ELSEVIER Computers in Industry 34 (1997) 55-72

A review of machine vision sensors for tool condition monitoring

S. Kurada * , C. Bradley D(?partment of Mechanical Engineering, Uniuersity of Victoria, Victoria, BC, Canada, V8W 3P6

Received 4 January 1994; revised 27 August 1996; accepted 27 August 1996

Abstract

Tool condition monitoring has gained considerable importance in the manufacturing industry over the preceding two decades, as it significantly influences the process economy and the machined part quality. Recent advances in the field of image processing technology have led to the development of various in-cycle vision sensors that can provide a direct and indirect estimate of the tool condition. These sensors are characterised by their measurement flexibility, high spatial resolution and good accuracy. This paper provides a review of the basic principle, the instrumentation and the various processing schemes involved in the development of these sensors. 0 1997 Elsevier Science B.V.

Keywords: Machine vision; Manufacturing information; Cutting tool monitoring; Flank wear

1. Introduction

The concept of tool condition monitoring has gained considerable importance in the manufacturing

industry. This is mainly attributed to the transfonna- tion of the manufacturing environment from manu-

ally operated production machines to CNC machine tools and the highly automated CNC machining cen- tres. For modem machine tools, 20% of the down-

time is attributed to tool failure, resulting in reduced productivity and economic losses. A reliable moni- toring system could prevent these problems and al- low optimum utilisation of the tool life, which is highly desirable.

The current trend is for CNC machine tools to be tended by operators, who are not fully equipped with the blend of training and experience necessary to gauge a tool’s wear. A skilled machinist will pay

* Corresponding author.

close attention to cutting tool performance particu- larly when a new combination of tool, material and part program parameters are being tried. However,

the recent trend towards unsupervised machining centres equipped with open architecture controllers has changed the manufacturing environment signifi- cantly. In this environment, operators will not be available to make tool changing decisions. Also, the pre-planned tool replacement strategies are no longer appropriate as the machining conditions vary consid- erably. Thus, there is a great demand for monitoring systems that ensure optimum performance of the unsupervised machining centres.

In addition to the complexity of the metal cutting operation, the various combinations of the operating conditions, tooling and the materials, increases the probability of the machine tool breakdown. Although several models [l-5] have been developed to predict cutting tool life, none of these are universally suc- cessful due to the complex nature of the machining

0166-3615/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved. PII SO166-3615(96)00075-9

56 S. Kurada, C. Bradley/ Computers in Industrv 34 (I997) 55-72

process. Various studies [6,7] have pointed out the importance of sensing technology in the develop- ment of flexible manufacturing systems.

Sensors play a vital role in the acquisition of

information relating to the machine, process and part to optimise the machine tool performance. In the case of unsupervised machining centres, it has been demonstrated that the addition of sensor capabilities can dramatically reduce down time and improve product quality [8]. The deployment of these sensors for tool condition monitoring can be categorised as either in-process or in-cycle. An in-process sensor monitors the tool condition during the machining operation, whereas an in-cycle sensor examines the tool periodically, for example between machining blocks or during part changeovers.

Over the years, a number of sensors have been developed, and most of these have been limited for use in a laboratory environment. However, recent

advances in the field of image processing technology have led to the development of various in-cycle vision sensors that can be used to obtain information about the cutting tool as well as the machined part. The relative speed and absence of any physical contact with the tool makes on-line monitoring feasi-

ble, provided the tool is not in permanent contact with the workpiece. As vision and artificial intelli-

gence (AI) are natural partners, integration of the two technologies is also possible, to provide a better understanding of the tool wear problem. The poten- tial of these techniques for tool condition monitoring is unlimited and hence will be explored in this review paper.

Various techniques for tool wear monitoring were reviewed by Shiraishi [9- 111, Lister and Barrow [ 121 and Martin et al. [ 131. A survey of the general techniques used for surface roughness measurement are reviewed by Vorburger and Teague [14] and Thomas [15]. The main thrust of this review paper is

to include the development of vision sensors for cutting tool and the workpiece assessment. Accord- ingly, the paper is organised as follows: First, an introduction to the background information in tool condition monitoring is presented. Next, a descrip- tion of the machine vision components is presented, followed by a review of the literature in cutting tool and workpiece quality inspection. Sensor fusion is discussed next followed by the concluding remarks.

2. Tool condition monitoring - The background

The life of a cutting tool can be brought to an end either due to gradual wear leading to tool failure or

premature edge failure due to chipping. As the cut- ting tool approaches the end of its life, the degrada- tion in surface quality of the machined workpiece is quite evident. Characterisation of the surface topog- raphy of a machined workpiece can act as a finger- print of the machining process and, more specifi- cally, the condition of the cutting tool. This is at- tributed to the change in the textural characteristics of the machined workpiece becoming apparent as the cutting tool approaches the end of its life. Hence, the tool wear sensors can detect the signal either directly from the tool or indirectly from the workpiece. The

following sections describe the direct and indirect aspects of tool condition monitoring.

2.1. Tool life criteria

There are two predominant wear mechanisms that limit a tool’s useful life; flank wear and crater wear. Flank wear occurs on the relief face of the tool and is mainly attributed to the rubbing action of the tool on the machined surface and the high temperatures developed. Crater wear occurs on the rake face of the tool and changes the chip-tool interface, thus affect-

ing the cutting process. The most significant factors influencing crater wear are temperature at the tool- chip interface and the chemical affinity between the tool and workpiece materials [16].

Chipping is the term used to describe the breaking away of a small piece from the cutting edge of the tool. Unlike wear, which is a gradual process, chip- ping results in a sudden loss of tool material and shape, and has detrimental effect on the surface finish. It is mainly attributed to the mechanical shock due to interrupted cutting and thermal fatigue.

A typical flank wear profile is divided into three regions (see Fig. 1):

Zone C - Nose or trailing groove, which forms near the relief face and contributes significantly to surface roughness. Zone B - A plateau consisting of uniform wear land. Zone A - Leading edge groove, which marks the outer end of the wear land.

S. Kuraah, C. Bradley / Computers in Industry 34 f 1997) 55-72 57

Fig. 1. Illustration of the form of a typical flank wear pattern.

According to Imemational Standards Organisation (ISO), tool life criteria are concerned only with the leading edge groove in zone A. If the profile is uniform, the tool can be used unless the average

value VB is greater than 0.3 mm. For uneven wear, maximum wear land width (VBmax) should be less than 0.6 mm. A tylpical crater wear profile is shown

in Fig. 2. The extent of cratering is specified by the maximum depth of the crater from the original rake face K,. In some cases, its size is specified by K,

and K,.

2.2. Tool wear sen,pors

The requirements for tool wear sensors to be successful in a machining environment include:

good correlation between the sensor signal and

the tool condition; the response should be fast enough for feedback

control; simple in design and rugged in construction; non-contact, accurate and reliable; no interference with the machining process.

Over the years, a number of sensing techniques have been developed. Most of these systems have been limited to the clean room environment, and only a few have emerged as viable tools for in-pro- cess measurement. Generally, these techniques are

Fig. 2. Illustration of the form of a typical crater wear pattern.

I Direct Indirect

Fig. 3. Classification of previously tested tool wear sensing tech-

niques.

classified into two categories (see Fig. 3): direct sensors and indirect senors.

2.2.1. Direct sensors

The measurement of the actual dimensions of the worn area on the tool and/or direct determination of

the condition of the tool’s cutting edge. These meth- ods have the advantage of providing a direct and accurate assessment of the tool’s state but are limited to in-cycle deployment. The most common direct sensing techniques are:

Proximity sensors. Proximity sensors estimate tool wear by measuring the change in the distance be- tween the tool’s edge and the workpiece [17,18].

This distance can be measured by electric feeler micrometers and pneumatic touch probes. The mea- surement is affected by the thermal expansion of the

tool, deflection or vibration of the workpiece and the deflection the cutting tool due to the cutting force.

Radioactive sensors. Radioactive sensors [ 19,201 have been used for direct measurement of tool wear. A small amount of radioactive material is implanted on the flank face of the cutting tool. During the cutting process, worn tool material is transferred to the chips. By monitoring the amount of radioactive material deposited on the chips, tool wear can be

assessed. The need for collecting chips on-line and the hazardous nature of radioactive material limits this technique for laboratory environment.

Vision sensors. The direct application of the vi- sion sensor for measurement of tool wear utilises the cutting tool itself. In general, these sensors depend on the higher reflective properties of the wear land,

58 S.

compared to the unworn surface, to derive various morphological parameters that characterise tool wear. The majority of the research work has pursued only the measurement of flank wear whereas few re-

searchers have attempted to measure both flank and crater wear. Flank wear regions can be imaged with a CCD camera, however, crater wear determination requires the projection of a structured light pattern onto the tool, in order to derive depth information from within the crater. In structured light sensing, the distortion of parallel lines of laser light gives a measure of crater depth. Due to the hostility of the cutting environment (presence of lubricant, built-up-

edge or metal deposits on the cutting tool), current vision sensors can only be used between cutting

cycles.

2.2.2. Indirect sensors

This approach measures a parameter that can be correlated with tool condition. Although these pa- rameters can be measured, they are often influenced by non-wear phenomena leading to an erroneous prediction of tool life. Indirect tool wear sensors can generally be deployed in-process. The most com- monly used indirect sensing techniques are:

Cutting force. Cutting force signals have been extensively used for condition monitoring due to the availability of sensor technology 121-231. A dy-

namometer is mounted on a tool holder to monitor the cutting force in 1 or 2 orthogonal directions. The force sensor signal indicates the increase in the cutting force required as a progressively wearing tool is forced through the material. Therefore, signal analysis has to be performed on the force data in order to determine when the tool has to be replaced. This signal analysis problem is complicated by pa- rameters, other than tool wear, affecting the cutting force. For example, material properties (density, hardness, ductility), cutting tool geometry, chip

build-up on tool edge, etc. This makes it difficult to develop a robust and reliable force sensor that can predict tool wear.

Vibration. Machining with a worn tool increases the fluctuation of forces on the cutting tool. This is attributed to the friction between the flank face of the cutting tool and the workpiece, and also the internal fractures of the tool. Due to these force fluctuations, vibrations occur in the system. There-

fore, by monitoring the level of vibration, tool wear can be assessed 124,251. The sensing device consists of a piezo-electric accelerometer attached to the up- per surface of the cutting tool, as close as possible to

the cutting edge. The output of the sensor is com- pared to a reference threshold, and if the threshold is exceeded repeatedly, failure is predicted. If the sen- sor is mounted close to the cutting location, the variability of the signal increases with the progres- sion of the cutting process. Also, the amplitude of the signal decreases with an increase in the distance between the sensor and the cutting edge.

Acoustic emission. Acoustic emission (AE) is de- fined as the “transient elastic energy spontaneously released in materials undergoing deformation, frac-

ture or both” [26]. The emission signal is usually detected by a contacting piezo-electric transducer mounted on the machine tool. The acoustic signal information must be carefully analysed to separate the cutting signal from other signals present in the spectrum. This requires, in addition, to the sensor, signal amplifiers, filters and processing electronics. Furthermore, sensor location on the machine tool is problematic; different machine tools have different characteristics that need to be considered when pro- cessing the AE data.

The sensors described above are not mutually exclusive; by adopting techniques from the AI com- munity and employing multiple sensors, some of the problems described above have been minimised. Fildes [27] provides an overview of the sensor fusion techniques available to monitor at the part, tool, and machine level. Sensor fusion is a philosophy whereby several data modes, indirectly measuring the same phenomenon, are combined to increase prediction reliability. Rangwala and Domfeld [28] applied neu- ral network techniques to the multi-sensor tool wear monitoring problem. Neural networks allow an auto-

matic learning capability so that some of the ma- chine dependency problems can be eliminated. Neu- ral networks also permit data from multiple sensors to be combined in order to use the maximum amount of information in a control decision. As more sensor-based data is utilised, the certainty of the derived tool wear parameters increases. In a similar manner, fuzzy systems have also been applied to the multi-sensor monitoring problem [29]. Du et al. 1301 reviewed these modes of machine monitoring and

S. Kurada, C. Bradley / Computers in Industry 34 (1997) 55-72 59

the use of AI techniques (such as neural networks and fuzzy logic) to achieve multi-sensor fusion. Clearly, by monitoring, and hence controlling, the best operating state of a machine tool maintains the

parts produced by that machine in optimum quality condition.

2.3. Surface texture

The accurate characterisation of surface texture involves the definition of parameters that quantify the surface topographical or geometrical features. It includes several features present in a part’s surface

profile; roughness, waviness, lay and flaws. Rough- ness is comprised of the randomly distributed surface irregularities, extending over the whole area, on a

microscopic scale. It is mainly attributed to the intrinsic action of ,the machining process. Waviness, characterised by height variations at a given spatial

frequency, is the more widely spaced component of surface texture. It results from such factors as ma- chine or work deflections, vibrations, and tool chat- ter. The lay of a surface is the direction of the predominant surfa’ce pattern and is usually deter- mined by the ma’chining method used. Flaws are unintentional irregularities, which occur at one loca- tion or at relatively infrequent intervals on the sur- face, arising from accidental damage either during or

after the surface generation. The parameters that are commonly used to characterise surface texture in- clude:

- Spatial parameters, derived directly from the sur- face profile, are commonly used to characterise textural properties of various machined surfaces. Amplitude parameters, R, (centre-line average) and R, (RMS average), are the most widely used and are essentially the same.

- Frequency parameters, derived by decomposing the surface profile into a number of periodic components of different wavelengths and ampli- tudes, are used to reveal more information about the machining process. The decomposition proce- dure is commonly carried out by using a discrete Fourier transformation.

The stylus profilometer has traditionally been used for surface roughness measurement in an industrial

U

\

Flank face J

Fig. 4. Schematic diagram of a computer vision-based tool wear

sensing system.

clean room. All the national and international stan-

dards are defined in terms of the measurements produced by this instrument. However, due to the direct physical contact required to produce the sur- face profile, it damages the surface (particularly soft materials). Also, line sampling of the data limits its repeatability and ability to accurately describe the surface characteristics.

3. Machine vision components

A vision-based tool condition monitoring system

consists of the major components discussed below (a schematic diagram is shown in Fig. 4).

3.1. Illumination

One of the most important parts of the hardware configuration is the illumination. The selection of an appropriate light source is dependent on the stand-off distances required, amount of light and the environ-

mental issues involved [31]. For cutting tool wear monitoring, the main requirement of the system is to provide adequate contrast between the worn region and the background. The intensity and the angle of the illumination source should be adjusted to accen- tuate the tool region of interest. The light sources that are commonly used for tool condition monitor- ing are incandescent lamps and lasers.

In addition to the selection of an appropriate light source, consideration must also be given to the tech- nique which will give the optimum results. The three

60 S. Kurada, C. Bradley / Computers in Industry 34 (I 997) 55-72

techniques that have been used extensively for vari-

ous machine vision applications include [31]:

Front lighting, which involves direct illumination of the object, has been widely used in tool wear and surface roughness measurement. Buck lighting provides excellent contrast, but lim-

ited to silhouette information. Structured lighting refers to the sources of illumi-

nation, where the geometric shape of the pro- jected light pattern is controlled by some means. Structured light sensing has been used to estimate the depth of the crater wear region.

3.2. Cameras

Two types of video cameras have been used for tool condition monitoring. The earlier studies em- ployed vidicon cameras, which consist of a photo- sensitive surface present inside a vacuum tube. When an electron beam scans this surface, analogue voltage proportional to the scene brightness (at that point in

the image) is produced. These cameras suffer from geometric distortions and image drift.

The emergence of cost effective and improved solid state technology over the past decade, has made CCD sensors readily available for various machine

vision applications. The basic structure of the CCD is that of an analogue shift register consisting of a series of closely spaced capacitors. Typical sensors offer a pixel resolution of 768 X 493 with imaging rates of 30 images/set. High resolution cameras offer sensor sizes up to 2048 X 2048 pixels, but at lower temporal resolution and at a very high cost. High speed cameras, with imaging rates of up to 1000 images/set, are limited by a reduced spatial resolution and high cost. The CCD cameras have a standard C-mount, which is compatible with a wide variety of lenses.

3.3. Image digitisation

Image digitisation converts an infinitely variable value (analogue signal) to an integer from 1 to N, where N represents degree of Gray scale recognised by the system. A wide range of frame grabbers are available for the A/D conversion of a standard video signal. Some of the recent frame grabbers are

Input Digital Image Lri

+

Check for Tool Breakage Breakage

No Breakage

Fig. 5. Typical sequence for determining cutting tool flank wear

parameters.

equipped with real time low level image pre-

processing.

3.4. Image analysis

The processing methodologies employed in evalu- ating the images for direct or indirect assessment of the tool condition are discussed in the following sections.

3.4.1. Tool wear

In principle, the steps involved in determining the cutting tool condition are shown in Fig. 5. During the monitoring process, the tool is positioned in front of the camera and can focus on its flank or crater face. A magnified image of the cutting tool clearly shows the three distinct textural regions (see Fig. 6):

* the worn region, which is characterised by a non-uniform texture;

. the background, comprising of the tool holder and the platform, is uniform and smoothly textured;

* the unworn region, which is distinguished by a uniform but coarser texture than the background.

S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72 61

Fig. 6. Photograph of a typical flank wear pattern on an insert.

The tool wear image can be affected by uneven lighting or random bright spots due to the specular

reflections off asperities on the tool surface. Position- ing of the illumination sources relative to the tool is crucial in minimising these effects, however, they cannot be totally eliminated. Therefore, the image has to be enhanced prior to the application of the segmentation operators. This involves either contrast stretching or a smoothing algorithm. A contrast stretching algorithm is comprised of a linear scaling transformation that normalises the actual intensity

values so that the:y are distributed over a wider intensity range. A smoothing algorithm is primarily used to remove the spurious effects present in the image. It involves either a lowpass filtering approach or cascaded median filtering.

A key step in evaluating flank wear is the delin- eation of the wear region pixels from the remainder of the image, which is composed of the background and the unworn tool region. Termed segmentation, a simple approach involves global thresholding of the image based on the Gray level histogram. Ideally, the bright worn region in a dark background yields a bimodal intensity histogram and an optimum thresh- old lies between the histogram peaks. However, due to non-uniform background illumination, the selec- tion of a threshold value from the histogram is extremely difficult. Worn tool region segmentation can be achieved through both edge operators (that directly define the worn region boundary) and tex- ture operators (that find regions of similar surface texture). An edge is a significant change in the local image intensity between, for example, the dark im- age background and the brighter region defining an

object surface. By locating the image pixels that are edges in an image, and linking them together, the boundaries of objects can be defined. As the gradient of a function is a measure of change, edge detectors utilise gradient techniques to locate edges. Recent work by Kurada and Bradley [32], comparing edge detection and texture-based operators, has shown the advantages of texture operators in tool wear assess- ment.

Texture operators have received much attention in the image processing literature for their ability to perform superior segmentation in certain applications

[33]. Texture operators transform pixels with similar texture into pixels that have a similar brightness, thereby allowing segmentation to be completed by

applying a simple brightness thresholding operation. The variance texture operator was used in [32] to successfully segment flank wear images acquired with a CCD camera. The variance of the brightness values in a 5 X 5 mask around each pixel of interest is computed as the sum of the squares of the differ- ences between the brightness of the central pixel and its neighbours. A typical image generated by the variance operator is shown in Fig. 7; the worn region has clearly been delineated and can be segmented from the remainder of the image through intensity

thresholding. Pixels, present within the wear region boundary,

must be collected into an identifiable morphological feature from which a set of useful tool wear parame- ters can be calculated. Clustering is performed on the binary image generated by texture segmentation. The algorithm proceeds from top to bottom, scanning left to right, and collects bright pixel run lengths. Run

Fig. 7. Photograph of a segmented flank wear image employing a

variance operator.

62 S. Kurada, C. Computers in 34 (1997)

lengths identified in the previous row are combined with the current run length and amalgamated into the same feature. The output of this operation are feature vectors; the largest area feature obtained is the wear region. However, the interiors still contain dark pix- els which must be filled prior to any feature mea- surement. A feature vector describing the perimeter of each region is generated, thereby allowing the interior dark pixels to be converted to bright pixels

in order to obtain a homogenous region (i.e. without any interior dark valued pixels). Small area features that remain in the image are eliminated, leaving only

the tool wear feature, through morphological erosion. A 5 X 5 structuring element is applied to the binary image to erode smaller areas until the image contains a single tool wear feature from which the set of tool wear parameters is calculated. Since the processing software computes the various wear lengths and areas in terms of pixels, it is crucial to determine precise calibration factors (mm/pixel) for obtaining an absolute value of these wear parameters.

3.4.2. texture

Among the non-contact techniques, that have been proposed as an alternative to the stylus profilometer,

optical techniques are the most promising in terms of accuracy, speed and flexibility. Of the numerous optical techniques that have been used over the years, the relatively recent introduction of low-cost vision-based processing systems has opened up a new area of surface quality measurement with many exciting possibilities. Previous research indicates that

the majority of these techniques have focused on correlating one or more parameters, extracted from the vision system software processing, with stylus measurements of R, on the same surface.

During the monitoring process, the workpiece is positioned on the platform such that the machining marks are perpendicular to the longer dimension of the imaging sensor (see Fig. 8). The first step in processing these images consists of eliminating the influence of the non-uniform background. This could be done by subtracting the background image, ob- tained by fitting a second order regression surface with points representing the background, from the original image [34].

Images of the machined surfaces can be processed either in the spatial or the frequency domain. In the

Fig. 8. Photograph of the characteristic texture formed on a turned

surface.

spatial domain approach, the Gray level histogram of the scattered pattern is used to extract statistical parameters. These parameters are then correlated with the roughness (R,) values, obtained from the stylus profilometer, to establish the calibration curves. This relationship varies with the type of material and machining process employed [35,36]. The frequency domain approach involves the trans- formation (either optically or digitally) of the origi- nal image from the spatial to the Fourier domain. The magnitude of the frequency components indi-

cates the degree to which periodically occurring features are present in the image. The frequency spectrum is useful in indicating the roughness com- ponents due to lay marks, tool wear marks and the tool vibration [37]. Statistical parameters, derived from the spectrum, have been correlated with the surface roughness obtained with the stylus pro- filometer.

4. Cutting tool inspection

Research work carried out in the direct assess- ment of the tool condition is presented in the follow- ing sections.

4.1. Flank wear

The first attempt to utilise a vision system for characterising tool wear is attributed to Matsushima et al. [38]. The cutting tool was examined by a TV camera at every tool change. The gray level image was converted into a binary image by using a thresh- old value, selected manually from the intensity his- togram. The flank wear width was calculated directly

S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72 63

from the binary image by counting the number of

image elements in the direction of flank wear. Due to the irregularities in the surface texture of the wear land, the intensity of the reflected light varies over the entire worn region. Hence, the use of global thresholding generally produces a binary image con- sisting of stray dark pixels within the tool wear

region leading to erroneous results. By deploying a vision sensor for in-cycle assess-

ment of flank wear, the sophistication of the mea- surement technique was improved by Cuppini et al. [39]. A TV camera., equipped with necessary optics,

was mounted on the machine-tool. The cutting tool,

illuminated by a fibre-optic bundle, was imaged during cutting dwells. Three different segmentation algorithms were implemented. The measurement sys-

tem was not calibrated to provide absolute units of measurement. No comparisons were made between the segmentation techniques that were used in the

work. A more comprehensive approach, using a VI-

COM-based image analysis system, has been re- ported by Lee et al. [40]. The cutting tool was positioned under a microscope, equipped with a Vidicon camera, u:sing a specifically designed fix-

ture. The processing of the tool wear image was carried out in twcl steps. First, a simple contrast stretching algorithm was used to enhance the image, followed by an interactive segmentation process to delineate the worn region from the background. By deriving a number of parameters, an attempt was made to provide a more complete description of the flank wear phenomlena. However, the use of interac- tive segmentation limits the technique to laboratory use. Controlled illumination was identified as a key factor in improving the system performance.

A fibre-optic sensor prototype was developed for in-cycle inspection of cutting tool wear [41]. Two different lighting arrangements were used in con-

junction with one camera position to record wear images from flank and rake faces. For flank wear, light from a diffused source was used to discriminate between the worn and the unworn regions. The segmentation of the flank wear images was accom- plished by using lo-pixel wide stripes. For each stripe, the threshold1 was selected by determining the average gray level for the worn and unworn regions. The sensor, with minor modifications, can be utilised

on several machines operating under different work- ing conditions.

An alternate approach, using a coherent light source, was investigated by Jeon and Kim [42]. The cutting tool tip was illuminated by a laser beam (0.8 mm beam diameter) and the reflected pattern was captured by a camera, located perpendicular to the flank face. A sequence of image processing steps were performed on the binary image to remove noise and produce a contour of the wear region. The accuracy of the system was found to lie within 0.1 mm of the traditional tool microscope results. The

small size of the illumination area limits the amount

of wear information that could be derived from each image frame. The high processing speed (1.7 set) makes the sensor suitable for on-line measurement.

The possibility of using a vision sensor for on-line assessment of flank wear was investigated by Peder- sen [43]. The camera and the light source (halogen lamp) were mounted on VDF-Boehringer PNE 480 CNC turret lathe. Flank wear region was delineated by using the threshold value determined from a smoothed histogram. The measurements from the system were found to conform to the traditional three-stage pattern (initial, steady state and terminal

wear). Due to the spurious reflections from other parts of the tool, large variations in the flank wear width were observed. Selection of a threshold from the gray level histogram in conjunction with the inability of the lens to reproduce sharp changes of contrast limited the accuracy of the measurement system.

An adaptive observer, combination of the ob- server technique based on the flank wear model and the recursive least squares parameter estimation al- gorithm, was used to measure flank wear [44]. A

vision system was used to calibrate the results ob- tained with the adaptive observer. Due to the limited resolution of the camera, only the central portion of the flank wear region was imaged. After threshold- ing, the flank wear width was determined as the distance between the top and bottom of the flank wear region. From the experimental results, it was observed that the integrated method performed well under constant and time varying cutting conditions.

Teshima et al. [45] were the first to integrate a vision sensor with neural network processing capa- bility to predict tool life. The state of the flank and

64 S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72

crater wear along with the cutting conditions were input to a 3-layer neural network classifier, which predicted the rest of tool life and the wear type. More emphasis was placed on the neural network processing aspect rather than the tool wear assess-

ment. A slightly different approach for determining the

flank wear involved the use of the rake face image

[46]. The amount of flank wear was determined from the deformation data and the sharp tool geometric

data. This method provides an entire flank wear map along the cutting edge. However, the accuracy of the method depends on how precisely the relative posi- tion and orientation of the tool could be achieved

with respect to the camera. Sensor Adaptive Machines Inc. (SAMI) has re-

cently developed an optical tool condition sensor for commercial use. This has been used by Du et al. [47] for monitoring flank wear. The sensor and the light source are provided in a completely sealed housing suitable for use in a machining environment. The

tool conditions were determined by comparing the master template of the tool profile, sensed when a new tool is inserted, and the tool profile obtained

after each cut. The tool profile was calibrated, for the presence of dirt on the tool tip and the relative positioning, from the master tool profile. Various parameters describing flank wear were derived from the two profiles. The positioning of the current tool profile with respect to the master profile is crucial in extracting accurate wear information. This limits the deployment of this instrument for in-cycle applica-

tions. A two-pass segmentation process was used to

identify and label the three texturally distinct regions in a flank wear image [48]. During the learning period, several tools were imaged under different lighting conditions and gray level ranges were as- signed for each region. Hough transform, which identifies straight lines and arcs in an image by transforming the image points into the parameter space of a line or a circular arc was used to identify the tool tip. Experiments were carried out with two different workpiece materials, and the wear land shape was found to be dependent on the material. The selection of the threshold levels from the learn- ing periods is critical for obtaining a good segmenta- tion of the worn region.

4.2. Crater wear

As crater wear is prevalent only under certain cutting conditions, fewer studies have been carried out to investigate the problem. Lee et al. [40] were the first to utilise a vision system to study the crater wear growth. Based on the nose angle of the tool, the software distinguished between the crater and flank

image. To ensure that the tool failure is crater domi- nated, higher cutting speeds and feeds were em-

ployed. Six parameters, including the average nose radius and the chipped area, were derived from the crater wear pattern. As pointed out in the flank wear measurement, the necessity of performing interactive segmentation limits the method to a laboratory envi- ronment.

Giusti et al. [41] proposed a more sophisticated experimental configuration to record crater wear im- ages. A laser beam was passed through a diffraction grating and the resulting interference fringes were projected onto the rake face of the tool. The deflec- tion of the fringes indicated the amount of deforma-

tion on the rake face, i.e. crater wear. This arrange-

ment has an advantage of providing a 3D map of crater wear, however, the control of lighting condi- tions requires added complexity in the hardware and would be hard to maintain in an industrial setting.

4.3. Tool breakage

One of the more dominant modes of failure, for

more than a quarter of all the advanced tooling material, is attributed to the breakage of cutting tool inserts [49]. For a sensor to be successful in detecting tool breakage, it should be able to operate under diverse cutting conditions and the output should be uniquely distinguishable [50]. Although machine vi- sion is well suited to tackle this problem, only a few systems have been implemented.

Matsushima et al. [38] detected the tool breakage and deformation by tracing the cutting edge line of the binary image of the cutting tool. If large varia- tions were continuously present over a span of more than three image elements, breakage was identified on the cutting edge. Similarly the deformation was detected when the difference between the original cutting edge line and the actual exceeded a certain value. The presence of other process irregularities

Tab

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hara

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istic

s of

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66 S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72

(hard spots, chip entanglement) or improper selection of an optimum threshold could lead to an inaccurate prediction of tool breakage.

Cuppini et al. [39] noted that tool breakage would result in loss of image section that would be incom- patible with normal wear dynamics. Thus a compari- son of the tool templates before and after a cutting process would reveal the occurrence of any break-

age. An accurate matching of the templates would depend on the tool and camera individual positions being the same in both situations. Also, any prob- lems that occur during the registration of the two templates would lead to an erroneous prediction.

Oguamanam et al. [48] predicted breakage by determining the difference between the nearest tool edge point and the tool tip, and comparing it with a pre-set threshold. The tool was classified as broken if the threshold was exceeded. The threshold was deter- mined from a series of tests performed with a sharp tool. The threshold was set to accommodate the allowable amount of tool tip roundness and round-off errors. Selection of the proper breakage threshold is

crucial, as it could lead to catastrophic results if a broken tool was incorrectly classified as good.

To facilitate the comparison of vision-based tool wear measurement techniques, reviewed in this pa- per, Table 1 is provided. Table I organised into four categories, hardware, software, results and the appli- cation area.

5. Workpiece surface quality inspection

Research work carried out in the indirect assess- ment of the tool condition is presented in the follow- ing sections.

5.1. Su$ace texture assessment

One of the earlier attempts in using a vision system involved a 2D light sectioning method to study the effect of various operating conditions on the surface finish of a turned part [51]. An equi-con- tour map of the patterns was used to highlight vari- ous features of the machining process. A 2D Fourier analysis was shown to be an effective tool in charac- terising the chatter marks. Baker [52] developed a microscope image comparator, capable of recording

the image intensity distribution along with the far- field diffraction pattern, for on-machine assessment of workpiece surface texture. The degradation of the surface finish with severity of tool wear was shown by generating profiles from the intensity distribution.

A more successful attempt at quantifying rough- ness using a vision system involved the use of a Gray level histogram of the light scattering pattern

from ground surfaces [35]. Optical roughness param-

eter, defined as the ratio of statistical parameters derived from the histogram, was correlated with R,

for samples from different materials. A non-linear, increasing trend with R,, was observed for the opti- cal parameter. As the gray level histogram is based on tallying the number of pixels for each intensity level, the optical parameter is affected by the overall uniformity and degree of illumination. By incorporat- ing a fibre optic lighting arrangement to the mea- surement system, the technique was extended for samples from different machining processes [36]. The technique was further modified to include a

yellow LED (light emitting diode) light source in the measurement system [53]. A qualitative comparison of gray level histograms was carried out for various machined surfaces, but failed to evaluate the results on a broader set of samples.

Shiraishi and Sato [54] implemented dimensional and roughness control in a turning operation by developing an optical system based on the shadow graph principle. Surface profiles of the turned part were imaged by passing a laser light beam over the edge’s profile. The sensor determined the maximum

value of roughness (typically rough surfaces were examined, R, = 10 km> on the part and a flat bite tool was used, where necessary, to keep the rough- ness profile within tolerance.

Digital Fourier patterns of the light scattering distribution were shown to be an effective way of comparing various machined surfaces [55]. It has

been pointed out that these patterns facilitate the manifestation of various machining process charac- teristics. The possibility of generating these patterns with an optical set-up was investigated by Huynh et al. [56]. The peaks in the power spectrum, derived from the Fourier pattern, were found to correlate with the feed rate spacing. Statistical parameters computed from the spectrum were used to charac- terise surface roughness. Cuthbert et al. [57] derived

S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72 67

the gray level hismgram of the optical Fourier pat- tern to deduce a roughness parameter. As rougher surfaces tend to c:reate a diffuse pattern on the camera, the technique was limited to lower rough- ness range (R, < 0.4 pm). Also, the need for the precise alignment of the imaging optics, makes it unsuitable for on-line inspection.

A new hybrid roughness parameter, based on both the spacing and amplitude characteristics of the ma- chined surfaces, was proposed from the data ob-

tained with a vision system [58]. The parameter was shown to be successful in the assessment of wear track for the evaluation of lubricants. However, the

measurements have been limited to the higher rough- ness range (6-100 pm) due to the low resolution of the camera.

The ideal roughness profile, that the tool should produce on the workpiece, was determined by imag- ing the cutting tool [59]. The profile was found to be

similar to the one observed on the workpiece, and the differences werf: attributed to the swelling of the material during the cutting process. Based on an extensive literature survey, a design strategy for the potential development of an on-line roughness sensor was proposed by Jolic et al. [60]. Three algorithms,

based on analysing the scattered light distribution of machined surfaces, were utilised to process the sen- sor data. Ceramic parts, machined by different pro- cesses, were examined. Parameters from the three algorithms were fa’und to correlate reasonably well with the stylus measurements.

An in-process assessment of turned part quality was performed by Lonardo et al. [61]. Diffraction patterns of the rotating stainless steel samples were

recorded by a CCD camera and input into the neural network for classification. The ability of the super- vised and unsupervised networks for accurately clas- sifying the machined surfaces was assessed.

More recently, a fairly comprehensive database comprising of three:-dimensional light scatter images, their classification measures, lookup tables of the most efficient measures and the 3D stylus maps was compiled for samples from different machining pro- cesses [62]. The classification measures used include: geometric, colour content and AI. The lookup tables were provided to identify the best measures for a given machining process. It has been pointed out that the system is capable of discriminating surfaces with

similar finish, produced by different machining pro- cesses.

To facilitate the comparison of vision-based sur- face roughness measurement techniques, reviewed in this paper, Table 2 is provided. Table 2 is organised

into four categories, hardware, software, highlights and the application area.

5.2. Correlation of surface texture with machine tool

life

Theoretically, the surface roughness of a turned workpiece is given by

f2 R, = ~

18fiR ’

where f is the feed rate and R is the tool nose radius. The roughness can be affected by the follow- ing factors [63]:

- Controllable operating conditions such as the feed rate, cut-

ting speed and the depth of cut; material properties of the cutting tool and the

workpiece; tool geometry.

- Uncontrollable tool conditions such as chatter, wear and built- up-edge.

In the absence of uncontrollable factors, the sur- face roughness was found to conform to the theoreti-

cal value [63]. The optimum cutting conditions for carbide and ceramic tools were investigated by in- corporating tool life, which was defined as the time required for the surface roughness R, to deteriorate

to a value of 1.524 pm (representative of general finish turning) [64]. Allen and Brewer [65] related the machine variability and tool flank wear to the surface roughness of the workpiece. The R, values were found to be distributed normally, with the lowest value reported at a tool flank wear of 0.737 mm. Gillibrand and Heginbotham [66] related the surface roughness values, obtained from the lay and perpendicular directions, to the cutting speed for various workpiece materials.

Sundaram and Lambert [67] found in their investi- gation that the lowest roughness values were ob- tained at a flank wear width of 0.889 mm. It was

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S. Kurada, C. Bradley/Computers in Industry 34 (I 997) 55-72 69

also observed that the roughness values obtained with a tool having a flank wear level of 0.838 mm were less than those obtained in the initial stage of tool wear. This suggested that the tool can be used

productively for a greater length of time rather than discarding them at a wear level of 0.762 mm. Also, no significant variation in the diameter of the turned

part was observed beyond the flank wear level of 0.84 mm.

Sata et al. [68] studied the wavelength spectrum of the machined workpiece to identify abnormal conditions such as chatter, spindle error and the swelling of the workpiece. A monitoring system, which utilised a light reflectance sensor in conjunc- tion with a pattern classification model, was devel- oped by Domfeld and Fei [69]. The classifier, based

on the linear discriminant function, was able to distinguish between surfaces, produced under differ- ent states of the cutting tool condition.

Xue et al. [70] utilised scanning electron micro- scope (SEM) images to study the effect of flank wear on the machined surface quality. The roughness of the machined surface was found to deteriorate with increase in tool wear. Additionally, small cavi-

ties were detected on the surface. When a severely worn tool was used., the machined surface was found to be very rough, with partially fractured laps or cracks on the outside boundary. This was attributed to the higher contact pressure between the tool’s flank face and the workpiece, resulting in adhesion wear.

Surface roughne.ss of the workpiece was incorpo- rated as a specification for tool condition by Du et al.

[48]. When a new insert was used, the roughness values initially decreased over the first few cuts, and then started to increase gradually until the tool was worn out. The surface roughness values were found to increase at a much higher rate, when the tool was worn out. Based on this behaviour, a new model that compensated for tool wear was proposed. The tool was classified as good, if the roughness predicted by the model was less than that required, otherwise it

was discarded. A workpiece inspection strategy, based on the statistical analysis of the roughness data, was developed by Yang and Jeang [71]. A mathematical model developed for predicting flank wear was used to describe the surface roughness behaviour.

6. Integrating wear sensors with data communica- tion

The diagram in Fig. 9 illustrates the integration of a vision-based tool wear monitor within a mini- workcell environment. At the supervisory level of

the network, operations such as CNC part program creation and part scheduling are performed. The information, for each CNC lathe, is transmitted to the machine via a protocol such as MAP (manufac-

turing automation protocol) over a local area net- work. Fibre optic network links are preferred due to their high bandwidth and immunity to radio fre- quency noise and cross talk.

Information on tool wear state (and additionally surface texture of the work piece) is provided by each of the vision sensors. As previously discussed,

each tool wear image is processed to extract the relevant wear parameter; this can be accomplished at

each CNC lathe or alternatively, each image can be supplied to a central image processing station (via

the fibre optic link) at the supervisory level. Higher level information regarding tool wear and surface quality can be combined with other sensor informa- tion to monitor the overall machine tool condition.

Each CNC controller, as illustrated in Fig. 9, would typically be a ‘closed box’ controller that has

Fig. 9. Integration of vision-based tool monitors with work cell

control network.

70 S. Kurada, C. Bradley/ Computers in Industry 34 (1997) 55-72

the principal function of processing the part program and controlling the tool’s servo motors. Recent trends to open architecture controllers permit vision proces- sor boards to reside on a common bus with the

CNCboard. This eliminates the need for additional stand alone computers at each cell, and ultimately would permit more immediate reaction to changing tool condition at the machine tool level. A single vision-based sensor (with associated processing soft- ware) can monitor both tool wear and workpiece surface texture (such as average roughness and as- perity peak count). Complimentary sensor data per- mits construction of a process model that would be

unavailable from a single sensor which is inherently a more reliable source of feedback on the tool wear

state.

7. Concluding remarks

The state of vision technology as applied to tool condition monitoring has been discussed in this pa- per. The development of these sensors is particularly crucial for the realisation of fully automated manu- facturing environments, such as unmanned machin- ing centres.

The computer vision techniques provide addi-

tional wear data (tool chipped area, tool wear area, etc.) that are not available from most of the other sensors. However, more research is needed in estab- lishing vision sensors for on-line applications in an industrial environment. Future research should be aimed at developing a sensor that is capable of deriving tool wear parameters from multiple data modes (e.g. morphological and textural data) and fusing the data modes to provide a robust indicator of tool condition. The work discussed in this paper has demonstrated that vision sensors adapt well for

measuring multiple modes of wear data. Some of the observations arising from this review, are sum- marised below:

The use of Gray level images should be explored. The inherently greater information content present in gray level images will enable more reliable tool wear monitoring at no extra cost. Textural charac- teristics or pattern recognition techniques could

also be used with gray level images, to greater effect. Surface texture information, obtained from the workpiece, should be incorporated into the tool

wear prediction model. Such information could be vital in the selection of optimum cutting condi- tions. This information could be obtained by us- ing the same vision sensor, that has been used for tool condition monitoring, with minor modifica- tions. Most of the research that has been carried out using vision sensors for tool wear measurement

involved global thresholding. The use of more efficient segmentation algorithms, such as region based techniques, should be investigated as they

are less prone to errors. To establish vision sensors as a standard for tool wear measurement, more studies have to be un- dertaken with a wide range of materials and under different machining conditions. Also, the results should be validated with theoretical models that have been proposed in the past, as the information obtained from traditional techniques is very lim- ited. A standardised benchmark for the system perfor- mance in terms of accuracy, precision and bias

should be reported for every vision sensor that is implemented for tool wear measurement.

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Satya Kurada is presently working as a

research and development engineer at

Mipox International Corporation, Hay-

ward, California. He earned his doctor-

ate in Mechanical Engineering from the

University of Windsor, Windsor,

Canada. His research interests include

automated inspection, surface topogra-

phy and image processing.

CoIin Bradley is an Associate Professor

of Mechanical Engineering at the Uni-

versity of Victoria, British Columbia,

Canada. Currently, his research interests

include the application of 3D machine vision to reverse engineering and the

use of sensor technologies within the

manufacturing work cell.