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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
http://en.wikipedia.org/wiki/Abrasive_machininghttp://en.wikipedia.org/wiki/Grinding_wheelhttp://en.wikipedia.org/wiki/Cutting_tool_(machining)http://en.wikipedia.org/wiki/Cutting_tool_(machining)http://en.wikipedia.org/wiki/Cutting_tool_(machining)http://en.wikipedia.org/wiki/Cutting_tool_(machining)http://en.wikipedia.org/wiki/Cutting_tool_(machining)http://en.wikipedia.org/wiki/Grinding_wheelhttp://en.wikipedia.org/wiki/Abrasive_machining


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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
http://en.wikipedia.org/wiki/Imaging_sciencehttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Photographhttp://en.wikipedia.org/wiki/Video_framehttp://en.wikipedia.org/wiki/Outputhttp://en.wikipedia.org/wiki/Outputhttp://en.wikipedia.org/wiki/Video_framehttp://en.wikipedia.org/wiki/Photographhttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Imaging_science


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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/Parameter


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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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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


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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(High Carbon High Chromium ) Steel specimen at various time intervals (continued)


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(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




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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.


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 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 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



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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