tool monitoring using image processing

8/10/2019 Tool Monitoring Using Image Processing

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http://dx.doi.org/10.1016/j.cirpj.2013.02.005http://www.sciencedirect.com/science/journal/17555817mailto:[email protected]://dx.doi.org/10.1016/j.cirpj.2013.02.005http://crossmark.dyndns.org/dialog/?doi=10.1016/j.cirpj.2013.02.005&domain=pdfhttp://crossmark.dyndns.org/dialog/?doi=10.1016/j.cirpj.2013.02.005&domain=pdf


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from the results of signal processing techniques. Then prediction of

process data and process optimization canbe possible usingdesign

of experiment (DoE) and artificial intelligence (AI) techniques from

the extracted and selected features. Comparison of actual and

predicted values of selected features are also required to find out

the precision of that technique. Then optimized data are fed to the

machine controller and servo mechanism which can control the

machining process. Elbestawi et al. [34] comprehensively classified

different sensor systems for monitoring different output process

parameters viz. dimensions, cutting force, feed force, spindle

motor and acoustic emissions used in turning, milling and drilling

operations. Two excellent case studies have been conducted by

them using proposed multiple principal component fuzzy neural

network for classification of sharp tool, slightly worn tool, medium

worn tool, severe worn tool and breakage in turning and drilling

experiment using force, vibration and power signal. An online

monitoring of chipping in drilling process has also been conducted

by them using vibration signal with 97% success rate. Roth et al.

[106] emphasized wireless, integrated and embedded low cost

sensors; wavelet, time-frequency and time-scale analysis as a

signal processing approach; artificial neural network (ANN) and

support vector machine approach for assessment of tool condition;

hidden Markov model and recurrent neural network for the

prediction purpose in their comprehensive review of TCM forturning, milling, drilling and grinding processes. Nebot and

Subiron [92] reviewed the TCM systems of machining and

proposed a generic methodology combining DoE and ANN for

improved process modelling and prediction. Teti et al. [121] made

a comprehensive review on intelligent sensors for monitoring and

control of advancedmachining operation. They also mentioned the

real industrial implementationof the intelligent sensor systems for

TCM of advanced machining of complex-shaped parts made of

super alloy. Chandrasekaran et al. [19] made an comprehensive

literature review on the application of soft computing techniques

viz. neural network, fuzzy logic, genetic algorithm, simulated

annealing, ant colony optimization and particle swarm optimiza-

tion on turning, milling, grinding and drilling operations for

optimization of cutting conditions with minimum cost machiningwith maximum production rate based on prediction of process

outputs viz. surface finish, cutting force and tool wear.

The product quality is principally dependent on the machined

surface. The surfacequality ismainlydependent on the cutting tool

wear. Cutting tool wear is dependent upon cutting conditions,

work and tool material, tool geometry. There are four modes of

cutting tool wears, such as, adhesive wear due to shear plane

deformation, abrasive wear due to hard particles cutting, diffusion

wear due to high temperature and fracture wear due to fatigue.

Four principal types of wear occur in cutting tool and they are nose

wear, flank wear, crater wear and notch wear. Flank wear (as

shown in Fig. 1) occurs due to rubbing between tool flank surface

and work piece. Flank wear is specified by maximum flank wear

width (VBmax) or mean flank wear width (VBmean). Tool life

criterion is mainly dependent on the VBmean. Cutting tools are

experiencing three stages of wear [29] viz. initial wear (during first

few minutes), steady-state (cutting tool quality slowly deterio-

rates) and severe wear (rapid deterioration as the tool reaches the

end of its life). Crater wear are produced at the due the high

temperature for chip-tool interaction. This wear is characterized

by the crater depth and crater area.

Principally, tool condition monitoring systems can be classified

into two groups. They are, (a) direct techniques and (b) indirect

techniques. In direct techniques, flank wear width, crater depth

and crater area are measured directly either with tool makers

microscope, 3D surface profiler, optical microscope or scanning

electron microscope (off-line method) or with CCD camera (in-

process method). In indirect techniques, the measured parameters

or signals (viz. force, acoustic emission, current, power, surface

finish, etc.) of the cutting process allow for drawing conclusions

upon the degree of tool wear. Normally, these tool wear

monitoring systems are based upon the comparison of a reference

signal of an optimized cutting process with the actual process

signal [127]. These techniques have predominantly been imple-

mented, employing such varied technologies as acoustic emission,

cutting force, spindle current, and vibration sensors [99]. However,

there are some limitations of these methods. To overcome thoselimitations, research is going on to identify the degree of tool wear

by analyzing surface texture of machined surfaces with digital

imageprocessing technique from the images ofmachined surfaces.

There is a wide range of application of digital image processing

(DIP) using machine vision in machining processes like control of

surface quality, tool wear measurements, work piece surface

texture measurements, etc.

1.1. Advantages and disadvantages of DIP for tool condition

monitoring

There are some advantages of using digital image processing

techniques over other techniques to monitor any manufacturing

process.Such as, (1) it appliesno forceor load to the surface textureunder examination; (2) it is a non-contact, in-process application

[63]; (3) this monitoring system is more flexible and inexpensive

than other systems; (4) this system can be operated and controlled

from a remote location, so it is very much helpful for unmanned

production system; (5) this technique is not dependent on the

frequency of the chatter, directionality as acoustic emission (AE)

sensors are dependent on those factors; also, the AE sensors are

mainly detecting tool breakage inmachining [17,102,29]. Thus, the

monitoring of progressive wears of cutting tool is very difficult

using AE sensors; (6) vibration sensors (accelerometer) can

monitor tool breakage, out of tolerance parts and machine

collisions [52]; the progressive wear monitoring has not been

possible using vibration sensors; (7) DIP technique is not affected

Fig. 1. Flank wear and notch wear from the microscopic image of a tool insert.

S. Dutta et al./ CIRP Journal of Manufacturing Science and Technology 6 (2013) 212232 213


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by the high frequency forces as this high frequency forces cannot

be taken by dynamometer; also the force sensors are sensitive to

machine vibrations [53]; (8) to monitor and control a machining

process, the fusion of several sensors (AE sensor, dynamometer,

vibration signatures, etc.) is required, which is not at all cost

effective [52]; (9) however, the machined surface image carries the

information of tool imprint as well as the change of tool geometry

[9]; thus, a roughness, waviness and form information can be

obtained by analyzing a machined surface image [15]; (10) a 2D

information canbe obtained from a machined surface image which

is not possible to get by a 1D surface profiler [122]; (11) also, the

information of machining parameters can be obtained from

machined surface images [31]; (12) the development of CCD

cameras has also contributed to the acceptance of industrial image

processing, since CCD cameras are less sensitive to the adverse

industrial environment; (13) optical image processing has brought

about the possibility of adding, subtracting, multiplying, storing

and even performing different image transforms using optical

devices; (14) three dimensional surface roughness of machined

surface can be measured, accurately, using scanning type 3D

surface profiler [1,23,88,95]; however, these 3D measurements are

not effective for in-process or online tool conditionmonitoring due

to uneconomic time, cost ineffectiveness and inaccessibility to the

machine tools; to overcome this situation, a machine vision basedsystem can be useful for monitoring purpose. However, there are

some limitations for using machine vision system in tool condition

monitoring techniques also [141]. (1) An appropriate illumination

system, robust image processing algorithm, protection from

machining noises (chips, dirts, etc.) are very much essential for

the successful implementation of this technique [9]. (2) Monitor-

ingofdrillpartsusing DIP are verydifficult due to its inaccessibility

[51]. However, a method to monitor deep hole parts has been

developed in recent years [84].

This paper is composed of five major components. The first

component presents an overview of digital image processing

techniques used for tool condition monitoring. The second

explains lighting systems which are used in TCM. The third

presents direct TCM techniques usingdigital imageprocessing.Thefourth component presents different in-direct TCM techniques

using image processing. And the final and last component draws

overall conclusions and suggests future directions for TCM

research through digital image processing technique.

2.

Digital

image

processing

techniques

Image acquisition is the first step of any machine vision system.

In case of TCM, images of cutting tool (rake face or flank surface) or

work piece surface are captured with a CCD (Charged Coupled

Device) camera or CMOS (Complementary Metal-Oxide Semicon-

ductor) digital camera. CCD camera is comprised of CCD sensor

which

is

an

array

of

photosensitive

elements

to

collect

electricalcharges generated by absorbed photons. Those electrical charges

are then converted to an electrical signal which is converted to a

digital image via frame grabber. Finally, the image is transferred to

a PC for processing purpose [50]. CMOS is different from CCD

sensor by its faster capturing rate. CMOS sensor can acquire frames

faster than CCD camera. But the sensitivity of CMOS sensor ismuch

less than CCD sensor. To create a digital image, a conversion is

needed from the continuous sensed data into digital form. This

involves two processes: sampling and quantization. Digitization of

coordinate values and amplitude values are called sampling and

quantization. Image magnification is also possible by linear

interpolation, cubic interpolation, cubic convolution interpolation

etc. Different types of neighbourhood operations are also needed

for

further

processing

[41].

From the illumination point of view, an Image f(x, y) may be

characterized by two components: (1) the amount of source

illumination incident on the scene, and (2) the amount of

illumination reflected by the objects. Appropriately, these are

called the illumination and reflectance components and are

denoted by i(x, y) and r(x, y), respectively. The two functions

combine as a product to form f(x, y),

f x;y ix;yrx;y (1)

Image pre-processing is required for the improvement of

images by contrast stretching, histogram equalization, noise

reduction by filtering, inhomogeneous illumination compensa-

tion etc. To increase contrast in an image, contrast stretching and

histogram equalization are two mostly used techniques. To

reduce noise, low pass filtering is very important technique. It

includes image smoothing by using low pass filtering in both

spatial and frequency domains. In spatial low pass filtering, a

filter mask is convolved with the image matrix to reduce

unwanted noise present in the image (image smoothing). Order

statistics or median filter is used to remove impulse noise in an

image (image smoothing). Butterworth and Gaussian low pass

filters are some common low pass filters in frequency domain.

High pass filters are used to enhance the sharpness of an image(image sharpening). Unsharp masking (to emphasize high

frequency components with retaining low frequency compo-

nents), Laplacian filter (second order filter) are some spatial high

pass filters used for image sharpening purpose [41]. Image

filtering and enhancement operations are very much essential to

reduce the noise of the images specially for cutting tool images,

because there are a chance of noise due to the dirt, oils, dust of

machining on the object surface. The low-pass filtering (e.g.

median filter, Gaussian filter, etc.) is useful to reduce the noises

present in the cutting tool wear images and machined surface

images. Also the high pass filtering technique can be useful to

enhance tool wear profile and for clear identifications of feed

marks in machined surface images.

After pre-processing, image segmentation and edge detectionare generally done to segment the worn region of cutting tool

from the unworn region and also to detect the edges of the feed

lines of themachined surface images. Image segmentation is the

method of partitioning an image intomultiple regions according

to a given criterion. Feature-state based techniques collect pixel/

region properties into feature vectors and then use such vectors

for assigning them to classes, by choosing some threshold

values. While feature-state based techniques do not take into

account spatial relationships among pixels, image-domain based

techniques do take them into account; for example, split and

merge techniques divide and merge adjacent regions according

to similarity measurements; region growing techniques aggre-

gate adjacent pixels starting fromrandomseeds (region centres),

again by comparing

pixel values. Watershed-based segmenta-tion technique can be useful for micro and nano surface

topography. Watershed analysis, which consists in reasoning

over a surface topography in terms of hills and dales, actually

originates from the work by Maxwell on geographical analysis.

Watershed-based surface segmentation consists in partitioning

the surface topography into regions classified as hills (areas from

which maximum uphill paths lead to one particular peak) or

dales (areas from which maximum downhill paths lead to one

particular pit), the boundaries between hills being watercourse

lines, and the boundaries between dales being watershed lines

[2].

The edge detection operation is used to detect significant edges

of an image by calculating image gradient and direction. Gradient

and direction of an image f(x, y) are defined in Eqs. (2) and (3),

S. Dutta et al./CIRP Journal of Manufacturing Science and Technology 6 (2013) 212232214


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respectively.

GxGy

d f

dxd f

dy

2664

3775 (2)

ux;y tan1 GyGx

(3)

where u is measured with respect to the x-axis.

Robert operator (sensitive to noise), Sobel operator, Prewitt

edge operator are some first order edge detectors which are very

useful for automatic detection of tool wear profile. Canny edge

detector is widely used in the field of machine vision because of its

noise immunity and capability to detect true edge points with

minimum error. In Canny edge detection method, the image is first

convolved with Gaussian smoothing filter with standard deviation

s. This operation is followed by gradient computation on the

resultant smoothed image. Non-maxima suppression, double

thresholding and edge threshold selection with Bayes decision

theory are the steps to implement Canny edge detection. Gradient

images of tool flank wear (experimentally obtained from milling

operation) and machined surface (experimentally obtained from

turning operation) usingCanny edge detector are shown in Fig.2. Awear profile or edges of surface texture can be obtained by this

method. The edge detector based on double derivative is used to

detect only those points as edge points which possess local

maxima in the gradient values. Laplacian and LaplacianofGaussian

are the most commonly used double derivative-based edge

detectors.

For partitioning a digital image into multiple regions, grey level

thresholding techniques are computationally inexpensive. Based

on some optimal threshold, an image can be partitioned into

multiple regions. For example, to partition the flank wear profile

from itsbackground, thresholding techniques are generallyused. A

very common thresholding technique used in tool wear measure-

ment is Otsus optimal thresholding technique. In this technique, a

class, C0 is formed with all the grey value V(k) for a grey levelintensity, k and all the other form another class, C1. Optimal k value

is selected for maximum between-class variance. In bi-level

thresholding technique imagesarepartitioned into foreground and

background segments and in multilevel or dynamic thresholding,

images are divided into more than two segments. In entropy-based

thresholding, the threshold value is selected in such a way, so that

the total entropy value of foreground and background is maximum

[2]. Thresholding techniques are important forbinarizationofflank

wear profile.

After edge detection and thresholding, morphological opera-

tions viz. erosion, dilation, closing, opening are important tools for

completing the wear profile, accurately. In this operation, a

noiseless morphology is obtained by introducing or removing

some

points

or

grey

values

in

a

profile

[41].Tool condition monitoring via surface texture of machined

parts are mainly dependent on the texture analysismethod. This

methodcan be applied after pre-processing. Texture is a repeated

pattern, whichisa setof local statisticsorattributes vary slowlyor

remain approximately periodic. Primitive in texture is a con-

nected set of pixels, characterized by a set of attributes

(coarseness and directionality). For example, in case of turned

surface, a repetitive feed marks can be obtained as texture

primitives. Texture analysis can be done using statistical,

geometrical, model-based and signal processing basedmethods.

In statistical method a texture is modelled as a randomfield and a

statistical probability density function model is fitted to the

spatial distribution of intensities in the texture. Higher-order

statistics like run-length statistics,

second order statistics like

grey level co-occurrence matrix (GLCM) can beused as statistical

texture classifiers. In geometric texture analysis method, the

analysis depends upon the geometric properties of texture

primitives. Voronoi tessellation, Zuckers model are some of the

geometric texture analysis methods. In model based methods,

texture analysis is done with some signal model like, Markov

random field, Gibbs random field, Derin-Elliot, auto-binomial,

fractal (self-similarity) models are some mathematical model-

based texture analysis methods. In signal-processing based

texture analysis, spatial domain filtering, Fourier-domain filter-

ing, Gabor and wavelet analysis are some common texture

analysis methods [125].

3.

Lighting

systems

Lighting system is the most important and critical aspect to

receive a proper image for image processing. Due to inhomoge-

neous illumination for improper lighting set-up, the information

from images will not be sufficient for any machine vision

application. Several researches give strongimportanceon lighting

set-up for tool condition monitoring using image processing.

Lighting systems required are varying depending on applications

viz. for capturing tool wear image and machined surface image.

Weis [132] triedto capture thetoolwear imageusing adiodeflashlight incorporated with a infrared band filter, which helped to

enhance the tool wear region with respect to the background.

KuradaandBradley [73] usedtwofibre-opticguides tocapture the

tool wear regions. They used it to obtain adequate contrast

between the worn and unworn tool regions. Pfeifer andWeigers

[99] usedring of LEDs attachedwith camera to capturethe proper

illuminated images of tool inserts from different angle. Kim et al.

[70] useda fibre optic light surrounding the lens to illuminate the

flank face portion of a 4-fluted end mill. They also examined that

the best measurement of flank wear can be possible with a high

power lighting (60 W). Jurkovic et al. [58] utilized a halogen light

to illuminate the rake andflank face of the cutting tool and a laser

diode and accessories to obtain a structured light pattern on the

face of the tool to detect the tool wear by the deformation ofstructured light on the rake face. Wang et al. [131] used a fibre

optic guided light to illuminate the flank portion of each insert

attached to a 4-fluted milling tool holder and capture the

successive images in a slow rotating condition by using a laser

trigger with very less blurring. A white light from a fluorescent

ring as well as light from a fibre bundle was used to minimize

specular reflections on capture the tool imagesby Kerr et al. [68].

So, highly illuminated and directional lighting is required to

capture the tool wear region as to get a very accurately

illuminated image. Wong et al. [134] used a 5 mW HeNe laser

0.8mm-diameter beamfor focusingonto themachined surfaceby

a lens at an incident angle of 308 for capturing the centre of the

pattern. Then the reflected light pattern was formed on a screen

made

of

whitecoated glass from

where

the

scattered patternwasgrabbed using a CCD camera. The setup was covered in order to

minimize interference from ambient light and a consistent

lighting condition for all the tests has been provided. But the

actual image of the machined surface is required instead of

reflectedpattern.Tsai etal. [123],triedtoobtaina homogeneously

illuminatedmachined surface image bya regularfluorescent light

source which was situated at an angle of approximately 108

incidence with respect to the normalof the specimen surface. The

camera was also set up at an angle of approximately 108 with

respect to the normal of the specimen surface to obtain image at

the direction of light. But this set-up may only be useful for flat

specimens not for curved surfaces. Bradley and Wong [16] used a

fibre optic guided illumination source and a lighting fixture. A

uniform

illumination of

the

machined surface was

ensured

by



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changing the position of lighting fixture. During surface assess-

ment, the specimen was positioned on the platformso thatthe lay

marks were perpendicular to the longer dimension of the CCD

sensor. The light sourcewas positioned at a distance of 8 cm from

the surface, as

this

provided

the

best image

contrast. In

thistechnique, the images of flat specimens (end milled) were

captured but the images of turned surface (i.e. curved surfaces)

were not obtained. Leeet al. [78] useda diffused,blue light source

situated at an angle of approximately 458 incidence with respect

to the machined (turned) surface specimen to accomplish the

illumination of the specimens. Alegre et al. [4], explained about a

diffusedlighting system(aDC regulated light sourcewith infrared

interference filter for cool illumination) for capturing images of

turned parts. They also used a square continuous diffused

illuminator for getting diffused illumination in the camera axis.

The last lighting system is most appropriate for obtaining a

homogeneously illuminated image of turned or curved parts. A

cover can beused to reducethe interference of ambient lighting in

industrial environment.

4. Direct TCM techniques using image processing

There are two predominant wear mechanisms for a cutting

tools useful life: flank wear and crater wear. Flank wear occurs

on

the relief

face of

the tool and is

mainly attributed

to therubbing action of the tool on the machined surface. Crater wear

occurs on the rake face of the tool and changes the chip-tool

interface, thus affecting the cutting process. Tool wears increases

progressively during machining. It depends on the type of tool

material, cutting conditions and lubricant selected. Online

measurement of tool wear by image processing after taking

images of cutting tool through machine vision system is under

research. This technique is coming under the area of direct tool

condition monitoring. Flank wear can directly be determined by

capturing images of cutting tool but a more complex technique is

required to determine the crater depth [59]. Cutting tool wears

have been measured by two dimensional and three dimensional

techniques in various researches which are described in the

following

sections.

Fig. 2. (a) Milling tool wear image and (b) corresponding gradient image using Canny edge detector (c) turned surface image and (d) corresponding gradient image using

Canny edge detector.



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function for accurate segmentation of wear contour of cutting

inserts used for milling operation. They measured the tool wear

area by this method. However, the measurement of flank wear

width had been missing in their research.

Otieno et al. [96], studied flank wears of two fluted micro end

mills ofdiameter 1 mm,0.625 mm and0.25 mmwithdigital image

processing techniques using filtering and thresholding by XOR

operator. But any edge detection, tool wear quantification and

wear classification was not performed.

Inoue et al. [47] made a generalized approach by detecting

defects in rod-shaped cutting tool via edge detection (by Prewitt

operator) and extracted image parameters after performing

discrete Fourier transform (DFT) on the edge detected image.

However, many unstudied defects cannot be possible to recog-

nized by this system.

Jackson et al. [49] utilized aneural imageprocessingmethod for

accurate detection of very small wear developed in very small

diameter milling cutter on the environmental scanning electron

microscopic (ESEM) images of tool. They have even measured the

small average wear of 5mm developed in a 9.5 mm diameter

milling cutter. Though this technique is very much useful for

micro-machining, but the method is very much difficult to use

online.

Grain fracture, bond fracture and attritous wear are three typesofpre-dominant wears in grindingwheel.Wear flats aredeveloped

on the grinding wheel surface due to attritous wear. Consequently,

the increasing rate of wear flats area develops heat and burn the

workpiece. But the automatic and precise segmentation of true

wear flats are quite challenging task from the wheel surface

images. An edge detection approach after thresholding were

utilized to distinguish true wear flats from its background [138].

However, the accurate selection of intensity threshold and edge

threshold was a difficult task. To overcome this problem, Lachance

et al. [75]utilizeda region growingmethod for segmenting the true

wear flats from its background. However, some morphological

techniques can be utilized for more accurate computation of wear

flat area. Heger and Pandit [43] captured the images of grinding

wheel surface by multidirectional illumination and image fusionfor obtaining more detailed information. Then they have utilized

multi-scale wavelet transform and classification technique for

distinguishing the grains and cavities on the surface. A new

approach to discriminate the fresh and worn out grinding wheels,

progressively, has been established by Arunachalam and Rama-

moorthy [10]. They extracted some texture descriptors for

describing the condition of grinding wheel surface utilizing

histogram based, GLCM based and fractal based texture analysis

methods on the wheel surface images taken at different progres-

sive time. However, no explanation regarding the variations of

selected features with the progressive wear has been encountered.

In the area of integrated circuit (IC) manufacturing, the surface

of stamped tool or cutting dust has been monitored real time by

Kashiwagi

et

al.

[62].

They

captured

the

surface

image

of

cuttingdust and determined the width of stamped line by using image

histogram and cross-correlation technique. They observed that the

width was decreasingwith the increase of cutting time or decrease

of tool sharpness.

4.2.

Three

dimensional

techniques

Three dimensional measurement techniques are used to

measure the crater depth accurately. Yang and Kwon [137,136]

first used a microscope equipped with a CCD sensors to capture

noisy images of rake face of an worn out tool insert and measured

thedepth of crater indifferent levels ofwearby automatic focusing

technique. They have used image consolidation and median

filtering

to

remove

high

frequency

noises

without

blurring

from

rake face image. Then they thresholded optimally for segmenting

the worn region from the background and detected the crater

contour by using Laplacian method. Edge linking and dilation

methods incorporating eight neighbourhood chain coding have

been applied on that contour to get an accurate shape of crater

region. A Laplacian criterion function incorporating an infinite

impulse response (IIR) filter has been used for getting the focused

position along z-direction. A hybrid search algorithm with

polynomial interpolation and golden search technique has been

utilized to improve the accuracy of the automated focusing

technique, in this method. This way they measured the crater

depth. They used seven features (four were related to flank wear

and three were related to crater wear) to classify flank wear, crater

wear, chipping and fracture. A mathematical model was intro-

duced in their work to obtain flank wear profile from crater wear

contour. Then they selected 12 input nodes (each node contains

seven feature parameters) and 4 output nodes (flank wear, crater

wear, chipping and fracture) in a multi-layer perceptron (MLP)

neural network to classify four types of wear. All the tests were

done on a P20 cemented carbide tool insert without chip breaker.

Though the work is pioneering the crater depth measurement very

accurately, but the 3D map of crater region has not been evaluated

by this offline technique. Also, it may be difficult to use their

technique for insert with chip breaker due to the major undulationof rake surface. Ramamoorthy and co-workers [61,100]used image

processing with stereo vision technique with only a single CCD

camera todetermine thedepth of each point in the crater. Trends of

tool wear pattern were then analyzed with a MLPNN algorithm,

where inputs were speed, feed, depth of cut and cutting time and

output parameters were flank wear width and crater wear depth.

However, the crater depth estimation less than 125 mm could not

be obtained accurately by this technique. Also some pre-

processing algorithm were required to eliminated the noises from

dirt, chip, oil etc. on the rake face to make the method possible in

online.

Ng and Moon [93] proposed a technique for 3D measurement of

tool wear for micro milling tool (50 mm diameter) by capturing

images with varying the tool and camera plane distance with15 mm resolution. Then they have re-constructed 3D image from

the captured imagesusing digital focusmeasurement. Finally, they

proposed that the tool wear measurement could be possible by

combining the actual 3D image and the 3D CAD model of the tool.

However, no depth measurement had been performed in their

work.

Devillez et al. [24] utilized white light interferometry technique

to measure the depth of crater wear and determined the optimal

cutting conditions (cutting speed and feed rate) to get the best

surface finish in orthogonal dry turning of 42CrMo4 steel with a

uncoated carbide insert. In white light interferometry technique, a

vertical scanning has been performed to get the best focus

positions for each and every point presented in the object to be

measured.

White

light

is

used

to

get

the

high

resolution

(sub-nanometer) and high precision measurements over a wider area.

However, this technique is an offline technique and the measure-

ment of crater depth of grooved inserts or inserts with chip breaker

is quite challenging for this technique. Dawson and Kurfess [22]

used a computational metrology technique to determine the flank

wear and crater wear rate of a coated and uncoated cubic boron

nitride (CBN) tool for progressive wear monitoring in offline. They

have acquired the data of the worn out cutting insert by using

white light interferometry and compute the volume reduction in

the insert by comparing those data with the CAD model of fresh

insert developed by using computational metrology. However, no

grooved insert has been used in their technique. Wang et al. [128

131] measured various parameters viz. crater depth, crater width,

crater

centre

and

crater

front

distance

of

crater

wear

by



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Table 1

Direct TCM techniques based on image processing.

Researcher Illumination sys-

tem

Image processing Type of tool wear

measurement

Machining Remarks

Galante et al. [40] Diffused lighting Thresholding Flank wear Turning Offline, 2D technique

Weis [132] Diode flash light

with

infra-red

band

filter

Dilation and thresholding Flank wear

measurement

Milling No evaluation of accuracy

Kurada and

Bradley

[73]

Fibre optic guided

light

Image enhancement, Image

segmentation,

thresholding,morphological operation

Flank wear

measurement

Turning Offline

Tauno and

Lembit [120]

Blue light source Median filter, Roberts edge

detector, thresholding

Flank wear

measurement

Turning, milling Offline, 8% error

Pfeifer and

Weigers [99]

Ring of LED Method to set optimum

incidence angle of lighting for

controlled illumination

Flank wear

measurement

Turning, milling Online

Sortino

[116]

Median

filtering

Statistical filter for edge

detection

Flank

wear

measurement

Generalized

for insert

Offline

Flank wear

Jurkovic

et

al.

[58]

Halogen

light

along

with a laser diode

Manual

measurement

using

a

image processing software

Flank

wear

and

deformation of

laser light pattern

on rake face

Tool

inserts

Manual

measurement,

crater

depth measurement has not

been done

Wang et al.

[128131]

Laser trigger

synchronized with

camera, fibre optic

guided light

Find critical area, find reference

line, pixel to pixel scan for

measuring VBmaxfrom reference

line

Flank wear

(captured when

tool is moving)

Milling inserts Online, max error 15mm,

difficult to measure coated

carbide inserts

Liang et al. [83] Backlighting Image registration, spatial

transformation, image

subtraction,

similarity

analysis

Nose wear Inserts Difficult to implement for

flank wear width

measurement

Sahabi and

Ratnam [108]

Backlighting Weiner filter, thresholding,

detection and subtraction of

wornandunwornprofile in polar

co-ordinate

Flank wear from

nose radius and

surface roughness

profile

Inserts 7.7% (from nose) and 5.5%

error (from surface

roughness), difficult to

implement in very low feed

application

Fadare and Oni [35] 2 incandescent

light sources

inclined at 458

Weiner filter, shadow removing,

canny edge detection, pixel

counting

Flank wear Inserts Sensitive to the fluctuation of

ambient light

Kerr et al. [68] White ring light,

fibre

optic

guided

light

Unsharp mask, manual

measurement,

histogram

analysis, GLCM analysis, Fourier

spectrum analysis, fractal

analysis

Flank wear

measurement

via

texture descriptors

Turning inserts,

end

mill

cutter

Texture analysis of wear

region,

no

automatic

measurement of wear

Lanzetta [76] Structured lighting

with Laser

Resolution enhancement,

averaging, segmentation

Flank and crater

wear

Generalized

for insert

The effect of dirt, oils on

inserts did not addressSchmitt et al. [112] and

Stemmer et al. [117]

Ring light (for full

and side

illumination)

Sobel filter, line interpolation,

histogram transformation,

morphological opening &

closing, blob analysis, contour

detection

for

measurement;

NN

for flank wear and breakage

classification

Flank wear

measurement,

wear and breakage

classification

Milling Resolution 4.4mm,

classification error 4%; the

method has not been applied

for different variety of cutting

inserts

Castejon et

al.

[18]

and

Barriero et al. [13]

DC

regulated

light

with square

continuous

diffused

illuminator

Low

pass

filter,

cropping,

histogram stretching, manual

segmentation,moment invariant

methods (Zernike, Legendre, Hu,

Taubin, Flusser), and linear

discriminant analysis for

classification

Classification

of

low, medium and

high wear

Inserts

99.88%

discrimination

for

Hus descriptor, no wear

prediction has been

performed

Alegre et al. [6] DC regulated light

with square

continuousdiffused

illuminator

Contour signature based on

Canny edge detected image, k-

NN

and

MLPNN

for

classification

Classification of

low and high wear

Inserts 5.1% classification error;

three levels of wear

classification

is

needed

Atli et al. [11] Silhouette image of

tool

Canny edge detection,

measurement

of

deviation

from

linearity of tool tip

Drill-bit Drilling Only useful for drilling; Flank

wear

width

cannot

be

measured

Makki et al. [86] Silhouette image of

tool captured at

1001500 r.p.m

Canny edge detection, best

fitting algorithm

Tool run out

detection

Drilling Tool run-outperpendicular to

the image plane had not been

measured

Liang and Chiou [82] Circular back

lighting

Spatial moment edge detection,

edge sorting, B-spline

smoothing,

gaussian

LPF,

thresholding, morphological

operation

Flank wear

detection for

progressive

machining

Multi-layer

twist drill

Results were not compared

with the microscopic wear

measurement;

applicable

for

no smear image

Su

et

al.

[118]

Circular

lighting

Accurate

edge

detection

proposed, rotation, automated

measurement

Flank

wear

detection for

progressive

machining

Micro

drill-bit

(for PCB drilling)

Resolution

0.996

mm/pixel;

only applicable when no

smearing in cutting plane

image



10/21

reconstructing a 3D crater profile by capturing four fringe patterns

with four phase shifting angles. No scanning is required in this

method unlike white interferometry technique. However, the

accuracy of the measurement is dependent on the fringe width or

fringe pattern.

Table 1 summarizes the application of digital image processing

in direct tool wear monitoring.

So, in direct technique, condition monitoring is done by

analyzing the change in geometry of the cutting tool. Chatter,

vibration, cutting force change etc. are not taken into account with

cutting tool observation whereas surface finish can emphasize

those changes as well as change in tool geometry. So, researchers

are

going

to

take

the

measurement

of

surface

finish

throughindirect TCM techniques using image processing of machined

surface images.

5. Indirect TCM techniques using image processing

Diversepropertiesplay an important role in the surfacefinish of

metallic parts, e.g. mechanical strength, wear resistance of the

surfaces or geometrical and dimensional quality of the parts. These

properties are directly related to the surface finish level, which is

dependent on the manufacturing process parameters and the

materials used. Thus, the measurement of the surface finish has

been a research matter of special interest during last sixty years in

machining sector. There are tactile and non-tactile techniques to

assess

the

surface

quality

of

the

machined

parts.

In

tactile

techniques, surface roughness parameters are measured using a

stylus instrument; whereas in non-tactile method, surface

roughness parameters are obtained from the images of machined

surface textures.But there is a chance of scratches on softmaterials

in tactile techniques due to the tracking of stylus on measurable

surface; whereas non-tactile techniques are becoming more

advantageous due to the advancement of computer vision

technology. While tactile techniques characterize a linear track

over the surface of the part, the computer vision techniques allow

characterizing whole areas of the surface of the part, providing

more information [8,111,113]. Besides, computer vision techni-

ques take measures faster, as images are captured in almost no

time

and

so

they

can

be

implemented

in

the

machine.

According

tothis, it is possible to apply these techniques for controlling the

processes in real time on an autonomous manner. An exhaustive

validity check can also be made to every single part produced.

Continuous advances have been made in sensing technologies and,

particularly, in the vision sensors that have been specially

enhanced in capabilities with lower cost. The advances made in

the image processing technology also provide more reliable

solutions than before. In all, computer vision is a very useful

non-invasive technique for the industrial environment. The use of

these systems in other monitoring operations in machining

processes has proved [5,18] an important reduction in the cycle

time and the resources. In this field, two guidelines should

be remarked: the study in spatial domain and in frequency

domain

[56,133]. Indirect

tool

condition

monitoring

using

image

Table 1 (Continued )

Researcher Illumination sys-

tem

Image processing Type of tool wear

measurement

Machining Remarks

Duan et al. [30] Front lighting with

LED

Histogram generation, level set

based contour segmentation,

histogram based contour

segmentation, fusion of both

segmentation, wear

measurement

Flank wear

detection for

progressive

machining

Micro drill-bit

(for PCB drilling)

Capable to remove the noise

due to smearing; More

computation time

Xiong et al. [135] Fluorescent high

frequency linearlight

Variational level set based

segmentation, no need for re-initialization of zero level set

Tool wear area Milling inserts No measurement of flank

wear width

Otieno et al. [96] Dome light with

low intensity back

lighting

Histogram equalization,

Gaussian filtering, XOR

operation for edge detection

Micro-Milling tool No measurement of wear

Yasui et al. [138] Microscope Thresholding, edge detection to

segment the wear flats from its

background

Grinding wheel

wear

Grinding Accuracy is low, possibility

for detection of false wear

flats

Lachance et al. [75] Fibre optic guided

light with beam

splitter

Thresholding, region growing Progressive wear of

grinding wheel

Grinding Morphological operations

will be lead to more accurate

segmentation

Prasad and

Ramamoorthy [100]

White light Histogram, GLCM and fractal-

based texture analysis

Progressive wear of

grinding wheel

Grinding Simple, faster but less

accurate

Karthik et al. [61] Automatic focusing

at various height

(interpolation

and

search technique

for

improvingaccuracy)

Image consolidation, median

filtering, thresholding, laplacian

contour

detection,

edge

linking,

dilation, chain coding, MLPNN

for

classification

Flank and crater

wear (depth)

measurement

and

classification

Turning inserts Leads to 3D measurement;

flank wear, crater wear,

chipping

and

breakage

were

classified; 3D map for crater

wear

has

not

been

evaluated;difficult

for

grooved

inserts

Prasad and

Ramamoorthy [100]

Stereo vision

technique using

law of triangulation

Stereo image processing for

getting the 3D map of crater,

MLPNN

Flank wear and

crater wear

prediction and

progressive wear

measurement

Turning inserts Less accurate technique for

crater depth less than

125mm; no technique to

reduce the noises from dirt,

dust, oil etc. difficult for

grooved inserts

Devillez et al. [24] White light

interferometer

White light interferometry by

automatic and varying focusing

Crater depth

measurement

Inserts Difficult to measure grooved

inserts

Dawson and

Kurfess

[22]

White light

interferometer

Volume reduction measurement

of

tool

from

fusion

of

CAD

model

and surface profile

Crater depth

measurement


inserts

Wang et al. [130] LCD projector for

fringe creation on

rake surface

3D reconstruction using phase

shifting method from 4 fringe

patterns with 4 phase shifting

angle

4 parameters of

crater wear

measurement


inserts



11/21


12/21

tried tomonitor the condition of a sharp, a semi-dull and adull tool

by this technique. However, they did not analyze the error of

prediction. Kassim et al. [64] introduced a procedure to define

edges of surface texture obtained from turning, end milling and

face milling operation by connectivity oriented fast Hough

transform parameters like spread of orientation, average line

length, main texture orientation and total fitting error. This

connectivity oriented fast Hough transform process was faster and

less computationally complex than standard Hough transform

technique which was used to analyze the uniformity of surface

textures obtained from sharp and dull tools. Then the tool wear

was then predicted by using a MLPNN where inputs were taken

from the parameters of processed images. However, they did not

get any correlation for image number 35. Kassim et al. [63] also

showed that run length statistics technique for the detection of

surface textures machined by sharp tool and dull tool was faster

and better than column projection technique and connectivity

oriented fast Hough transform technique. Column projection

analysis technique was working well for highly regular surfaces

whereas Hough transform technique was extracting line segments

for variety of length. With the features extracted from run length

matrix, they classified the sharp tool and dull tool by applying

Mahalanobis distance classifier. Also they compensated inhomo-

geneous illumination of the texture images through an excellentway. However, they did not get any systematic trend of variation

between image textureparameters andmachining time. The image

descriptors were not normalized and no correlation study of image

texture descriptors with progressive tool wear or surface

roughness has been indicated in their work.

In a very recent study, Datta et al. [21] captured the turned

surface images for progressive wear of a uncoated carbide tool and

analyzed those images using a grey level co-occurrence matrix

(GLCM) technique based texture analysis. They also find a linear

correlation between the extracted features, namely, contrast and

homogeneity with the tool wear in terms of slope of the linear fit

and a fitting parameter, coefficient of determination. It has also

been observed from their study that the selection of GLCM

parameters viz. pixel pair spacing and direction is very muchimportant to get the accurate results as the distribution of feed

marks are varyingwith the variation of machining conditions (feed

rate and depth of cut). However, they did not mention about any

method to find the optimum pixel pair distance. As an improve-

ment of theprevious technique, Dutta et al. [31]has beenproposed

a novel technique to find the optimum pixel pair spacing

parameter to get an accurate resultby textureanalysis ofmachined

surfaces with the progressive tool wear. They got a periodic

relation of extracted texture descriptors viz. contrast and

homogeneity with the different pixel pair spacing. Utilizing this

periodic property, they found out the periodicity using Fourier

power spectral density technique and later on they found the

optimum pixel pair spacing parameter of GLCM. However,

the

optimum

pixel

pair

spacing

is

also

varying

dependent

onthe change of feed rate. They got a very good correlation of

extracted descriptors with tool wear and surface roughness.

However, they did not do any experiment to detect the progressive

tool wear of coated carbide tools.

Fractal analysis of surface texture for tool wear monitoring was

proposed by Kassim et al. [66] to deal with high directionality and

self-affinity of end-milled surfaces and a hidden Markov model

(HMM) was used to differentiate the states of tool wear.

Anisotropic nature of end-milled and turned surface textures

was analyzed by fractal analysis along different directions to the

entire image by Kassim et al. [65]. They used a 13-element feature

vector to train the HMMmodel for classifying fourdistinct states of

tool condition. However, no estimation of classification error has

been

encountered

in

their

study.

Kang

et

al.

[60]

used

a

fractal

analysis technique to study the variation of fractal dimension with

measured surface roughness, wear values with machining time for

different feed combination for high-speed end milling of high-

hardened material by a coated carbide tool. However, no

quantitative analysis of correlation of fractal dimension with

flank wear or surface roughness was done.

Persson [98] established a non-contact method to measure the

surface roughness by incorporating angular speckle correlation

technique. A speckle pattern created on the machined surface with

the help of a coherent HeNe laser and captured at different angle

of illumination. Then a correlationbetween those captured speckle

pattern at different angle of illumination has been calculated. The

lower correlation value has been observed for rougher surfaces.

Though this technique canbeused for the in-process measurement

of surface roughness but the accuracy of this method is limited by

the proper angular positioning of the set-up. However, this

limitation can be overcome by using a laser interferometric

technique for tilt measurement of the set up.

With a different approach, Li et al. [79] has been introduced an

waveletpacket analysis of machined surface images obtained from

turning operation. They got a good correlation between the

extracted feature, namely,high frequency energy distribution ratio

with progressive cutting tool wear. However, a systematic

quantitative correlation analyses was missing in their study.

5.2.

Offline

techniques

Luk and Huynh [85] analyzed the grey level histogram of the

machined surface image to characterize surface roughness. They

found the ratio of the spread and the mean value of thedistribution

to be a nonlinear, increasing function of Ra. Since their method was

based solely on the grey level histogram, it was sensitive to the

uniformity and degree of illumination present. In addition, no

information regarding the spatial distribution of periodic features

could be obtained from the grey level histogram. Hoy and Yu [45]

adopted the algorithm of Luk and Huynh [85] to characterize the

surface quality of turned and milled specimens. They found one

exception where the ratio of the spread and the mean of the grey-level distribution was not a strictly increasing function of surface

roughness and, therefore, the value of the ratio might lead to

incorrect measurement. They also addressed the possibility of

using the Fourier transform (FT) to characterize surface roughness

in the frequency domain. However, only simple visualjudgement

of surface images in the frequency plane was discussed. No

quantitative description of FT features for the measurement of

surface roughness was proposed. Al-kindi et al. [7] examined the

use of a digital image system in the assessment of surface quality.

The measure of surface roughness was based on spacing between

grey level peaks and the number of grey level peaks per unit length

of a scanned line in the grey level image. This 1D based technique

did not fully utilize the 2D information of the surface image, and is

sensitive

to

choice

of

lay

direction,

lighting

and

noise.

Cuthbert

andHuynh [20] increased the sophistication of the analysisby applying

a statistical texture analysis on the optical Fourier transform

pattern createdon the ground surface images.Then they calculated

the mean, standard deviation, skewness, kurtosis, and root mean

square height of the grey level histogram of the image. There were

two limitations of this technique. Only surfaces upto an average

surface roughness of 0.4 mm could be inspected, as rougher

surfaces tend to createadiffusedpattern in the camera. Precise and

complex alignment of the imaging optics was required, thereby

making it difficult to the use in online inspection.Jetley and Selven

[54] used the projection of a reflection pattern of a beam of low

power (1 mW) HeNe laser light from ground surface. Then the

pattern was analyzed and characterized using blob area, thresh-

olding

and

hence

correlated

to

the

surface

roughness.

But

the



13/21


14/21

surface images using the GLCM texture descriptors. However, they

have not optimized any of the GLCM parameters. Also they have

only tested this method for milled surface images only. Gadel-

mawla [39] predicted average surface roughness (Ra) values from

the texture descriptors extracted from the GLCM of turned surface

images with only a single combination of GLCM parameters for

different machining conditions. The error between the measured

Ra value by stylus method and the predicted Ra value is 7%.

However, the distance parameter of GLCM could be optimized for

getting

more

accurate

and

precise

result.

Myshkin et al. [89] introduced a special type of co-occurrence

technique with the concept of multi-level roughness analysis

to determine the surface roughness for nanometer scale

deviations obtained from the atomic force microscope (AFM)

images. However, no quantitative analysis has been done in their

study.

Tsai et al. [123] investigated Fourier power spectrum of shaped

and milled surface images with various maximum surface

roughness. The maximum surface roughness values were mea-

sured using a stylus-based surface profiler. They found image of

the surface patterns of the shaped specimens were more regular

and present less noise than those of the milled specimens. They

further found a monotonically decreasing trends for feature major

peak frequency, principal component magnitude squared, centralpower spectrum percentage and monotonically increasing trends

for average power spectrum with increasing values of measured

surface roughness for both the shaped and milled parts. Further-

more they used two artificial neural network (ANN) techniques for

classification of roughness features in fixed and arbitrary orienta-

tions of surfaces. Then they selected major peak frequency as the

best feature for both shaped and milled specimen in fixed

orientation, because, it was the distance between the major peak

and the origin, so it was a robust measure to overcome the effect of

lighting of the environment. However, they only did the surface

finish measurement for flat surfaces not for curved parts. Tsai and

Wu [124] used a Gabor filter-based technique for an automated

classification of defective and non-defective surfaces from the

surface images. They convolved the image with a 2D Gaborfunction, which is an oriented complex sinusoidal grating

modulated by a 2D Gaussian function. Then they have selected

the best parameter of the Gabor function, such that the energy of

the convolved image was zero, using exhaustive search method.

Then a threshold value has been chosen using statistical control

method for distinguishing the homogeneous and non-homoge-

neous surface texture. However, a very accurate controlled set-up

for capturing the surface images are required for practical

accomplishment of their method. Dhanasekar et al. [25] captured

speckle patterns of machined surfaces (ground and milled) using a

collimated laser beam (HeNe laser, 10 mW, l = 532 nm) and a

CCD camera. Then, pre-processing of speckle images was carried

out to remove unwanted intensity variations due to ambient

lighting

change,

etc.

The

speckle

images

were

filtered

by

Butter-worth filter and then the centralized fast Fourier transform (FFT)

was determined. After that average and integrated peak spectral

intensity coefficient and autocorrelation coefficient in X, Y and

diagonal directionswere determined. The width of autocorrelation

functions for smooth and rough images were varied. The spectral

speckle correlation (auto-correlation) technique for surface

roughness assessment had been used before and after pre-

processing of speckle images. They were then compared to stylus

values (Ra). It was found that autocorrelation parameters after pre-

processing had a better correlation (i.e. higher correlation

coefficient) with the average surface roughness (Ra) measured

for themilledand ground components. To getmore accurate result,

image model for compensating inhomogeneous illumination [14]

could

be

used

in

their

work.

Josso et al. [57] analyzed and classified eight surface images

obtained from eight types of engineering processes viz. casting,

grinding, gritblasting, hand filing, horizontal milling, linishing,

shotblasting, vertical milling. They have developed a space-

frequency representation of surface texture using frequency

normalized wavelet transform (FNWT) and extracted some surface

finishdescriptors. Then they classified those eight types of surfaces

using discriminant and cluster analysis approach. However, there

is a high chance of misclassification between similar types of

texture viz. milling and grinding. So, they compared continuous

wavelet transform (CWT), standard and scaled discrete wavelet

transform (DWT) methods and concluded that the standard

discrete wavelet transform associated with cluster analysis was

the best method for classification purpose. In their another work

[55], they tried to measure the form, waviness and roughness of

machined surfaces images by using FNWT. Niola et al. [94] tried to

reduce the problem of brightness variation on surface images at

different lighting condition by enhancing images of machined,

ground and polished surfaces using Haar wavelet transform.

However, no surface finish descriptors were extracted from the

surface images, in their study.

Ramana and Ramamoorthy [104] classified ground, milled and

shaped images based on GLCM, amplitude varying rate approach

and run length statistical technique. However, they did not decideabout the best feature for vision-based surface roughness

measurement. Also they did not do any quantitative correlation

study between vision based and stylus based surface roughness.

Bradley and Wong [16] presented the performance of three image-

processing algorithms, namely, analysis of the intensityhistogram,

image frequency domain analysis and spatial domain surface

texture analysis for evaluating the tool condition from face milled

surface images. Though, the histogram based technique revealed a

proper trend for the progressivewear of face milling tool but itwas

very much influenced by the lighting condition. Frequency domain

technique was much less sensitive to inhomogeneous illumination

than the histogram based approach. The major advantage of a

texture-based method was the dependence on localized similari-

ties in the image structure. The absolute value of illuminationintensity was not critical; the illumination must be sufficient to

highlight image features. Similarly, the method was not sensitive

to the angle of illumination, except for extreme cases where the

axis of illumination approached 908. They showed a systematic

variation of texture parameters with machining time. However, no

quantitative correlation has been reported by them. Zhang et al.

[142] developed an accurate defect detection and classification

system by extracting the best features from discrete cosine

transform (DCT), Laws filter bank, Gabor filter bank, GLCM. They

used support vector machine (SVM) and RBFNN for classification

purpose. They have got a 82% success using the combination of

Gabor filter, GLCM and SVM. Singh and Mishra [115] classified

different types of spangles obtained due to the galvanization of

steel

sheets

using

GLCM

and

Laws

texture

descriptors

with

RBFNN.They achieved 80% accuracy of classification. Their approach can

also be used for progressive wear monitoring. Alegre et al. [4] used

first order statistical texture analysis, GLCM method and Laws

method to evaluate turned surface images and classified two

roughness classes using k-NN technique. Best result was obtained

by using Laws method, in their study. In a different approach,

Bamberger et al. [12] compared three methods for examining the

chatter marks produced at the time of machining in valve seat of

automotive parts from the images of the valve seats. They

compared three image processing based techniques, namely, circle

fitting, circularity and GLCM method to classify accepted and

rejected parts. Though, they selected the appropriate distance

parameter of GLCM, manually, but it is needed to develop an

automatic

method

for

detection

of

optimized

distance

parameter.



15/21

Table 2

Indirect TCM techniques based on image processing.

Researcher Illumination system Image processing algorithm Applied in Remarks

Wong et al. [134] HeNe laser Mean and standard deviation of

laser pattern created on machined

surface

Turning Offline; no study on correlation

and progressive wear

Gupta

and

Raman

[42]

HeNe

laser,

circular

variable attenuator

Histogram

based

1st

order

statistical texture analysis

Turning

(moving

and

static condition); surface

roughness measurement

Online;

no

correlation

study

between vision-based surface

roughness and stylus-based

surface

roughness

andprogressive tool wear; no

discussion about blurring due to

movement

Tarng and Lee [119] 2 Light sources

situated at an acute

angle with the axis of

workpiece

Determination of Ga, polynomial

network with self organized

adaptive learning (feed, speed,

depth

of

cut

and Gaas input, Raas

output)

Turning; Raprediction Online; prediction error

(max)=14%; extraction of 1

descriptor only; no prediction of

tool

wear

Ho et al. [44] 2 Light sources

situated

at

an

acute

angle with the axis of

workpiece

Determination of Ga, ANFIS (feed,

speed,

depth

of

cut

and Gaas input,

Raas output)

Turning, Raprediction Online; prediction of Rausing

ANFIS

prediction

error

(max)=4.55%; extraction of 1

descriptor only; no prediction of

tool wear

Lee et al. [78] A diffused blue light in

458 inclination

Standarddeviation of grey level, two

frequency domain parameters and

abductive network (input as 3

texture descriptors, output as Ra)

Turning, Raprediction Online; max prediction

error=14.96%; no prediction of

tool wear

Lee et al. [79] A diffused blue light in

458 inclination

Standarddeviation of grey level, two

frequency domain parameters and

ANFIS

(input

as

3

texture

descriptors, output as Ra)

Turning, Raprediction Online; max prediction

error=8%; no prediction of tool

wear

Akbari et al. [3] Scattered pattern of

light

Histogram based 1st order

statistical texture analysis (four

descriptors) & MLPNN

Milling, Raprediction Online; No quantification of

prediction error; No prediction

of tool wear

Narayanan et al. [91] An evolvable hardware Image enhancement, determination

of Ga, genetic algorithm

Milling; Surface

roughness measure

Online; no quantification of

prediction error; no prediction of

tool wear

Sarma et al. [110] Determination of Ga, frequency

domain analysis

Turning GFRP composite

with PCD tool

No study for progressive wear

monitoring

Palani

and

Natarajan

[97]

Frequency

and

spatial

domain

based

texture analysis, BPNN

End

milling, Raprediction No study for progressive wear

monitoring

Kassim et al. [67] Sobel operation, thresholding,

column

projection

(CP)

(applied

on

thresholded images), run-length

statistics (RLS) (applied on greylevel images)

Turning; progressive wear

monitoring

Online; Progressive wear

monitoring;

classification

between sharp tool and dull tool

in various machining; nocorrelation study with Ra

Mannan et al. [87] Sobel operation, thresholding, CP,

RLS, extraction of AE parameters

using wavelet analysis, RBFNN for

flank

wear

prediction


monitoring

Online; monitor sharp, semi-dull

and dull tool; no quantification

of prediction error

Kassim et al. [64] Canny edge detection, connectivity

oriented fast Hough transform,

MLPNN

for

FW

prediction

Turning, end milling, face

milling; progressive wear

monitoring

Online; no quantification and

comparison of prediction error

Kassim et al. [63] Compensating inhomogeneous

illumination compensation,

comparison of CP, connectivity

oriented fast Hough transform and

RLS, Mahalanobis distance classifier

for classification of sharp and dull

tool


monitoring and

classification

Online; RLS was selected as the

best technique depending only

on a single cutting condition;

classification between two wear

state only; more

experimentation needed

Datta et al. [21] Diffused light GLCM technique Turning; progressive wear

monitoring

Online; extraction of best feature

depending

only

on

a

singlecutting

condition;

No

optimization of GLCM

parameters

Datta

et

al.

[31]

Diffused

light

GLCM

technique

with

optimized

pixel pair spacing (pps) parameter

Turning;

Progressive

wear

monitoring

Online;

Optimization

of

pps

developed; applicable for any

periodic textures; no study to

monitor coated carbide tool

Kassim et al. [66] Fractal with HMM End milling; Classification Online; No estimation of

classification error

Kassim et al. [65] 3D fractal with HMM End milling; classification

of 4

states

of

wear

Online; no estimation of

classification

error

Kang et al. [60] Fractal; progressive variation study

with surface roughness and tool

wear

High speed end milling

(with coated carbide)

Online; no estimation of

correlation parameter

Li et al. [81] Diffused light Wavelet packet decomposition Turning; progressive wear

monitoring

Online; no correlation analysis

with tool wear



16/21



Hoy and Yu [45] Diffused white light Histogram analysis, 2D FFT analysis Turning, milling Offline; no progressive wear

monitoring

Cuthbert and Hynh [20] HeNe laser, spatial

filter, beam splitter

and mirror

Histogram based 1st order

statistical texture analysis

Grinding Offline; complex attenuator;

difficult to implement for high

roughness values; no

progressive wear monitoring

Jetley and Selven [54] HeNe laser Blob analysis, thresholding Grinding Offline; no progressive wear

monitoring

Ramamoorthy andRadhakrisnan [103]

GLCM analysis Grinding, shaping, milling Offline;no correlationparameterstudy

Kiran et al. [71] Diffused light; light

sectioning; phase

shifting with grating

projection

Frame averaging; low pass filtering;

2nd order co-occurrence statistics;

three lighting methods were

compared for rough, medium rough

and smooth images

Grinding, milling, shaping Offline; mainly the comparison

of three types of lighting; no

roughness evaluation

Younis [140] White light Neighbourhood processing Grinding (different

material)

Offline; coefficient of variation

8.6%

Coefficient of determination (R2)

0.790.92; no progressive wear

study

Kumar et al. [72] Cubic convolution interpolation,

linear edge crispening,

Determination of Ga

Shaping, milling, grinding Offline; no progressive wear

monitoring

Khalifa

et

al.

[69]

Edge

enhancement,

magnification,

statistical texture analysis (1st and

2nd

order),

calculation

of

Gavalue

Chatter

detection

in

turning

Discrimination

between

chatter-

rich and chatter-free process

from

surface

imagesAl-kindi

and

Shirinzadeh [8]

Ambient

light

Comparison

between

two

lighting

models viz. intensity topography

compatibility and light diffused

model, extraction of optical surface

roughness parameters from 1st

order statistics

Face

milling

No

correlation

study

with

progressive wear

Elango and

Karunamoorthy [32]

Diffused light at

different grazing angle

Determination of Ga, Taguchis

orthogonal array and ANOVA

Face turning No correlation study with

progressive wear

Dhanasekar and

Ramamoorthy [28]

White light POCS for reconstruction of high

resolution image, frequency domain

and

histogram

based

texture

analysis, GMDH

Milling, grinding (Raprediction)

No prediction error analysis, no

correlation study with

progressive

wear

Zhongxiang et al. [143] Stereo zoom

microscope, halogen

lamp

Median filtering, histogram

conversion, histogram

homogenization, calculation of 3D

roughness

Paning, plain milling, end

milling, grinding

No correlation study with

progressive wear

Dhanasekar and

Ramamoorthy [26]

RichardsonLucy algorithm for

deblurring, frequency and spatial

domain based texture analysis, ANN

Milling, grinding, Raprediction

Correlation coefficient 0.923 and

0.841 for milling and grinding,


progressive wear

Gadelmawla[36]

Microscope

GLCM,

study

the

effect

of

pps

Face

turning

No

optimization

ofpps value, No


progressive wear

Gadelmawla

et al. [37,38]

Microscope

GLCM

Milling,

Reverse

engineering for cutting

conditions

No

optimization

ofpps value, No


progressive wear

Gadelmawla [39] Microscope GLCM Face turning, Correlation

with Ra

No optimization ofpps value, No


progressive wear

Tsai et al. [123] Fluorescent light

source

Fourier analysis, ANN Shaping, Milling No correlation study with

progressive wear

Tsai and Wu [124] Gabor filtering, classification of

defective and non-defective parts,

Milling No mention of success rate; no

progressive wear or surface

roughness

study

Dhanasekar et al. [25] HeNe laser Speckle pattern, Butterworthfiltering, Fourier analysis,

Autocorrelation

Grinding, milling No correlation study withprogressive wear

Josso et al. [57] Frequency normalized wavelet

transform, discriminant and cluster

analysis

Classification of ground,

milled, cast surfaces etc.


progressive wear

Josso et al. [56] Frequency normalized wavelet

transform,

Form, waviness,

roughness measurement


progressive wear

Niola et al. [94] Haar wavelet for reduction of

inhomogeneous

illumination

Milling, grinding,

polishing

No extraction of surface finish

parameters

Raman

andRamamoorthy [104]

GLCM, amplitude varying rate

method, RLS

Classification of ground,

milled, shaped surfaces

No correlation study with stylus

based surface roughness; no

progressive

wear

study



17/21



Bradley and

Wong [16]

Fibre optic guided light

(regulated)

Frame averaging, Gaussian filtering,

median filtering, after filtering:

image histogram analysis,

frequency domain analysis, texture

segmentation

Face milling, progressive

wear study

Comparison between histogram

analysis, frequency domain

analysis and texture

segmentation; no correlation

analysis of vision-based surface

finish with tool wear

Zhang et al. [142] DCT, Laws filter, Gabor filter, GLCM,

Shape features, SVM with RBFNN

kernel

Defect detection and

classification in ground

and polished surfaces

82% success rate using the

combination of Gabor filter and

GLCM with SVMAlegre et al. [4] DC regulated lightwith

SCDI

First order statistical texture

analysis, GLCM, Laws method, k-NN

classification

Turning No progressive wear study

Nakao [90] Fibre optic light Thesholding, component labelling Drilling burr

measurement

3% and 2% error in measuring

burr thickness and height

Yoon and

Chung [139]

Halogen (front light)

LED (back light)

Edge detection (burr width

measurement), Shape from focus

(burr height measurement)

Micro-drilling 0.1mm resolution; less than

0.5mm accuracy

Sharan and

Onwubolu [114]

High intensity spot

lighting

Burr profile measurement Milling 2.2mm resolution

Fig. 3. Flow diagram of proposed tool condition monitoring technique using digital image processing.



18/21

Ikonen and Toivanen [46] proposed an algorithm that gave

priority to a pixel in the tail so as to calculate the minimum

distance in a curved space so that it helped in calculating the

roughness in a faster and more efficient manner.

Vesselenyi et al. [126] utilized 2D box counting method and

found nine parameters as roughness descriptor by linear, second

order and third order polynomial fitting on shaped, ground and

polished surface images of different surface roughness. Then

they classified them using C-means clustering. However, more

number of sampleswere required to proof the suitability of their

method.

Quality of honed surfaces was also determined by Leon et al.

[80] using image processing technique. He quantified the groove

textures anddefects of honed cylinder bore in frequency domain.

In frequency domain, the groove texture of interest was

separated from the other defects such as groove interrupts,

holes, cracks, flakes, material defects, graphite lamellae, material

smearings, smudgy groove edges and foreign bodies. The images

were taken from fax film replicas of honed surfaces. The images

were enhanced by contrast stretching. Digital image processing

was also used in chatter identification and burr detection in

machining.

Nakao [90] captured images of drilling burrs and then

processed to monitor drilling process. Here the conventionalimage processing techniques such as the binary image proces-

sing, the noise reduction and the labelling were applied to

measure image data. Here burr height and thickness were

measured fromthe processed image usingco-ordinate data. Yoon

et al. [139]usededge detection algorithm tomeasurehole quality

and burr width in micro-drilled holes. They also measured burr

height with Shape From Focus (SFF) method. Here a halogen

light was used as a front light and LED was used as a backlight for

getting uniform illumination. Sharan and Onwubolu [114]

measured the burr profile of milled parts with 2.2 mm system

resolution.

In most of the research, the variation of vision based surface

texture descriptors with machining time were not studied for

progressive wear monitoring. Also there is a requirement tonormalize the texture or wear descriptors for reducing the effects

of lighting variations. Research in this area is requiring a detailed

study with various work tool material combination with various

cutting parameters for differentmachining application to establish

a robust monitoring system.

The indirect tool condition monitoring techniques, using image

processing are summarized in Table 2.

6. Conclusions

In this paper, the application of image processing technology

applied for tool condition monitoring is discussed. For real time

tool condition monitoring with noncontact techniques, the image

processing

algorithms

can

be

used

for

enhancing

the

automationcapability in unmanned machining centres.

The digital image processing techniques are very useful for fast

and easier automatic detection of various types of tool wear (such

as crater wear, tool chipping and tool fracture) which are very

difficult to recognize by other modes. Textural analysis techniques

are playing a predominant role for tool condition monitoring via

assessment of ma

tool monitoring using image processing

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