normal force. The results agree
with the literature.
Wen-Tung Chang, Ting-Hsuan Chen and Yeong-Shin Tarng (2011) measures the
characteristic parameters of form grinding wheels used for microdrill fluting. With the aid of
the indirect duplication of wheel contours and by using computer vision, this paper presents a
systematic process for the wheel contour measurement. The measuring process includes five
sequential steps: the edge detection, the straight line detection, the contour separation, the
circular arc fitting, and the circular arc angle evaluation. To test the proposed measuring
process, a measuring apparatus was built, and experiments measuring the characteristic
parameters of diamond grinding wheels used for microdrill fluting were conducted. It showed
that the proposed measuring process was feasible to measure the characteristic parameters of
certain form grinding wheels used for microdrill fluting.
J. C. Su and Y. S. Tarng (2006) measures the grinding wheel wear using machine
vision system. The vision-aided measuring system comprises a CCD coupled with a
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telecentric lens, back lighting board and frame grabber. Measuring the image of the specimen
with a grinded gap substitute directly captures the image of the actual grinding wheel. Using
this method makes the 3 D of the topography of the grinding wheel into the 2 D of the contour
of the grinding wheel. The results show that this developed system achieves a repeatable
accuracy of 3 m for the measurement of the grinding wheel contours.
Z. Feng and X. Chen (2006) detects and identifies the chip loading and cutting edge
wear of a grinding wheel using the image processing toolbox of MATLAB. The different
optical characters of the metal chips and the abrasive grains are analysed. The Sobel operator
is adopted to make edge detection. A sensitivity threshold based on the global condition is
used to decrease the noise. Image dilation and erosion processes are used to ensure the edge of
each loaded chip is covered by a continuous section. The ratios of chips are calculated and
displayed to monitor the wheel surface working status.
K.C. Fan, M.-Z. Lee and J.I. Mou (2002) proposes an on-line non-contact method
for measuring the wear of a form grinding wheel. A CCD (charge coupled device) camera
with a selected optical lens and a frame grabber was used to capture the image of a grinding
wheel. The analogue signals of the image were transformed into corresponding digital grey
level values. Using the binarisation technique, the images of background and the grinding
wheel were segmented. Thus the grinding wheel edge was identified. The mapping function
method is used to transform an image pixel coordinate to a space coordinate. An auto -focus
technology is also developed. The statistics of pixels are used as the focusing index. The
signal was sent through an 8255 control card to drive a d.c. motor, and then to control the lens
focusing movement to acquire the focal plane. The images before and after the grinding
process were captured. The position deviation of the grinding wheel edge was analysed. Then,
the grinding wheel wear was evaluated.
Bernard C. Jiang, Chung-Li and Tsung-Chi Chen (2001) proposed a machine
vision system to determine the protrusion rate of a diamond tool. The method developed is a
noncontact method without manual judgment. Three sets of field samples were used to
demonstrate the proposed method for determining protrusion rate.
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C.K. Huang, Y.S.Tarng, C.Y. Chiu and A.P. Huang (2009) proposes using machine
vision technique to assist the double-wheel grinding mechanism of moving-drill and fixed
wheel to achieve resharpening function. First find and adjust the posture of micro-drill from
vision system for grinding, then detect the line equation of cutting edge of primary facet
grinded micro-drill and rotate cutting edge to be horizontal; and then grind the secondary facet
of micro-drill. All grinding parameters for resharpening process are found automatically with
machine vision technology.
T. Warren Liao, Chi-Fen Ting, J. Qu and P.J. Blau (2006) presents a wavelet-based
methodology for grinding wheel condition monitoring based on acoustic emission (AE)
signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens
with a resinoid-bonded diamond wheel using two different conditions. During the
experiments, AE signals were collected when the wheel was sharp and when the wheel was
dull. Discriminant features were then extracted from each raw AE signal segment using the
discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was
finally applied to the extracted features in order to distinguish different states of grinding
wheel condition. The test results indicate that the proposed methodology can achieve 97%
clustering accuracy for the high material removal rate condition, 86.7% for the low material
removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet,
the decomposition level, and the GA parameters are properly selected.
Amin A. Mokbel and T.M.A. Maksoud (2000) uses an imprint of the profile of the
grinding wheel was used to measure the surface condition of the wheel. An acoustic emission
sensor with a high frequency sampling of 1.25 MHz was attached to the mild steel specimens
to monitor the wheel condition. The raw AE signals generated from the grinding
wheel/specimen contact were then analysed using a fast Fourier transform. The AE spectral
amplitude of different grinding wheel bond types, grit sizes and their conditions represented
by grinding wheel/truing speed ratios were then compared with the surface roughness (Ra) of
the ground mild steel specimens.
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SunHo Kim and Jung Hwan Ahn (1999) describes a systematic approach to deciding
a proper dressing interval and an optimal dressing depth for the working grinding wheel. An
eddy current sensor and a laser displacement sensor are used to measure the loading on the
working wheel surface and the topography of the dressed wheel surface respectively. The
dressing interval can be decided properly through the relational locus between the level of
loading and the machined surface roughness. An optimal dressing depth to ensure less wheel
loss and greater wheel surface quality is decided through the analysis of the variance of
topography for the dressed wheel surface, which decreases at three different rates according to
the accumulated dressing depth.
Pawel Lezanski (2001) uses the neural network and fuzzy logic to classify the
condition of the grinding wheel cutting abilities for the external cylindrical grinding process.
For each measuring signal a few statistical and spectral features are calculated and used as an
input for data selection andclassification procedures. First, a feed forward back propagation
neural network was implemented to perform feature selection task from the multiple sensor
system. Next, a neural network based fuzzy logic decision system for sensor integration in
grinding wheel condition monitoring is discussed.
2.1 SUMMARY
Much research concerning the monitoring of grinding process has been concentrated
on wheel wear only. As wheel loading can equally contribute to the quality as well as
productivity of the grinding process, monitoring of wheel loading and wheel dressing is also
important. This work concentrates on monitoring the wheel loading and relating it to surface
finish, thereby determining the optimum wheel dressing time.
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CHAPTER 3
GRINDING PROCESS
(Courtesy:http://nptel.iitm.ac.in/courses/Webcoursecontents/IIT%20Kharagpur/
Manuf%20Proc%20II/pdf/LM-27.pdf, LM-28.pdf, LM-29.pdf)
3.1 GRINDING
Grinding is the most common form of abrasive machining. It is a material
cutting process which engages an abrasive tool whose cutting elements are grains of
abrasive material known as grit. These grits are characterized by sharp cutting points,
high hot hardness, and chemical stability and wear resistance. The grits are held
together by a suitable bonding material to give shape of an abrasive tool. Figure 1
illustrates the cutting action of abrasive grits of disc type grinding wheel similar to
cutting action of teeth of the cutter in slab milling.
Figure 3.1. Cutting action of abrasive grains
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3.2 GRINDING WHEEL
Grinding wheel consists of hard abrasive grains called grits, which perform the cuttingor material removal, held in the weak bonding matrix. A grinding wheel commonly identified
by the type of the abrasive material used. The conventional wheels include aluminum oxide
and silicon carbide wheels while diamond and CBN (cubic boron nitride) wheels fall in the
category of super abrasive wheel.
3.2.1 SELECTION OF GRINDING WHEELS
Selection of grinding wheel means selection of composition of the grinding wheel and
this depends upon the following factors:
1) Physical and chemical characteristics of the work material
2) Grinding conditions
3) Type of grinding (stock removal grinding or form finish grinding)
3.3 TYPES OF ABRASIVES
Aluminum oxide
Aluminum oxide may have variation in properties arising out of differences in
chemical composition and structure associated with the manufacturing process. Pure Al2O3
grit with defect structure like voids leads to unusually sharp free cutting action with low
strength and is advantageous in fine tool grinding operation, and heat sensitive operations on
hard, ferrous materials. Regular or brown aluminum oxide (doped with TiO 2) possesses lower
hardness and higher toughness than the white Al2O3 and is recommended heavy duty grinding
to semi finishing.Al2O3 alloyed with chromium oxide (
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Microcrystalline sintered Al2O3 grit is the latest development particularly known for its
toughness and self sharpening characteristics.
Silicon carbide
Silicon carbide is harder than alumina but less tough. Silicon carbide is also inferior to
Al2O 3 because of its chemical reactivity with iron and steel. Black carbide containing at least
95% SiC is less hard but tougher than green SiC and is efficient for grinding soft nonferrous
materials. Green silicon carbide contains at least 97% SiC. It is harder than black variety and
is used for grinding cemented carbide.
Diamond
Diamond grit is best suited for grinding cemented carbides, glass, sapphire, stone,
granite, marble, concrete, oxide, non-oxide ceramic, fibre reinforced plastics, ferrite, graphite.
Natural diamond grit is characterized by its random shape, very sharp cutting edge and free
cutting action and is exclusively used in metallic, electroplated and brazed
bond.Monocrystalline diamond grits are known for their strength and designed for particularly
demanding application. These are also used in metallic, galvanic and brazed bond.
Polycrystalline diamond grits are more friable than monocrystalline one and found to be most
suitable for grinding of cemented carbide with low pressure. These grits are used in resin
bond.
CBN (cubic boron nitride)
Diamond though hardest is not suitable for grinding ferrous materials because of its
reactivity. In contrast, CBN the second hardest material, because of its chemical stability is the
abrasive material of choice for efficient grinding of HSS, alloy steels, HSTR alloys. Presently
CBN grits are available as monocrystalline type with medium strength and blocky
monocrystals with much higher strength. Medium strength crystals are more friable and used
in resin bond for those applications where grinding force is not so high. High strength crystals
are used with vitrified, electroplated or brazed bond where large grinding force is expected.
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Microcrystalline CBN is known for its highest toughness and auto sharpening character and
found to be best candidate for HEDG and abrasive milling. It can be used in all types of bond.
Grit size
The grain size affects material removal rate and the surface quality of work piece in
grinding. Large grit- big grinding capacity, rough work piece surface Fine grit- small grinding
capacity, smooth work piece surface.
Grade
The worn out grit must pull out from the bond and make room for fresh sharp grit inorder to avoid excessive rise of grinding force and temperature. Therefore, a soft grade should
be chosen for grinding hard material. On the other hand, during grinding of low strength soft
material grit does not wear out so quickly. Therefore, the grit can be held with strong bond so
that premature grit dislodgement can be avoided.
3.4 GRINDING WHEEL LOADING
There is another type of wheel wear phenomenon that has a disastrous effect on
grinding performance. Loading occurs when the work piece material adheres to the tips of the
abrasive grains and is brought into repeated contact with the material. Loading also occurs if
long work piece chips fill the pores of the abrasive and are retained there. The consequences
of loading and clogging are extremely poor surface texture of the work piece, increased
grinding forces and increased grinding wheel wear. To avoid loading, it is important to use
ample coolant with effective lubrication properties.
3.4.1 EFFECTS OF GRINDING WHEEL LOADING
Work piece damage
High grinding wheel wear
High Grinding forces
Increase in Grinding wheel work piece Interface Temperature
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High vibrations
3.4.2 FACTORS MAINLY CAUSE GRINDING WHEEL LOADING
Soft work piece materials
Coarse grade wheel material
High depth of cut
Grinding huge length materials with low width wheel
Operator skill
Very high transverse and longitudinal table speed
Insufficient coolant supplyLayout of machine
3.5 DRESSING OF GRINDING WHEEL
Dressing is the conditioning of the wheel surface which ensures that grit cutting edges
are exposed from the bond and thus able to penetrate into the workpiece material. Also, in
dressing attempts are made to splinter the abrasive grains to make them sharp and free cutting
and also to remove any residue left by material being ground. Dressing therefore produces
micro-geometry. The structure of micro-geometry of grinding wheel determines its cutting
ability with a wheel of given composition. Dressing can substantially influence the condition
of the grinding tool. Truing and dressing are commonly combined into one operation for
conventional abrasive grinding wheels, but are usually two distinctly separate operation for
superabrasive wheel.
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CHAPTER 4
MACHINE VISION AND IMAGE PROCESSING
(Courtesy: Understanding and applying Machine Vision , Hello Zeuch)
4.1 MACHINE VISION
Machine vision involves the acquisition of image followed by processing and
interpretation of data using computer for some useful application.Machine vision (MV) is the
technology and methods used to provide imaging-based automatic inspection and analysis in
the field of automatic inspection, process control, and robot guidance in industry.
Sophisticated manufacturing systems require automated inspection and test
methods to guarantee quality. Methods like machine vision can be applied in all the following
manufacturing processes: incoming receiving, forming, assembly, and warehousing and
shipping. However, hardware alone cannot be the main factor. The data which is being
obtained from such machine vision systems is the foundation for computer integrated
manufacturing. It ties all of the resources of a company together - people, equipment and
facilities.
Machine vision, or the application of computer-based image analysis and
interpretation, is a technology that has demonstrated its caliber to contribute significantly in
improving the productivity and quality of manufacturing operations virtually in every
industry. In many industries (semiconductors, electronics, automotives), some products can
not be produced without machine vision as an integral technology on production lines.
Machine Vision is the term associated with the merger of one or more sensing
techniques and computer technologies. Fundamentally, a sensor (typically a television-type
camera) acquires electromagnetic energy (typically in the visible spectrum; i.e., light) from a
scene and converts the energy into an image which can be used by the computer . The
computer extracts data from the image (often first enhancing or otherwise processing the data),
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compares the data with previously developed standards, and gives the results usually in the
form of a response.
The three main operations in Machine vision are
1. Image acquisition and digitization2. Image processing and analysis3. Interpretation
4.2 FUNCTIONAL BLOCK DIAGRAM OF MACHINE VISION SYSTEM
Figure 4.1. Block diagram of Machine Vision System
4.3 IMAGE PROCESSING
In imaging science, image processing is any form ofsignal processing for which the
input is an image, such as a photograph orvideo frame; the output of image processing may be
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either an image or a set of characteristics orparameters related to the image. Most image-
processing techniques involve treating the image as a two-dimensional signal and applying
standard signal-processing techniques to it. Image processing usually refers to digital image
processing, but optical and analogue image processing are also possible.
Image processing may occur in either the hardware or software. Image processing
hardware makes sense when large numbers of images are to be processed repetitively by the
same set of algorithms. Hardware implementation is faster than software execution but with
less flexibility. Most systems perform some image-processing operations in hardware and
some in software.
Image processing is generally performed on most images for basically two
reasons: to improve or enhance the image and, therefore, make the decision associated with
the image more reliable, and to segment the image or to separate the features of importance
from those that are unimportant. Enhancement might be performed, for example, to correct the
non-uniformity in sensitivity from photo site to photo site in the imaging sensor, correct
distortion, correct non-uniformity of illumination, to enhance the contrast in the scene, correct
perspective, etc.
Image processing is typically considered to consist of following steps
Image Acquisition and digitization
Enhancement/Preprocessing
Segmentation
Code/Feature Extraction
Image Analysis/Classification/Interpretation
4.3.1 IMAGE ACQUISITION AND DIGITIZATION
Image Acquisition is accomplished using a video camera. The camera is
focused on to the object of interest and image is obtained by dividing the viewing area into a
number of pixels in which each element has a value that is proportional to the light intensity of
the portion of the scene. Each pixel is converted into its equivalent dgital value by ADC.
http://en.wikipedia.org/wiki/Parameterhttp://en.wikipedia.org/wiki/Two-dimensionalhttp://en.wikipedia.org/wiki/Signal_(electrical_engineering)http://en.wikipedia.org/wiki/Digital_image_processinghttp://en.wikipedia.org/wiki/Digital_image_processinghttp://en.wikipedia.org/wiki/Optical_engineeringhttp://en.wikipedia.org/wiki/Analog_image_processinghttp://en.wikipedia.org/wiki/Analog_image_processinghttp://en.wikipedia.org/wiki/Optical_engineeringhttp://en.wikipedia.org/wiki/Digital_image_processinghttp://en.wikipedia.org/wiki/Digital_image_processinghttp://en.wikipedia.org/wiki/Digital_image_processinghttp://en.wikipedia.org/wiki/Signal_(electrical_engineering)http://en.wikipedia.org/wiki/Two-dimensionalhttp://en.wikipedia.org/wiki/Parameter7/30/2019 Full Reporte
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The two types of cameras are most commonly used are
Vidicon Camera
Solid state Camera
Solid state cameras have several advantages such as physically smaller more
rugged and image produced is more stable.
4.3.2 Enhancement/Preprocessing
Enhancement techniques transform an image into a "better" image, or one more
suitable for subsequent processing to assure repeatable and reliable decisions. There are three
fundamental enhancement procedures
Pixel or Point transformations
Image or Global transformations
Neighborhood transformations
4.3.3 Segmentation
Process of separating objects of interest (each with uniform attributes) from the
rest of the scene or background, partitioning an image into various clusters. Two of the most
common segmentation techniques are
1. Thresholding2. Edge detection3. Morphology
Thresholding
Thresholding is the process of assigning "white" (maximum intensity) to each
pixel in the image with gray scale above a particular value, while all pixels below this value
become "black". That particular value is the threshold and is a gray scale value. Areas that are
lighter than the threshold become white; areas darker than the threshold become black. The
resulting image, consisting of only black and white, is called a binary image. Thresholding
was the first segmentation technique used, and almost all systems use it to some extent. It has
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The dilation operation between two sets A and B involves transforming each individual
pixel in the A image by each pixel in the B image. In one definition of dilation, the
transformed image that results is characterized as the outermost image made up of the center
point of all the B images (typically the structured element) added to the A image.
Erosion is the opposite of dilation and is essentially a containment test. The erosion
operation between two sets A and B (typically the structured element) results in a transformed
image that is the universe of all center points of set B, where set B is fully contained in set A.
4.3.4 Code/Feature Extraction
Feature extraction is the process of deriving some values from the enhanced and/or
segmented image. These values, the features, are usually dimensional but may be other types
such as intensity, shape, etc. Some feature extraction methods require a binary image, while
others operate on gray scale intensity or gray scale edge-enhanced images. Code/Feature
extraction are grouped into three sections
Miscellaneous Scalar Features, including dimensional and gray level values;
Shape Features
Pattern Matching Extraction.
4.3.5 Image Analysis/Classification/Interpretation
For some applications, the features, as extracted from the image, are all that is
required. Most of the time, however, one more step must be taken; classified interpretation.
The most important interpretation method is conversion of units. Rarely will dimensions in
"pixels" or "gray levels" be appropriate for an industrial application. As part of the software, a
calibration procedure will define the conversion factors between vision system units and real
world units. Most of the time, conversion simply requires scaling by these factors.
Occasionally, for high accuracy systems, different parts of the image may have slightly
different calibrations (the parts may be at an angle, etc.). In any case, the system should have
separate calibration factors in X and Y.
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CHAPTER 5
MONITORING OF WHEEL LOADING AND DRESSING THROUGH
MACHINE VISION SYSTEM
5.1 MONITORING OF WHEEL LOADING AND WHEEL DRESSING ON MILD
STEEL SPECIMEN
Initially the grinding wheel is dressed in order to bring the wheel to its best working
condition. Speed of the grinding wheel is set at 2000 rpm, feed at 0.2 mm/rev and depth of cut
as 0.1 mm. Surface grinding operation had been carried out on Mild steel specimen. As a
result of surface grinding operation the wheel got loaded. Images of the grinding wheel were
taken at regular intervals of time. The captured image was transferred to computer and was
processed using Matlab software by using Global Thresholding technique.
Specifications of the Experiment
Machine - Hydarulic Surface Grinding Machine
Speed - 2000 rpm
Feed - 0.1 mm/rev
Depth of cut - 0.2 mm
Work piece - Mild steel
Camera - Kodak Easyshare CD14
Mega Pixel - 8.2megapixel
Optical Zoom - 3x
Shutter speed - upto 1/1000sec
Image processing software - Matlab
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5.2 PROGRAMME FOR GLOBAL THRESHOLDING WITH A THRESHOLD
RANGE USING MATLAB
clc;
clear all;
clf;
I=imread('F:\project vipin\phase 2\grinding wheel
images\digital camera photos\real\good\DSCF5200.JPG');
I=rgb2gray(I);k=0;y=0;
I=imresize(I,[280 280]);k=0;
y=0;
for i=1:280
for j=1:280
if(I(i,j)>200 & I(i,j)
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processed using Global Thresholding technique. The original image and processed image of
the fully dressed wheel is as shown in figure 5.1. Original image and processed image of the
grinding wheel after every 5 minutes of operation is as shown in figure 5.2. On the processed
image the white portion represents the loaded portion of the wheel.
Figure 5.1Original image and processed image of the fully dressed wheel
Figure 5.2Original images and processed images of loaded wheel while machining
Mild steel specimen at various time intervals (continued)
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Figure 5.2Original images and processed images of loaded wheel while machiningMild steel specimen at various time intervals (continued)
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Figure 5.2Original images and processed images of loaded wheel while machining
Mild steel specimen at various time intervals
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The percentage of loading obtained after every 5 minutes of operation is given in
table5.1. Plot with time along X-axis and percentage of loading along Y-axis is shown in
figure 5.3.
Table 5.1Percentage of loading obtained after every 5 minutes of operation on Mild
Steel Specimen
Sl No Time (minutes)Number of
white pixels
Number of black
pixels
Percentage of
loading
1 5 6 78394 0.007653
2 10 14 78386 0.017857
3 15 20 78380 0.02551
4 20 27 78373 0.034439
5 25 27 78373 0.034439
6 30 41 78359 0.052296
7 35 43 78357 0.054847
8 40 61 78339 0.077806
9 45 64 78336 0.081633
10 50 136 78264 0.173469
11 55 159 78241 0.202806
12 60 191 78209 0.243622
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0
0.05
0.1
0.15
0.2
0.25
0.3
5 10 15 20 25 30 35 40 45 50 55 60
Percentageofloading
Time (Minutes)
Figure 5.3Percentage of loading Vs Time
From this graph it is clear that percentage of loading increases with time which
indicates a positive relationship between time and wheel loading.
5.4 MONITORING OF WHEEL LOADING ON HCHCR STEEL SPECIMEN
Initially the grinding wheel is dressed in order to bring the wheel to its best working
condition. Speed of the grinding wheel is set at 2000 rpm, feed as 0.2 mm/rev and depth of cut
as 0.1 mm. Surface grinding operation had been carried out on HCHCR (High Carbon High
Chromium) Steel specimen. As a result of surface grinding operation the wheel got loaded.
Images of the grinding wheel were taken at regular intervals of time. The captured image was
transferred to computer and was processed using Matlab software, by using Global
Thresholding technique.
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Machine - Hydarulic Surface Grinding Machine
Speed - 2000rpm
Feed - 0.1mm
Depth of cut - 0.2mm
Work piece - HCHCR Steel
Camera - Kodak Easyshare CD14
Mega Pixel - 8.2 megapixel
Optical Zoom - 3x
Shutter speed - upto 1/1000sec
Image processing software - Matlab
5.5 RESULTS AND DISCUSSIONS FOR EXPERIMENT CONDUCTED ON
HCHCR STEEL
Surface grinding operation had been carried on HCHCR (High Carbon High
Chromium) Steel specimen. Images of grinding wheel were taken after certain intervals of
time with constant settings. These images were processed using Global Thresholding
technique. Original images and processed images of the grinding wheel after various trials is
as shown in figure 5.4. On the processed image the white marks represent the loaded portion
of the wheel.
Figure 5.4 Original images and processed images of loaded wheel while machining HCHCR
(High Carbon High Chromium ) Steel specimen at various time intervals (continued)
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Figure 5.4 Original images and processed images of loaded wheel while machining HCHCR
(High Carbon High Chromium ) Steel specimen at various time intervals (continued)
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Figure 5.4 Original images and processed images of loaded wheel while machining HCHCR
(High Carbon High Chromium ) Steel specimen at various time intervals
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The percentage of loading obtained after certain intervals of time is given in table 5.2.
Plot with trial along X-axis and percentage of loading along Y-axis is shown in figure 5.5.
Table 5.2Percentage of loading obtained after certain intervals of time on HCHCR
Steel specimen
Trial no Number of
white pixels
Number of black
pixels
Percentage of
loading
1 0 78400 0
2 7664 70736 9.77551
3 20027 58373 25.54464
4 30102 48298 38.39541
5 34630 43770 44.17092
6 40760 37640 51.9898
7 41911 36489 53.45791
8 44375 34025 56.60077
9 45029 33371 57.43495
10 61940 16460 79.0051
11 66995 11405 85.45281
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0
20
40
60
80
100
5 12 20 26 30 34 38 40 43 50 55
Percentage
ofLoading
Time (Minutes)
Figure 5.5Percentage of Loading Vs Time
From this graph it is clear that percentage of loading increases with time which
indicates a positive relationship between time and wheel loading.
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CHAPTER 6
MONITORING OF SURFACE ROUGHNESS (WITH RESPECT TO
WHEEL LOADING WITH THE AID OF MACHINE VISION SYSTEM)
6.1 OPTIMISATION OF DEPTH OF CUT AND FEED FOR SURFACE
GRINDING ON HCHCR STEEL
In order to establish a correlation between surface finish and wheel loading, the
experiments have to be carried out at optimized cutting conditions. So the first step was to
optimise the cutting conditions for surface grinding on HCHCR Steel. The Design of
Experiments (DOE) is an effective approach to optimise the parameters in manufacturing
related process.
The various parameters which commonly affected the surface finish are Speed, Feed,
Depth of cut, Tool material, Work material, Wheel loading etc. In this experiment tool
material and work material are made constant and the speed of grinding machine also
maintained constant at 2000 rpm.
To optimise Depth of cut and Feed full factorial experiment was carried out. Three
levels of depth of cut and feed which are frequently followed in many industries were selected
and surface roughness was taken as the response. Totally Nine experiments were conducted.
The three levels of depth of cut and feed selected were given in table 6.1.
Table 6.1Three levels of depth of cut and feed
Depth (mm) Feed (mm/rev)
Level 1 0.05 0.1
Level 2 0.1 0.2
Level 3 0.15 0.3
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6.2 RESULTS FOR OPTIMISED CUTTING CONDITIONS
Surface grinding operation was carried out on HCHCR steel specimen keeping the
speed as constant. Nine experiments were carried out for all the possible combination of 3
levels of depth of cut and feed. The surface roughness for all the combinations were measured
and shown in table 6.2.
Table 6.2 - Full Factorial Experiment
Sl No Depth of Cut (mm) Feed (mm/rev) Surface Roughness (m)
Ra1
Ra2
1 0.05 0.1 0.58 0.59
2 0.05 0.2 0.52 0.54
3 0.05 0.3 0.38 0.36
4 0.1 0.1 0.52 0.53
5 0.1 0.2 0.6 0.58
6 0.1 0.3 0.59 0.59
7 0.15 0.1 0.65 0.62
8 0.15 0.2 0.61 0.62
9 0.15 0.3 0.7 0.69
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Figure 6.1 - Main effects plot for Full Factorial Experiment
Figure 6.2 - Interaction effects plot for Full Factorial Experiment
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From the analysis, optimized parameters for surface grinding on HCHCR steel are
given in the table 6.4.
Table 6.4 - Optimised parameters for Surface Grinding On HCHCR steel
Depth of cut 0.05 mm
Feed 0.3 mm/rev
Speed 2000 rpm
6.3 EXPERIMENT CONDUCTED TO RELATE SURFACE ROUGHNESS AND
WHEEL LOADING
In order to relate surface roughness and wheel loading, experiments were conducted at
optimised cutting conditions. Optimised working conditions are Speed = 2000rpm, Feed = 0.3
mm/rev, Depth of cut = 0.05 mm. 18 sets of readings were taken at optimized working
conditions. Veho Usb Microscope with a magnification of 25X was used for capturing images.
The captured image was transferred to computer and was processed using Matlab software.
The photographic view of surface roughness measuring equipment is as shown in figure 6.3.
Machine - Hydarulic Surface Grinding Machine
Speed - 2000 rpm
Feed - 0.3 mm/rev
Depth of cut - 0.05 mm
Work piece - HCHCR (High Carbon High Chromium) Steel
Camera - Veho Usb Microscope
Mega Pixel - 2 mega pixelMagnification - 25X
Image processing software - Matlab
Image processing technique - Global Thresholding
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Figure 6.3 : Photographic view of Surface Roughness Measuring Equipment
6.4 RESULTS FOR RELATING SURFACE ROUGHNESS AND WHEEL
LOADING
Surface grinding operation was carried out on HCHCR steel specimen at optimised
working conditions. The experimental data were shown in table 6.5
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Table 6.5 - Experimental data for correlating Surface Roughness and Wheel Loading
Sl No Surface Roughness Ra (m) Wheel Loading (%)
1 0.34 0
2 0.34 0.122449
3 0.35 0.602041
4 0.38 1.191327
5 0.4 1.992347
6 0.43 2.517857
7 0.47 3.11352
8 0.5 4.21231
9 0.52 5.519133
10 0.55 7.195153
11 0.57 8.260204
12 0.58 9.960459
13 0.6 11.61735
14 0.61 12.66071
15 0.63 14.86352
16 0.62 17.26148
17 0.64 21.47321
18 0.66 24.95153
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The graph plotted with surface roughness along X axis and Wheel loading along Y axis
was shown in figure 6.4.
.Figure 6.4 - Wheel Loading Vs Surface Roughness
From the graph it is clear that surface roughness increases with percentage of loading
but not in a linear way. So it is necessary to develop an ANN model to predict surface
roughness corresponding to wheel loading.
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CHAPTER 7
DEVELOPMENT OF ANN (ARTIFICIAL NEURAL NETWORK)
MODEL TO PREDICT SURFACE ROUGHNESS
(Courtesy: NeuralNetwork Toolbox, Howard Demuth and Mark Beale)
7.1 MONITORING OF SURFACE ROUGHNESS THROUGH ANN MODEL
Artificial Neural Networks have been studied for many years in the hope of achieving
the human-like performance in the field of its application. These neural networks are
composed of many non-linear computational elements operating in parallel. Neural Networks,
because of their massive nature, can perform computations at a higher rate. Because of their
adaptive nature using the learning process, neural networks can adapt to changes in the data
and learn the characteristics of the input signals.
The functioning of ANNs depends on their physical structure. A neural network
usually consists of an input layer, a number of hidden layers, and an output layer. Back-
Propagation algorithm is utilized for the prediction of surface roughness. In back-propagation
neural network, the learning algorithm has two phases. First, a training input pattern is
presented to the network input layer. The network then propagates the input pattern from layer
to layer until the output pattern is generated by the output layer. If this pattern is different from
the desired output, an error is calculated and then propagated backwards through the network
from the output layer to the input layer. The weights are modified as the error is propagated.
The neural network computational model coding is built using MATLAB 2012a
software.
Before developing an ANN model to predict surface roughness, it is very much
important to identify the input and output parameters of the network. The forecasting
capability or interpolation capability of an Artificial Neural Network (ANN) model strongly
depends on the appropriate selection of input-output parameters. Since the experiment is
carried out at optimised speed, feed and depth of cut there is no need to vary these three
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parameters. Wheel Loading and Surface Roughness are taken as input and output parameters
respectively in this ANN model.
In this study, several machining tests were carried out and thus 18 pairs of input-output
data set were obtained during the machining trials.
7.2 NEURAL NETWORK DESIGN AND TRAINING
The network architecture topology or features such as number of neurons and layers
are very important factors that determine the functionality and generalization capability of the
network. The selection of the activation function and training algorithm also plays a
significant role to obtain better forecast of response variable. In this work, standard feed-
forward back-propagation hierarchical neural network has been considered for the prediction
of surface roughness. The neural network has been designed with MATLAB 2012a software.
The back propagation algorithm is a gradient decent error-correcting algorithm which updates
the weights in such a way that network output error is minimized.
The feed forward back propagation network usually consists of an input layer (where
the inputs of the problems are received, the inputs are the activity of collecting data from the
relevant sources. These data are fed to the neural network) one hidden layer (where the
relationship between the inputs and outputs are established represented by synaptic weights)
and an output layer which emits the outputs of the network. The number of hidden layer may
vary depending on the nature, complexity and non-linearity of the data at hand, but single
hidden layer is sufficient to deal with this work. The input layer has one neuron corresponding
to wheel loading and output layer also had one neuron corresponding to surface roughness.
There is no fixed rule for determining the number of neurons in the hidden layer. The number
of neurons in this layer must be large enough to provide non-linear evaluation space in thenetwork. Training of an ANN plays a significant role in designing the direct ANN-based
prediction. The accuracy of the prediction depends on how well it has been trained. The
training of the neural network using a feed-forward back propagation algorithm has been
carried out in the work.
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The ANN configuration is represented as 1-7-1 that is input layer consists of one input
neurons; the hidden layer consists of seven neurons and the output layer consisting of one
output neurons. The architecture of neural network is as shown in figure 7.1.
Figure 7.1 - Architecture of Neural Network
The network performs two phases of data flow. First the input information is
propagated from the input layer to the output layer and, as a result it produces an output. Then
the error signals resulting from the difference between the networks predicted values and the
actual values are back propagated from the output layer to the previous layers for them to
update their weights accordingly. The update of weights continues until the network error goal
is reached. The performance of the network was evaluated by mean squared error (MSE)
between the experimental and the predicted values for every output nodes in respect of
training the network. The feedback from that processing is called the average error or
performance. Once the average error is be low the required goal or reaches the required goal,
the neural network stops training and is, therefore, ready to be verified. MATLAB 2012a has
been used for training the network architecture which was developed to predict surface
roughness.
The input output dataset consists of 18 data pairs. These 18 data pairs were used for
training the neural network. The algorithm used for the neural network learning is the
backward propagation algorithm. This training algorithm offers higher accuracy in function
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approximation. It also facilitates faster training. After the training is completed, the network is
stored in a separate file.
The various training parameters used for training the ANN are
Training = tarinlm (Levenberg-Marquardt)
Perfomance = Mean Squared error
Number of Hidden layers = 7 (trial and error)
Epoch = 1000 iterations
Time = Infinity
Maximum fall = 7
Gardient = 1.00e-0.005
7.3 RESULTS OF ANN MODEL
The ANN configuration is represented as 1-7-1 that is input layer consists of one input
neuron; the hidden layer consists of seven neurons and the output layer consisting of oneoutput neuron. In this study, several machining tests were carried out and thus 18 pairs of
input-output dataset were obtained during the machining trials which are given in the table 7.1.
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Table 7.1 - Experimental data set for training ANN
Sl No Wheel Loading (%) Surface Roughness Ra (m)
1 0 0.34
2 0.122449 0.34
3 0.602041 0.35
4 1.191327 0.38
5 1.992347 0.4
6 2.517857 0.43
7 3.11352 0.47
8 4.21231 0.5
9 5.519133 0.52
10 7.195153 0.55
11 8.260204 0.57
12 9.960459 0.58
13 11.61735 0.6
14 12.66071 0.61
15 14.86352 0.63
16 17.26148 0.62
17 21.47321 0.64
18 24.95153 0.66
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The performance plot and regression plot for training ANN are shown in the figure 7.2
and 7.3 respectively.
Figure 7.2 : Perfomance plot for ANN
Figure 7.3 - Regression plot for ANN
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7.4 VALIDATION OF ANN MODEL
Once the training of neural network is done, it is possible to predict the surfaceroughness for a given value of wheel loading. By using the online monitoring system the
percentage of wheel loading can be determined. Once the percentage of wheel loading is fed
as input parameter to the ANN model it will predict the surface roughness corresponding to
wheel loading. Thus it is possible to monitor the surface roughness, but in offline condition.
Five random values of wheel loading were given to the neural network and it predicts
the surface roughness corresponding to that wheel loading value. Validation test was also
carried out. Five random values of wheel loading, the predicted value of surface roughness,
experimental values of surface roughness and percentage of error are given in the table 7.2.
Table 7.2: Predicted values of Wheel Loading for a given Surface Roughness
Sl NoWheel Loading
(%)
Measured Surface
Roughness Ra
(m)
Predicted
Surface
Roughness Ra
(m)
Percentage of
Error
(%)
1 0.89 0.36 0.3558 1.166667
2 2.8 0.45 0.45923 2.05111
3 7.2 0.53 0.55756 5.2
4 11.5 0.61 0.59453 2.388525
5 16.12 0.63 0.6258 0.666667
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CHAPTER 8
ONLINE MONITORING OF WHEEL LOADING AND WHEEL
DRESSING
8.1 DEVELOPED PROGRAMME FOR ONLINE MONITORING OF WHEEL
LOADING AND WHEEL DRESSING
A programme is developed in Matlab to automate the image capturing and analysing
process. Once the program is given a start it will automatically capture the image, transfers it
to the computer, process the image and gives the percentage of loading as well as the
processed image within 3 seconds.
8.2 PROGRAMME FOR ONLINE MONITORING PURPOSE USING MATLAB
vid = videoinput('winvideo', 1, 'RGB24_640x480');
src = getselectedsource(vid);
vid.FramesPerTrigger = 1;
vid.ReturnedColorspace = 'grayscale';
vid.ROIPosition = [518 98 549 620];
start(vid);
stoppreview(vid);
imwrite(getdata(vid),'C:\Users\vipin\Desktop\orgnalimage.jpg');
I=imread('C:\Users\vipin\Desktop\orgnalimage.jpg');
figure, imshow(I);
title('Original Image');
[b,c]=size(I);k=0;
y=0;
for i=1:b
for j=1:c
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if(I(i,j)>40 & I(i,j)
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Mega Pixel - 2 megapixel
Magnification - 25X
Focus - Manual
Image processing software - Matlab
Image processing technique - Global Thresholding
8.4 RESULTS AND DISCUSSIONS
Veho Usb Microscope with a magnification of 25X was used for capturing images.
Images of grinding wheel were taken after every 5 minutes of operation and are processed tofind out the percentage of loading. Image processing was carried out using Global
Thresholding technique in which a binary image is created with loaded portion in white pixels
and rest of background in black pixel. Using developed Matlab program, the whole image
capturing and analysing process was automated. Original image and processed image of the
fully dressed wheel is as shown in figure 8.1. Original image and processed image of the
grinding wheel after every 5 minutes of operation is as shown in figure 8.2. On the processed
image the white marks represents the loaded portion of the wheel.
Figure 8.1 - Orginal Image and processed image of fully dressed wheel
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Figure 8.2Original images and processed images of loaded wheel for the machining of
HCHCR (High Carbon High Chromium ) steel specimen at various time intervals
(continued)
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Figure 8.2Original images and processed images of loaded wheel for the machining of
HCHCR (High Carbon High Chromium ) steel specimen at various time intervals
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The percentage of loading obtained after every 5 minutes of operation is given in table
8.1. Plot with time as X-axis and percentage of loading as Y-axis is shown in figure 8.3.
Table 8.1 Percentage of loading obtained after every 5 minutes of operation on
HCHCR Steel Specimen
Sl NoTime
(minutes)
Number of white
pixelsNumber of black pixels Percentage of loading
1 0 1 78399 0.001276
2 5 26 78374 0.033163
3 10124 78276 0.158163
4 15153 78247 0.195153
5 201229 77171 1.567602
6 25
1935 76465 2.468112
7 302683 75717 3.422194
8 353389 75011 4.322704
9 405395 73005 6.881378
10 455805 72595 7.404337
11 509082 69318 11.58418
12 5512269 66131 15.64923
13 6013418 64982 17.1148
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Figure 8.3Percentage of Loading Vs Time
From this graph it is clear that percentage of loading increases with time which
indicates a positive relationship between time and wheel loading.
8.5 COST ESTIMATION
The cost estimation of the hardware for the online monitoring system for grinding
wheel loading and dressing is as shown in table 8.2
Table 8.2Cost Estimation for the Online Monitoring System
Sl No Item Quantity Required Cost (Rs)
1 Veho Usb Microscope 1 4500
2 Laptop 1 25,000
3 USB Extension Chord 1 50
4 Microscope Stand 1 250
Total Cost = Rs 29,800
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CHAPTER 9
CONCLUSION
The experiments carried out show the feasibility of using Machine Vision System and
Image Processing techniques in determining the wheel loading and wheel dressing. Global
Thresholding with a threshold range is suitable for determining wheel loading. Experiment
conducted on Mild Steel and HCHCR Steel for determining the wheel loading shows that the
percentage of wheel loading increases with time.
Since most of the industrial applications are concerned majorly with surface finish,
time to redress the wheel depends upon the surface finish of the machined component. ANN
was developed to predict the surface roughness corresponding to wheel loading. ANN model
developed has shown good results with 5.2 % of error in predicting the surface roughness.
A programme was developed on Matlab to automate the image capturing and analysing
process. The whole machine vision system is made useful for online monitoring purpose with
this programme. Programme developed for online monitoring of wheel loading and wheel
dressing has shown good results with zero percentage of error. Implementation cost for the
online monitoring of wheel loading and wheel dressing is Rs 29,800. (Exclusive of
programme cost and Matlab software cost).
Present scenario of wheel dressing is based on human prediction and experience. This
may cause wheel dressing before or after the set wheel loading, which may result in loss in
productive time and quality respectively. This system will result in reduction in non
productive t ime because wheel dressing time is application oriented and not based on humanjudgment.
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9.1 SCOPE FOR FUTURE WORK
It is suggested that the output of the online monitoring system i.e., the percentage of
loading has to be fed as input to the ANN model, so that the ANN model will predict the
surface roughness corresponding to the percentage of loading. This will make the monitoring
of surface roughness also online. So once the surface roughness required for a particular
application is reached, an alarm will be activated which indicates the dressing operation has to
be carried out.
Yes
NO
Determine the Percentage of loading using
online monitoring system
Use ANN Model to predict surface
roughness corresponding to wheel loadingobtained
Carry out Surface Grinding operation
Is Predicted Ra >=
Maximum
Initiate an alarm to carry out dressing
operation
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