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CIRPJ-236; No. of Pages 21 CIRP Journal of Manufacturing Science and Technology xxx (2013) xxx–xxx

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CIRP Journal of Manufacturing Science and Technology journal homepage: www.elsevier.com/locate/cirpj

Review

Application of digital image processing in tool condition monitoring: A review S. Dutta a, S.K. Pal b,*, S. Mukhopadhyay c, R. Sen a a

CSIR-Central Mechanical Engineering Research Institute, Durgapur, India Mechanical Engineering Department, Indian Institute of Technology, Kharagpur, India c Electronics and Electrical Communication Engineering Department, Indian Institute of Technology, Kharagpur, India b

A R T I C L E I N F O

A B S T R A C T

Article history: Available online xxx

Tool condition monitoring is gaining a parallel development with the advancement of automatic manufacturing processes in the last thirty years due to the increasing need for improvement of product quality. The advances of digital image processing techniques used in tool condition monitoring are an important research interest due to the improvement of machine vision system, computing hardware and non-tactile application. In this paper, a review of development of digital image processing techniques in tool condition monitoring is discussed and finally a conclusion is drawn about required systematic research in this field. ß 2013 CIRP.

Keywords: Tool condition monitoring Digital image processing Surface texture Tool wear

Contents 1. 2. 3. 4.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and disadvantages of DIP for tool condition monitoring. 1.1. Digital image processing techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lighting systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direct TCM techniques using image processing . . . . . . . . . . . . . . . . . . . . . . . Two dimensional techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. 4.2. Three dimensional techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indirect TCM techniques using image processing . . . . . . . . . . . . . . . . . . . . . 5.1. Online techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Offline techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction In any machining process, high quality of the final product is the ultimate aim. The trend towards automation in machining has been driven by the need to maintain high product quality with improving production rate and the potential economic benefits of automation in machining are significant as well. These process improvements can be possible by monitoring and control of machining process. Tool condition monitoring (TCM) is very much inevitable for reducing machine tool downtime. Reduction of

* Corresponding author. Tel.: +91 3222 282996; fax: +91 3222 255303. E-mail address: [email protected] (S.K. Pal).

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machine tool downtime improves production rate, significantly. Excessive wear and breakage of the cutting tool is one severe cause of downtime. Dull or damaged cutting tool can put extra strain on the machine tool as well as surface finish of the machined part. Cutting speed can increase 10–50% with appropriate TCM techniques [105]. In a TCM system, acquisition of machining process data viz. cutting force, sound energy, power, current, surface finish, vibration, temperature, etc., which are influenced by cutting tool geometry and machining process conditions, has been performed through high level intelligent sensors viz. dynamometer, acoustic emission sensor, power and current sensor, surface profiler or vision based system, accelerometer, pyrometer [121]. The acquired sensory information are then filtered and processed through signal processing and some relevant features are extracted

1755-5817/$ – see front matter ß 2013 CIRP. http://dx.doi.org/10.1016/j.cirpj.2013.02.005

Please cite this article in press as: Dutta, S., et al., Application of digital image processing in tool condition monitoring: A review. CIRP Journal of Manufacturing Science and Technology (2013), http://dx.doi.org/10.1016/j.cirpj.2013.02.005

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from the results of signal processing techniques. Then prediction of process data and process optimization can be possible using design 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 for turning, milling, drilling and grinding processes. Nebot and Subiro´n [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 advanced machining operation. They also mentioned the real industrial implementation of 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 optimization on turning, milling, grinding and drilling operations for optimization of cutting conditions with minimum cost machining with 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 surface quality is mainly dependent 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 deteriorates) 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 maker’s microscope, 3D surface profiler, optical microscope or scanning electron microscope (off-line method) or with CCD camera (inprocess 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 implemented, 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 those limitations, research is going on to identify the degree of tool wear by analyzing surface texture of machined surfaces with digital image processing technique from the images of machined 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 applies no force or load to the surface texture under 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 in machining [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.

Please cite this article in press as: Dutta, S., et al., Application of digital image processing in tool condition monitoring: A review. CIRP Journal of Manufacturing Science and Technology (2013), http://dx.doi.org/10.1016/j.cirpj.2013.02.005

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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 can be 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 condition monitoring due to uneconomic time, cost ineffectiveness and inaccessibility to the machine tools; to overcome this situation, a machine vision based system 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) Monitoring of drill parts using DIP are very difficult 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 using digital image processing. The fourth 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 Semiconductor) digital camera. CCD camera is comprised of CCD sensor which is an array of photosensitive elements to collect electrical charges 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 is much 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].

3

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 ðx; yÞ ¼ iðx; yÞrðx; yÞ

(1)

Image pre-processing is required for the improvement of images by contrast stretching, histogram equalization, noise reduction by filtering, inhomogeneous illumination compensation 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 components), 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 detection are 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 the machined surface images. Image segmentation is the method of partitioning an image into multiple 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 aggregate adjacent pixels starting from random seeds (region centres), again by comparing pixel values. Watershed-based segmentation 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),

Please cite this article in press as: Dutta, S., et al., Application of digital image processing in tool condition monitoring: A review. CIRP Journal of Manufacturing Science and Technology (2013), http://dx.doi.org/10.1016/j.cirpj.2013.02.005

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respectively. 2 3 df   6 Gx dx 7 7 ¼6 4d f 5 Gy dy

uðx; yÞ ¼ tan1

  Gy Gx

(2)

(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) using Canny edge detector are shown in Fig. 2. A wear 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 Laplacian of Gaussian 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 its background, thresholding techniques are generally used. A very common thresholding technique used in tool wear measurement is Otsu’s optimal thresholding technique. In this technique, a class, C0 is formed with all the grey value V(k) for a grey level intensity, k and all the other form another class, C1. Optimal k value is selected for maximum between-class variance. In bi-level thresholding technique images are partitioned 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 for binarization of flank wear profile. After edge detection and thresholding, morphological operations 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 analysis method. This method can be applied after pre-processing. Texture is a repeated pattern, which is a set of local statistics or attributes vary slowly or remain approximately periodic. Primitive in texture is a connected 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 based methods. In statistical method a texture is modelled as a random field 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 be used as statistical texture classifiers. In geometric texture analysis method, the analysis depends upon the geometric properties of texture primitives. Voronoi tessellation, Zucker’s 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 modelbased texture analysis methods. In signal-processing based texture analysis, spatial domain filtering, Fourier-domain filtering, 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 inhomogeneous illumination for improper lighting set-up, the information from images will not be sufficient for any machine vision application. Several researches give strong importance on 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] tried to capture the tool wear image using a diode flash light incorporated with a infrared band filter, which helped to enhance the tool wear region with respect to the background. Kurada and Bradley [73] used two fibre-optic guides to capture the tool wear regions. They used it to obtain adequate contrast between the worn and unworn tool regions. Pfeifer and Weigers [99] used ring of LEDs attached with camera to capture the proper illuminated images of tool inserts from different angle. Kim et al. [70] used a 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 and flank 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 of structured 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 images by 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 He–Ne laser 0.8 mm-diameter beam for focusing onto the machined surface by 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 white coated glass from where the scattered pattern was grabbed 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 reflected pattern. Tsai et al. [123], tried to obtain a homogeneously illuminated machined surface image by a regular fluorescent light source which was situated at an angle of approximately 108 incidence with respect to the normal of 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

Please cite this article in press as: Dutta, S., et al., Application of digital image processing in tool condition monitoring: A review. CIRP Journal of Manufacturing Science and Technology (2013), http://dx.doi.org/10.1016/j.cirpj.2013.02.005

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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.

changing the position of lighting fixture. During surface assessment, the specimen was positioned on the platform so that the lay marks were perpendicular to the longer dimension of the CCD sensor. The light source was positioned at a distance of 8 cm from the surface, as this provided the best image contrast. In this technique, the images of flat specimens (end milled) were captured but the images of turned surface (i.e. curved surfaces) were not obtained. Lee et al. [78] used a 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 diffused lighting system (a DC regulated light source with 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 be used to reduce the interference of ambient lighting in industrial environment.

4. Direct TCM techniques using image processing There are two predominant wear mechanisms for a cutting 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. 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.

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4.1. Two dimensional techniques Flank wears are determined by two dimensional techniques. Kurada and Bradley [74] made a review on advances of machine vision sensors which are used to obtain information about the cutting tool and machined part. They made the comparison of advancement of machine vision techniques up to previous decade. They emphasized the laboratory level development. Kurada and Bradley [73] did a pioneering work for direct tool condition monitoring by capturing images of tool flank wear by using two fibre optic guided lights and CCD camera. Both lights were adjusted for illuminating the tool flank wear region. They first calibrated the image in terms of two factors in horizontal and vertical direction to convert pixel unit to length unit (micron). In their work, they used texture-based image segmentation technique step by step using image enhancement (using cascaded median filter) to reduce noise, image segmentation to extract the flank wear region from background (using variance operator), global thresholding, feature extraction by morphological operation (blob analysis) and flank wear calculation (by boundary and regional descriptors). However, they tried it out in offline using video zoom microscope. In case of offline techniques, all the time cutting inserts or cutting tools has to be disengaged from the machine tool. Thus, this is very much time consuming and may be erroneous for proper alignment of the cutting tool. For this reason, Weis [132] did a pioneering work to capture the tool wear region of a milling insert without disengaging the insert from the tool holder. Also the tool wear region has been enhanced and the background has been faded out with the help of an infra red band filter at the time of image acquisition. Diode flash light has also been synchronized with the CCD camera to capture a perfect tool wear region. A dilation and binary operation has been applied on flank face image to measure the flank wear width. They mainly have given the importance to the lighting system for online capturing of tool wear images, accurately. However, image processing methods in their technique has been given a second priority. Tauno and Lembit [120] developed a software for detection of flank wear using nonlinear median filter to remove noise and a Roberts filter operator for edge detection. This system provided the automatic measurement of surface area, average wear land length and perimeters of flank wear profile. However, their method could not be utilized for fully automated measurement. Pfeifer and Wiegers [99] captured images of tool inserts with a ring light in different angles of incidence. Then they compared those captured images and reduced inhomogeneous illumination problem for even complex cutting edges. However, they did not check their technique for different wear conditions. Sortino [116] developed a flank wear measuring software by using a new edge detection method from a colour image. In this statistical filtering method, the neighbourhood pixels of a pixel were considered as a set and the mean and standard deviation of each set have been calculated for each fundamental colour (red, green, blue). Then, a comparison parameter, Dedge, has been evolved from the set parameters (i.e. mean and standard deviation). Finally, the edge was detected accurately for higher Dedge values and cutting edge, borderline between worn zone and oxidized zone, borderline between oxidized zone and tool surface has been detected. However, the accuracy of this wear measuring system is limited for low flank wear width as the resolution is 10 mm. [58] used a specular reflection by structured lighting for the appearance and characterization of insert surface using projection of a line stripe to determine the deepness and furrowness of rake or flank face of a tool. But this method requires very much complex and costly set up for image acquisition. Also the method did not use any three dimensional model to show the depth profile of crater as well as

this method could not be helpful to measure the crater depth in grooved inserts. Wang et al. [131] developed an automated system to capture and process successive images of moving inserts to measure flank wear in milling using cross correlation technique between successive image pairs. The method developed by them is a robust technique to remove noises using a novel parallel scanning technique. However, the method is a threshold dependent method where the accuracy of the measurement is dependent on the selected threshold value and the method has not been very much useful to measure the coated carbide insert due to the mal interaction between the lighting and the coating material. To recover the limitation of threshold on the accuracy of measurement, they deployed another technique based on the moment invariance to select the exact bottom portion of a flank wear profile with maximum 15 mm error and minimum 3 mm error compared to the measurement obtained from microscope [128–130]. They also measured the flank wear of coated carbide insert, successfully. Though this system was independent of the thresholding, but it was dependent on the accuracy of a reference line with respect to whom the flank wear width was determined. The computation time for this method was 2 s which was not at all practical for real time measurement. Fadare and Oni [35], evaluated flank and notch wear using the insert images. Tool insert images were first filtered by Weiner filtering. Length, width, area, equivalent diameter, centroid, major axis length, minor axis length, solidity, eccentricity and orientation were the extracted descriptors of wear. They have taken tool insert images in a dark room with the help of two incandescent light sources. Maximum absolute difference of measurements between microscope and vision system was 3.13%. An overall tool wear indicator, namely, Tool Wear Index (TWI) was also derived from the extracted wear descriptors, which was a highly reliable tool wear indicator. A very good systematic variation was obtained in Fadare’s work. However, C/C++ programming language can give faster result than MATLAB and may be used for real time application. A better lighting conditions such as fibre optic guided light or diffused ring light is required to implement their method in industrial environment. Liang et al. [83] utilized an image registration and mutual information based method to recognize the change of nose radius of TiN-coated, TiCNcoated and TiAlN-coated carbide milling inserts for progressive milling operation. They also used the similarity metrics to describe the nose radius. However, their method is quite difficult for the measurement of crater and flank wear. Sahabi and Ratnam [109] measured nose radius of the turning insert online from the silhouette image of the cutting tool tip. They utilized median and Weiner filtering to reduce the image noise; applied morphological operations to reduce the noise due to micro-dust particle; used a conformity method for reducing the misalignment error; applied thresholding and subtraction of worn and unworn tool to measure the nose wear area. They used this technique for turning with various cutting speeds. However, they did not quantify the flank wear width. To improve their method for measuring the flank wear width in the zone nearer to the nose of the cutting tool, they utilized the information of nose radius and machined surface roughness profile using machine vision system in online for turning operation [108]. They have obtained an mean deviation of 7.7% and 5.5% between the flank wear determined by their method and flank wear measured by using tool maker’s microscope from nose radius and surface roughness profile, respectively. However, their method is very difficult to implement in ultra-precision machining with low feed rate. Kim et al. [70] has been developed a magnetic jig for fixing the camera and lighting system to accomplish the objective of on machine tool measurement of flank wear for a 4-fluted end mill. They compared the signal to noise ratio of measurements using microscope and CCD camera incorporating with a novel jig. They

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inserted the fibre optic guided lighting system into the lens for further improvement. However, this work is more prone to the measuring system instead of image processing technique. Kerr et al. [68] utilized four different texture analysis techniques namely histogram based processing, grey level cooccurrence technique, frequency domain based technique and fractal method to analyze the texture of the worn region of turning and milling insert. They obtained the best result by frequency domain or Fourier spectrum analysis techniques because this technique is position and illumination invariant. However, they have captured the tool tip portion of turning insert instead of the flank face portion which is not a standard practice. Jackson et al. [49] has been proposed a novel technique for accurate edge detection algorithm utilizing neural network technique for tool wear detection. They have utilized the scanning electron microscopic images of the flank wear images of a 4-fluted high speed steel milling cutter. However, they did not do any online monitoring using CCD or CMOS camera. Lanzetta [76] proposed an automated and flexible vision based sensor system incorporating the measurement and classifications of tool wears. The resolution of their sensor was 40 mm/pixel. However, several tests at different cutting conditions might be required to establish this technique. Measurement of cutting inserts with chip breaker, the effect of noises from dirt, oil chips on the insert surfaces has not been addressed in his research. Schmitt et al. [112] developed a flexible and automated tool flank wear measurement system incorporating ring illuminators and CCD camera where full illuminated and side illuminated images of flank wear portion of cutting inserts have been captured and processed. The full illuminated image was required for main cutting edge detection and cutting edge corner detection whereas sideillumination was used for flank wear profile segmentation and wear measurement. They applied region of interest selection, Sobel filter technique for cutting tool edge enhancement, morphological opening and closing for reduction of enhanced noise and line interpolation for getting the accurate cutting edge on full illuminated image. They applied linear transformation of histogram to brightened the wear area; thresholding and morphological opening and closing for noise elimination; blob analysis for detection of blobs situated outside the wear profile; creation of a dynamic region of interest (ROI) to detect the best initial point for contour detection and snake algorithm for contour detection on side illuminated image. Then they measured the average flank wear width and maximum flank wear width with 4.4 mm resolution after calibrating the vision system using a chequerboard pattern. Though their system is highly accurate but the computational time was not emphasized in their work. Stemmer et al. [117] applied a neural network classification technique to classify the flank wear and breakage of the cutting tool with 4% error using image processing. They have observed by their machine vision system that the flank wear featured a sharper and brighter textures whereas the breakage of the tool consisted of smooth and rough textures. Based on this phenomenon, they have classified the types of wear and also measured flank wear area, maximum and average flank wear width automatically, using Canny edge detection and line interpolation, pre-filtering and blob analysis, active contour detection by snakes algorithm, wear classification and measurement with a resolution of 4.4 mm/pixel. However, the chipping, crater wear, notch wear and nose wear were not classified in their technique for different variety of cutting tools. A faster classification approach was proposed by Castejo´n et al. [18,13]. They estimated different wear levels (low, medium, high) of the tool insert by means of the discriminant analysis of nine geometrical descriptors and assessed by means of the Fowlkes–Mallows index and also Zernike, Legendre, Hu, Taubin and Flusser invariant moments were used to characterize the shape of the worn region of

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flank wear zone in the binary images to classify the three wear levels viz. low, medium and high wear. Hu descriptor was found to be the best one in their work. However, this technique may be more simple and useful for indirect monitoring technique by processing the machined surface images. Alegre et al. [6] computed the average and maximum flank wear width based on contour signature of the binary image of flank wear profile. Contour signature is a vector whose elements are the distance between the centroid of the contour and the boundary pixel points. The number of elements can be chosen by the user. They have chosen 40 and 100 number of elements and on this basis they classified the low wear and high wear inserts used in turning experiment using knearest neighbourhood (k-NN) classification and multi layer perceptron neural network (MLPNN) classification. Finally, they concluded that the wear classification result was best by using the MLPNN, average flank wear width and 40 element signature with 5.1% minimum error. However, the classification has been based on two classes of wear only and the system calibration has not been done in their work. Atli et al. [11] developed a new measure namely DEFROL (deviation from linearity) to classify between sharp and dull drilling tool from their images. However, the emphasis has been done on the change of point angle and linearity deviation of the cutting edges due to the wearing effect, but no study has been taken care regarding the flank wear in this technique. So this technique was not suitable to measure the flank wear which was used to define the tool life in standard practice (ISO 3685) [48]. Makki et al. [86] did a real time capturing of drill bit image at the time of 100 rpm rotation. Then they processed those captured images by edge detection and accurate segmentation technique to find out the tool wear (only the deviation of the lip portion) and tool run-out in the image plane. However, the measurement of flank wear and tool run out perpendicular to the image plane cannot be possible by their technique. Liang and Chiou [82] has been introduced a flank wear measurement technique of multilayer coated twist drill by using image processing. They have detected the edges of the wear profile on the cutting plane using spatial moment edge detector with subpixel accuracy and also they smoothed the edges using B-spline technique. After that they have applied the Gaussian low-pass filtering technique for smoothing the curvature curve and finally, applied a statistical process control measure to select the accurate threshold value for extracting the accurate wear profile for precise measurement of maximum flank wear width. For improving the wear measurement technique of twist drill used for micromachining, Su et al. [118] studied the feasibility of measuring the flank wear in a micro drill of diameter 0.2 mm for drilling 10-layered PCB (Printed Circuit Board) with digital image processing technique. They measured wear area, average and maximum wear height by the help of an automated edge detection algorithm for cutting plane segmentation with 0.996 mm/pixel resolution in 1 s. The advantage of this technique is automatic detection of reference line and wear profile of microdrill irrespective of the position of the object. However, this technique is only useful when the cutting plane image is clearer or when no smearing occurs. There was a problem to differentiate between the smeared part of cutting plane and the clearance part of the micro drill. To overcome this problem, Duan et al. [30] has been applied a level set based technique for accurate segmentation of cutting plane of micro drill bit. They have fused the segmented image and thresholded image to get an accurate result. They also observed that a significant change of area, width and length has been occurred due to the wear of the micro drill bit used for PCB manufacturing. However, they have proposed a future scope to reduce the computational time. Xiong et al. [135] had also used the variational level set based method by eliminating the need for re-initialization of the zero level set

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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 of diameter 1 mm, 0.625 mm and 0.25 mm with digital 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 recognized by this system. Jackson et al. [49] utilized a neural image processing method 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 5 mm 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 types of pre-dominant wears in grinding wheel. Wear flats are developed 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] utilized a region growing method 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 fusion for 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 Ramamoorthy [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 progressive 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 cutting dust and determined the width of stamped line by using image histogram and cross-correlation technique. They observed that the width was decreasing with 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 the depth of crater in different levels of wear by 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 introduced 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 undulation of rake surface. Ramamoorthy and co-workers [61,100] used image processing with stereo vision technique with only a single CCD camera to determine the depth 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 preprocessing 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 with 15 mm resolution. Then they have re-constructed 3D image from the captured images using digital focus measurement. 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 (subnanometer) and high precision measurements over a wider area. However, this technique is an offline technique and the measurement 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 tem

Galante et al. [40] Weis [132]

Diffused lighting Diode flash light with infra-red band filter Fibre optic guided light

Kurada and Bradley [73] Tauno and Lembit [120] Pfeifer and Weigers [99]

sys-

Blue light source Ring of LED

Sortino [116]

Jurkovic et al. [58]

Halogen light along with a laser diode

Wang et al. [128–131]

Laser trigger synchronized with camera, fibre optic guided light Backlighting

Liang et al. [83]

Sahabi and Ratnam [108]

Backlighting

Fadare and Oni [35]

2 incandescent light sources inclined at 458 White ring light, fibre optic guided light

Kerr et al. [68]

Lanzetta [76] Schmitt et al. [112] and Stemmer et al. [117]

Structured lighting with Laser Ring light (for full and side illumination)

Castejo´n et al. [18] and Barriero et al. [13]

DC regulated light with square continuous diffused illuminator

Alegre et al. [6]

DC regulated light with square continuous diffused illuminator Silhouette image of tool

Atli et al. [11]

Makki et al. [86]

Liang and Chiou [82]

Su et al. [118]

Silhouette image of tool captured at 100–1500 r.p.m Circular back lighting

Circular lighting

Image processing

Type of tool wear measurement

Machining

Remarks

Thresholding Dilation and thresholding

Flank wear Flank wear measurement

Turning Milling

Offline, 2D technique No evaluation of accuracy

Image enhancement, Image segmentation, thresholding, morphological operation Median filter, Robert’s edge detector, thresholding Method to set optimum incidence angle of lighting for controlled illumination Median filtering Statistical filter for edge detection Manual measurement using a image processing software

Flank wear measurement

Turning

Offline

Flank wear measurement Flank wear measurement

Turning, milling

Offline, 8% error

Turning, milling

Online

Flank wear measurement

Generalized for insert

Offline Flank wear

Flank wear and deformation of laser light pattern on rake face Flank wear (captured when tool is moving)

Tool inserts

Manual measurement, crater depth measurement has not been done

Milling inserts

Online, max error 15 mm, difficult to measure coated carbide inserts

Nose wear

Inserts

Flank wear from nose radius and surface roughness profile

Inserts

Flank wear

Inserts

Difficult to implement for flank wear width measurement 7.7% (from nose) and 5.5% error (from surface roughness), difficult to implement in very low feed application Sensitive to the fluctuation of ambient light

Flank wear measurement via texture descriptors

Turning inserts, end mill cutter

Texture analysis of wear region, no automatic measurement of wear

Flank and crater wear Flank wear measurement, wear and breakage classification

Generalized for insert Milling

The effect of dirt, oils on inserts did not address Resolution 4.4 mm, classification error 4%; the method has not been applied for different variety of cutting inserts

Classification of low, medium and high wear

Inserts

99.88% discrimination for Hu’s descriptor, no wear prediction has been performed

Classification of low and high wear

Inserts

5.1% classification error; three levels of wear classification is needed

Drill-bit

Drilling

Tool run out detection

Drilling

Flank wear detection for progressive machining

Multi-layer twist drill

Only useful for drilling; Flank wear width cannot be measured Tool run-out perpendicular to the image plane had not been measured Results were not compared with the microscopic wear measurement; applicable for no smear image

Flank wear detection for progressive machining

Micro drill-bit (for PCB drilling)

Find critical area, find reference line, pixel to pixel scan for measuring VBmax from reference line Image registration, spatial transformation, image subtraction, similarity analysis Weiner filter, thresholding, detection and subtraction of worn and unworn profile in polar co-ordinate Weiner filter, shadow removing, canny edge detection, pixel counting Unsharp mask, manual measurement, histogram analysis, GLCM analysis, Fourier spectrum analysis, fractal analysis Resolution enhancement, averaging, segmentation Sobel filter, line interpolation, histogram transformation, morphological opening & closing, blob analysis, contour detection for measurement; NN for flank wear and breakage classification Low pass filter, cropping, histogram stretching, manual segmentation, moment invariant methods (Zernike, Legendre, Hu, Taubin, Flusser), and linear discriminant analysis for classification Contour signature based on Canny edge detected image, kNN and MLPNN for classification

Canny edge detection, measurement of deviation from linearity of tool tip Canny edge detection, best fitting algorithm Spatial moment edge detection, edge sorting, B-spline smoothing, gaussian LPF, thresholding, morphological operation Accurate edge detection proposed, rotation, automated measurement

Resolution 0.996 mm/pixel; only applicable when no smearing in cutting plane image

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Table 1 (Continued ) Researcher

Illumination tem

Duan et al. [30]

Front lighting with LED

Xiong et al. [135]

Fluorescent high frequency linear light Dome light with low intensity back lighting Microscope

Otieno et al. [96]

Yasui et al. [138]

Lachance et al. [75]

Prasad and Ramamoorthy [100] Karthik et al. [61]

Prasad and Ramamoorthy [100]

Devillez et al. [24] Dawson and Kurfess [22] Wang et al. [130]

sys-

Fibre optic guided light with beam splitter White light Automatic focusing at various height (interpolation and search technique for improving accuracy) Stereo vision technique using law of triangulation

White light interferometer White light interferometer LCD projector for fringe creation on rake surface

Image processing

Type of tool wear measurement

Machining

Remarks

Histogram generation, level set based contour segmentation, histogram based contour segmentation, fusion of both segmentation, wear measurement Variational level set based segmentation, no need for reinitialization of zero level set Histogram equalization, Gaussian filtering, XOR operation for edge detection Thresholding, edge detection to segment the wear flats from its background Thresholding, region growing

Flank wear detection for progressive machining

Micro drill-bit (for PCB drilling)

Capable to remove the noise due to smearing; More computation time

Tool wear area

Milling inserts

No measurement of flank wear width

Micro-Milling tool

No measurement of wear

Grinding wheel wear

Grinding

Progressive wear of grinding wheel

Grinding

Histogram, GLCM and fractalbased texture analysis Image consolidation, median filtering, thresholding, laplacian contour detection, edge linking, dilation, chain coding, MLPNN for classification

Progressive wear of grinding wheel Flank and crater wear (depth) measurement and classification

Grinding

Stereo image processing for getting the 3D map of crater, MLPNN

Flank wear and crater wear prediction and progressive wear measurement

Turning inserts

White light interferometry by automatic and varying focusing Volume reduction measurement of tool from fusion of CAD model and surface profile 3D reconstruction using phase shifting method from 4 fringe patterns with 4 phase shifting angle

Crater depth measurement Crater depth measurement

Inserts

Accuracy is low, possibility for detection of false wear flats Morphological operations will be lead to more accurate segmentation Simple, faster but less accurate 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 Less accurate technique for crater depth less than 125 mm; no technique to reduce the noises from dirt, dust, oil etc. difficult for grooved inserts Difficult to measure grooved inserts Difficult to measure grooved inserts

4 parameters of crater wear measurement

Inserts

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 through indirect TCM techniques using image processing of machined surface images. 5. Indirect TCM techniques using image processing Diverse properties play an important role in the surface finish 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

Turning inserts

Inserts

Difficult to measure grooved inserts

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 soft materials 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 techniques take measures faster, as images are captured in almost no time and so they can be implemented in the machine. According to this, 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

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processing can extract surface finish descriptors from images of machined surface textures. There are mainly two techniques for tool condition monitoring from the images of the machined surface: online and offline. In online method, images of just machined surfaces are captured using CCD or CMOS camera. Online techniques are mainly useful for long and heavy parts. In offline method, surface images are taken after finishing a number of components. Generally, small and lightweight parts are measured using offline techniques. Some researches on online and offline methods are discussed in the following sections. 5.1. Online techniques Gupta and Raman [42] measured the surface roughness of preturned cylindrical bar utilizing the images of the laser scatter pattern developed on the turned surface image, when the bar was rotating with speeds ranging from 140 to 285 r.p.m. They extracted first order statistical texture descriptors based on the grey level histogram of images. They have also concluded from their study that the ambient lighting and the speed of rotation were not affecting the extracted surface roughness. However, there was no correlation study of vision-based surface finish descriptors with the stylus-based surface roughness and progressive flank wear width. Ho et al. [44] did turning experiments in various feed, cutting speed and depth of cut combinations and simultaneously captured the machined surface images. Then they found out the grey level average (Ga) values of those images. After that adaptive neuro-fuzzy inference system (ANFIS) was applied with inputs as feed, cutting speed, depth of cut, Ga and output was average surface roughness (Ra). At the time of machining the Ra values were also measured from the machined surface with the help of a stylus instrument. The maximum prediction error of this process was 4.55%. However, only the grey level value, which is a first order statistical texture descriptor, of the surface image has been determined in this regard and no other higher order statistical descriptors has been extracted in their research. [119] took the images of turned surfaces at 57 different cutting speed, feed and depth of cut combination by a camera and then they calculated a parameter (Ga, arithmetic average of grey level) from those images. At the time of experiment, surface roughness (Ra) were also measured. Afterwards, cutting speed, feed, depth of cut and Ga were used as inputs in a polynomial network with self organized adaptive learning ability to predict the surface roughness. They found a good correlation between predicted and measured surface roughness with maximum 14% error. As an improvement of the previous work, Lee et al. [77] utilized both spatial and frequency domain properties of machined surface images without considering the machining conditions as the inputs of an abductive network to predict the surface roughness. They have considered the frequency co-ordinates, maximum eigen value of the covariance matrix of normalized power spectrum and the standard deviation of grey level as the inputs of abductive network. They have considered both the spatial and frequency domain properties of image texture for their analysis. Lee et al. [79] had further improved their model to predict the surface roughness from the image texture descriptors namely, spatial frequency, arithmetic mean grey level and standard deviation of grey level by using ANFIS. They achieved lesser deviation between predicted and measured surface roughness (maximum 8%) compared to the polynomial network technique. It can also be observed from their results that error is less for high surface roughness values. However, these methods have been applied on turning operation with only one combination of cutting tool and workpiece material. Also it has not been carried out for progressive wear monitoring. Akbari et al. [3] predicted the surface roughness of milled surfaces by using four texture descriptors, namely, arithmetic mean,

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standard deviation, average surface roughness and root mean square surface roughness based on grey level histogram of machined surface images as inputs to a multi layer perceptron neural network (MLPNN). Though an entire surface area has been evaluated to get the more accurate estimation but no quantitative error estimation with respect to the stylus based surface roughness or tool wear has been reported by them. Narayanan et al. [91] presented a genetic algorithm based EHW (evolvable hardware) chip for noise removal from the milled surface images captured by CCD camera. The surface image has been enhanced by 62.5% using their system and then the Ga value has been adopted as visionbased surface roughness. This technique can be used to evaluate the surface roughness of machined surface. However, an ANFIS programme may be incorporated to their hardware for more accurate prediction of surface roughness. Sarma et al. [110] turned a glass fibre reinforced plastic (GFRP) composite hollow bar and measured Ra and Ga values, simultaneously for each experiment during machining. After that a correlation has been achieved from those two values by linear regression analysis. Also a normalized power spectrum was obtained from the experimental images and the power spectral density was reducing with the improvement of surface finish for increasing cutting speeds. However, no explanation about the blurring effect due to the capturing of images during machining was present in their work. Jian and Jin [55] introduced a fast online surface texture analysis method to characterized the machined surface images with straight feed marks. They first binarize the machined surface images and then converted all the pixels along a vertical line into 1 if the number of 1-valued pixels in that vertical line is more than 50% of total number of pixels along that line. Then they calculated the width between two consecutive white lines and taken the average of all texture width in an image. This texture width was characterized as a roughness descriptors according to them. But their method is very much crude and less accurate method. Palani and Natarajan [97] did an online prediction of surface roughness values using cutting speed, feed, depth of cut, major peak frequency, principal component magnitude square, Ga as the input to a BPNN in end milling application. The prediction error between the predicted and stylus based surface roughness was 2.47%. However, their technique can also be implemented for progressive wear monitoring. Kassim et al. [67] turned AISI 1045 and AISI 4340 workpiece materials by coated and uncoated carbide inserts until the inserts reached to a catastrophic failure. Time to time, the images of the surface textures of machined surfaces were captured by a CCD camera with high magnification lens and the wear value of the inserts were taken after each pass. Then the surface texture images were processed using Sobel operation, thresholding operation and column projection technique. The column projection technique was used to normalize the image and to reduce the effect of nonuniform illumination. As a result they got a uniform pattern for machined surface machined by a sharp tool and irregular pattern for machined surface machined with a dull tool. On the other hand, gradient images (after Sobel operation) were analyzed by run length statistics approach with six parameters. Then two sets of machined surfaces (in set A, AISI 1045 workpieces were turned at the feed rate of 0.4 mm/rev and at a cutting speed of 220 m/min and in set B, the cutting speed was selected at 120 m/min to turn AISI 4340 workpieces with a feed rate of 0.3 mm/rev) were analyzed by the above mentioned methods and those sets were clearly classified. However, no correlation study with surface roughness has been performed by them. Mannan et al. [87] did image and sound analyses and combined the features extracted from both analyses to train a radial basis function neural network (RBFNN) for predicting different states of the tool flank wear corresponding to the applied features. Also flank wear was measured using an optical microscope for validity check. They

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tried to monitor the condition of a sharp, a semi-dull and a dull 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 3–5. 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 inhomogeneous illumination of the texture images through an excellent way. However, they did not get any systematic trend of variation between image texture parameters and machining 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 much important to get the accurate results as the distribution of feed marks are varying with 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 improvement of the previous technique, Dutta et al. [31] has been proposed a novel technique to find the optimum pixel pair spacing parameter to get an accurate result by texture analysis of machined 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 on the 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 HMM model for classifying four distinct 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 highhardened 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 He–Ne laser and captured at different angle of illumination. Then a correlation between 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 can be used 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 wavelet packet 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 the distribution 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 greylevel 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 visual judgement 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 and Huynh [20] increased the sophistication of the analysis by applying a statistical texture analysis on the optical Fourier transform pattern created on 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 create a diffused pattern 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) He–Ne laser light from ground surface. Then the pattern was analyzed and characterized using blob area, thresholding and hence correlated to the surface roughness. But the

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result in this technique is sensitive to grazing angle of lighting system. That might be eliminated by using any filtering technique to remove the inhomogeneous illumination. As noted by Elbestawi et al. [33,107], the conventional roughness measure (i.e. Ra), when plotted as a function of the distance, the tool had travelled on the part (or cutting time), undergone a complex evolution. They showed that Ra increased steadily with cutting time but then dropped markedly as the tool showed marked wear. The drop in Ra at long distances was due to heating and ductile surface deformation by the worn tool. Therefore, Ra alone was not a reliable tool wear indicator, even though a stylus profiler measurement indicated an acceptable Ra value. These two phenomena could generate a surface of incorrect form and texture. Ramamoorthy and Radhakrishnan [103] and Kiran et al. [71] had utilized the grey level intensity histogram for establishing some features for roughness evaluation of ground, milled and shaped surface images but they did not correlate those parameters with tool wear or profiler-based surface roughness. Wong et al. [134] used a 0.8 mm-diameter 5 mW He–Ne laser beam and focused it on the turned surface to get a laser scatter pattern. Then the mean and standard deviation of the captured laser scatter pattern image were calculated. But they did not show any definite correlation between the measured surface roughness of the machined surface and the intensity distribution of the scattered light pattern. Although, they found quite good correlation between tool wear and intensity distribution of the scattered light pattern for most of the work pieces, they did not show any systematic variations of image features with machining time. Younis [140] analyzed the scatter pattern created by white light on the ground surface and derived a vision-based surface roughness parameter. They computed the vision-based surface roughness parameter based on the squared difference of a pixel value with its 8-neighbourhood. They studied the correlation of stylus-based surface roughness and vision-based surface roughness for tool steel, copper and brass material and found the linear correlation coefficient ranging from 0.79 to 0.92. They have concluded that the correlation were varying for different material depending on different modes of tearing and fracture for grinding of different materials. However, more experiments were needed with different cutting conditions for the establishment of their technique. Whitehead et al. [133] compared contact and laser stylus methods for roughness measurement. Kumar et al. [72] magnified original images of shaped, milled and ground surfaces using cubic convolution interpolation technique and enhanced the edges using a linear edge crispening algorithm. Based on the surface image features, a parameter called Ga was estimated using regression analysis, for the original images and for the magnified and enhanced images. Finally, a comparison has been carried out to establish a correlation between magnification index, Ga and surface roughness. However, more number of image features might be evaluated to get more accurate results. Dhanasekar and Ramamoorthy [27] applied a geometric search technique for edge detection following a pre-processing the machined surface images of shaped, milled and ground parts. A good correlation has been encountered between the surface roughness (Ga) obtained by the vision-based system and by the stylus-based surface profiler. However, the correlation of the vision-based surface roughness with progressive tool wear has not been addressed in their work. Khalifa et al. [69] used magnification, edge enhancement, statistical and texture analysis of turned surface images to detect chatter. Those images were enhanced using composite Laplacian filter. After that Ga, histogram mean, standard deviation, variance were evaluated from filtered images. Subsequently, the GLCM analysis of enhanced images were also performed. Energy, entropy and inertia features were extracted from GLCM to discriminate chatter-rich and chatter-free images. Al-kindi and Shirinzadeh [8]

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proposed a method named intensity–topography compatibility (ITC) for characterizing the image data by three components, namely, lightning, reflectance and surface characteristics. They extracted the value of the all surface roughness parameters viz. average roughness, root mean square roughness, maximum peak to valley, maximum valley depth, maximum peak height, skewness, kurtosis etc. from grey level histogram. However, no wear or surface roughness correlation study has been performed in their technique. Elango and Karunamoorthy [32] studied the variation of grazing angle of a diffused light on face turning specimen turned at different striation angle with design of experiment using Taguchi’s L9 orthogonal array and analysis of variance technique (ANOVA). They considered the Ga value as a texture descriptor and find a 758 optimum grazing angle and 908 striation inclination to achieve accurate Ga. However, this method can suitably be applied for progressive wear monitoring purpose. [28] enhanced the resolution of the ground and milled surface image by using projection on convex sets (POCSs) algorithm. Then they extracted three texture descriptors using frequency domain based and histogram based texture analyses from the reconstructed image. Finally, they predicted the surface roughness using group method of data handling (GMDH) and compared those predicted values with stylus based surface roughness. However, no prediction error had been reported in their work. Zhongxiang et al. [143] captured images of plain ground, plained, plain milled and end milled specimens using a digital camera and then those images were pre-processed by median filtering, greyscale equalization and histogram conversion amplification methods. The image data were analyzed by normalized cross-correlation and surface fitting techniques by using CAS software. They extracted four features, namely, mean, standard deviation, root mean square value (3D) and kurtosis (3D) from those pre-processed images and found a correlation between the surface roughness (obtained from stylus measurement) and those extracted features. Texture analysis of higher order statistics may produce better and robust results in their technique. Dhanasekar and Ramamoorthy [26] pioneered to capture the moving machined surface images of milled and ground specimens and then deblurred those images using Richardson– Lucy restoration algorithm. Those deblurred images were preprocessed to compensate the inhomogeneous illumination. Afterwards, the spatial frequency, arithmetic mean value and standard deviations were extracted as texture features. An artificial neural network (ANN) was used with these three features as inputs to predict the vision based surface roughness. Then they plotted predicted result with experimental result and got coefficient of determination (R2) values of 0.923 and 0.841 for milling and grinding, respectively, for deblurred and restored images. Also they showed that the R2 values of restored images were much better than R2 values for non-restored images. An online tool condition monitoring using laser vibrometer and CCD camera has been performed by Prasad et al. [101]. They extracted the acousto-optic signal of machining and the 3D surface roughness of machined surface images using a surface metrology software, namely, TRUEMAP for progressive wearing of cutting tool in face turning operation. However, the computation time required for surface roughness measurement has not been mentioned in their work. Gadelmawla [36] did an automatic surface characterization by calculating the grey level co-occurrence matrix of 10 types of machined surface images with varying GLCM parameters, distance and orientation. They have also extracted four features from the GLCMs and observed that most of the features, except standard deviation of the GLCM, were behaving differently with respect to sensitivity for rough (turned) and smooth (lapped) surfaces. Gadelmawla et al. [37,38] developed a reverse engineering software for detecting and predicting the machining conditions, cutting speed, feed rate and depth of cut, from the machined

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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. Gadelmawla [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 measured 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, central power spectrum percentage and monotonically increasing trends for average power spectrum with increasing values of measured surface roughness for both the shaped and milled parts. Furthermore they used two artificial neural network (ANN) techniques for classification of roughness features in fixed and arbitrary orientations 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 Gabor function, 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-homogeneous 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 (He–Ne 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 Butterworth 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 directions were 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 preprocessing of speckle images. They were then compared to stylus values (Ra). It was found that autocorrelation parameters after preprocessing had a better correlation (i.e. higher correlation coefficient) with the average surface roughness (Ra) measured for the milled and ground components. To get more 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 spacefrequency representation of surface texture using frequency normalized wavelet transform (FNWT) and extracted some surface finish descriptors. 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 decide about 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 imageprocessing algorithms, namely, analysis of the intensity histogram, 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 progressive wear of face milling tool but it was 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 similarities in the image structure. The absolute value of illumination intensity 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.

Please cite this article in press as: Dutta, S., et al., Application of digital image processing in tool condition monitoring: A review. CIRP Journal of Manufacturing Science and Technology (2013), http://dx.doi.org/10.1016/j.cirpj.2013.02.005

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Table 2 Indirect TCM techniques based on image processing. Researcher

Illumination system

Image processing algorithm

Applied in

Remarks

Wong et al. [134]

He–Ne laser

Turning

Offline; no study on correlation and progressive wear

Gupta and Raman [42]

HeNe laser, circular variable attenuator

Mean and standard deviation of laser pattern created on machined surface Histogram based 1st order statistical texture analysis

Turning (moving and static condition); surface roughness measurement

Tarng and Lee [119]

2 Light sources situated at an acute angle with the axis of workpiece

Turning; Ra prediction

Ho et al. [44]

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 Ga as input, Ra as output) Determination of Ga, ANFIS (feed, speed, depth of cut and Ga as input, Ra as output)

Online; no correlation study between vision-based surface roughness and stylus-based surface roughness and progressive tool wear; no discussion about blurring due to movement Online; prediction error (max) = 14%; extraction of 1 descriptor only; no prediction of tool wear

Lee et al. [78]

A diffused blue light in 458 inclination

Lee et al. [79]

A diffused blue light in 458 inclination

Akbari et al. [3]

Scattered pattern of light

Narayanan et al. [91]

An evolvable hardware

Sarma et al. [110] Palani and Natarajan [97] Kassim et al. [67]

Mannan et al. [87]

Kassim et al. [64]

Kassim et al. [63]

Datta et al. [21]

Diffused light

Datta et al. [31]

Diffused light

Standard deviation of grey level, two frequency domain parameters and abductive network (input as 3 texture descriptors, output as Ra) Standard deviation of grey level, two frequency domain parameters and ANFIS (input as 3 texture descriptors, output as Ra) Histogram based 1st order statistical texture analysis (four descriptors) & MLPNN Image enhancement, determination of Ga, genetic algorithm Determination of Ga, frequency domain analysis Frequency and spatial domain based texture analysis, BPNN Sobel operation, thresholding, column projection (CP) (applied on thresholded images), run-length statistics (RLS) (applied on grey level images) Sobel operation, thresholding, CP, RLS, extraction of AE parameters using wavelet analysis, RBFNN for flank wear prediction Canny edge detection, connectivity oriented fast Hough transform, MLPNN for FW prediction Compensating inhomogeneous illumination compensation, comparison of CP, connectivity oriented fast Hough transform and RLS, Mahalanobis distance classifier for classification of sharp and dull tool GLCM technique

Turning, Ra prediction

Turning, Ra prediction

Online; prediction of Ra using ANFIS prediction error (max) = 4.55%; extraction of 1 descriptor only; no prediction of tool wear Online; max prediction error = 14.96%; no prediction of tool wear

Turning, Ra prediction

Online; max prediction error = 8%; no prediction of tool wear

Milling, Ra prediction

Online; No quantification of prediction error; No prediction of tool wear Online; no quantification of prediction error; no prediction of tool wear No study for progressive wear monitoring No study for progressive wear monitoring Online; Progressive wear monitoring; classification between sharp tool and dull tool in various machining; no correlation study with Ra Online; monitor sharp, semi-dull and dull tool; no quantification of prediction error

Milling; Surface roughness measure Turning GFRP composite with PCD tool End milling, Ra prediction Turning; progressive wear monitoring

Turning; progressive wear monitoring

Turning, end milling, face milling; progressive wear monitoring Turning; progressive wear monitoring and classification

Online; no quantification and comparison of prediction error

Turning; progressive wear monitoring

Online; RLS was selected as the best technique depending only on a single cutting condition; classification between two wear state only; more experimentation needed

GLCM technique with optimized pixel pair spacing (pps) parameter

Turning; Progressive wear monitoring

Kassim et al. [66]

Fractal with HMM

End milling; Classification

Kassim et al. [65]

3D fractal with HMM

Kang et al. [60]

Fractal; progressive variation study with surface roughness and tool wear Wavelet packet decomposition

End milling; classification of 4 states of wear High speed end milling (with coated carbide)

Online; extraction of best feature depending only on a single cutting condition; No optimization of GLCM parameters Online; Optimization of pps developed; applicable for any periodic textures; no study to monitor coated carbide tool Online; No estimation of classification error Online; no estimation of classification error Online; no estimation of correlation parameter

Turning; progressive wear monitoring

Online; no correlation analysis with tool wear

Li et al. [81]

Diffused light

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Table 2 (Continued ) Researcher

Illumination system

Image processing algorithm

Applied in

Remarks

Hoy and Yu [45]

Diffused white light

Histogram analysis, 2D FFT analysis

Turning, milling

Cuthbert and Hynh [20]

He–Ne laser, spatial filter, beam splitter and mirror

Histogram based 1st order statistical texture analysis

Grinding

Jetley and Selven [54]

He–Ne laser

Blob analysis, thresholding

Grinding

GLCM analysis

Grinding, shaping, milling

Frame averaging; low pass filtering; 2nd order co-occurrence statistics; three lighting methods were compared for rough, medium rough and smooth images Neighbourhood processing

Grinding, milling, shaping

Offline; no progressive wear monitoring Offline; complex attenuator; difficult to implement for high roughness values; no progressive wear monitoring Offline; no progressive wear monitoring Offline; no correlation parameter study Offline; mainly the comparison of three types of lighting; no roughness evaluation

Cubic convolution interpolation, linear edge crispening, Determination of Ga Edge enhancement, magnification, statistical texture analysis (1st and 2nd order), calculation of Ga value Comparison between two lighting models viz. intensity topography compatibility and light diffused model, extraction of optical surface roughness parameters from 1st order statistics Determination of Ga, Taguchi’s orthogonal array and ANOVA POCS for reconstruction of high resolution image, frequency domain and histogram based texture analysis, GMDH Median filtering, histogram conversion, histogram homogenization, calculation of 3D roughness Richardson–Lucy algorithm for deblurring, frequency and spatial domain based texture analysis, ANN

Shaping, milling, grinding

Face turning

Ramamoorthy and Radhakrisnan [103] Kiran et al. [71]

Younis [140]

Diffused light; light sectioning; phase shifting with grating projection White light

Kumar et al. [72]

Khalifa et al. [69]

Al-kindi and Shirinzadeh [8]

Ambient light

Elango and Karunamoorthy [32] Dhanasekar and Ramamoorthy [28]

Diffused light at different grazing angle White light

Zhongxiang et al. [143]

Stereo zoom microscope, halogen lamp

Dhanasekar and Ramamoorthy [26]

Grinding (different material)

Chatter detection in turning Face milling

Milling, grinding (Ra prediction)

Milling, grinding, Ra prediction

Correlation coefficient 0.923 and 0.841 for milling and grinding, No correlation study with progressive wear No optimization of pps value, No correlation study with progressive wear No optimization of pps value, No correlation study with progressive wear No optimization of pps value, No correlation study with progressive wear No correlation study with progressive wear No mention of success rate; no progressive wear or surface roughness study No correlation study with progressive wear

GLCM, study the effect of pps

Face turning

Gadelmawla et al. [37,38]

Microscope

GLCM

Gadelmawla [39]

Microscope

GLCM

Milling, Reverse engineering for cutting conditions Face turning, Correlation with Ra

Tsai et al. [123]

Fluorescent light source

Fourier analysis, ANN

Shaping, Milling

Gabor filtering, classification of defective and non-defective parts,

Milling

Speckle pattern, Butterworth filtering, Fourier analysis, Autocorrelation Frequency normalized wavelet transform, discriminant and cluster analysis Frequency normalized wavelet transform, Haar wavelet for reduction of inhomogeneous illumination GLCM, amplitude varying rate method, RLS

Grinding, milling

Josso et al. [57]

Josso et al. [56] Niola et al. [94] Raman andRamamoorthy [104]

He–Ne laser

No correlation study with progressive wear No prediction error analysis, no correlation study with progressive wear No correlation study with progressive wear

Microscope

Dhanasekar et al. [25]

Discrimination between chatterrich and chatter-free process from surface images No correlation study with progressive wear

Paning, plain milling, end milling, grinding

Gadelmawla[36]

Tsai and Wu [124]

Offline; coefficient of variation 8.6% Coefficient of determination (R2) 0.79–0.92; no progressive wear study Offline; no progressive wear monitoring

Classification of ground, milled, cast surfaces etc.

No correlation study with progressive wear

Form, waviness, roughness measurement Milling, grinding, polishing Classification of ground, milled, shaped surfaces

No correlation study with progressive wear No extraction of surface finish parameters No correlation study with stylus based surface roughness; no progressive wear study

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17

Table 2 (Continued ) Researcher

Illumination system

Image processing algorithm

Applied in

Remarks

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

DCT, Laws filter, Gabor filter, GLCM, Shape features, SVM with RBFNN kernel First order statistical texture analysis, GLCM, Laws method, k-NN classification Thesholding, component labelling

Defect detection and classification in ground and polished surfaces Turning

Comparison between histogram analysis, frequency domain analysis and texture segmentation; no correlation analysis of vision-based surface finish with tool wear 82% success rate using the combination of Gabor filter and GLCM with SVM No progressive wear study

Zhang et al. [142]

Alegre et al. [4]

DC regulated light with SCDI

Nakao [90]

Fibre optic light

Yoon and Chung [139]

Halogen (front light) LED (back light)

Sharan and Onwubolu [114]

High intensity spot lighting

Edge detection (burr width measurement), Shape from focus (burr height measurement) Burr profile measurement

Drilling burr measurement Micro-drilling

3% and 2% error in measuring burr thickness and height 0.1 mm resolution; less than 0.5 mm accuracy

Milling

2.2 mm resolution

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

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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 samples were required to proof the suitability of their method. Quality of honed surfaces was also determined by Leo´n et al. [80] using image processing technique. He quantified the groove textures and defects 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 conventional image processing techniques such as the binary image processing, the noise reduction and the labelling were applied to measure image data. Here burr height and thickness were measured from the processed image using co-ordinate data. Yoon et al. [139] used edge detection algorithm to measure hole 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 to normalize 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 different machining 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 automation capability 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 machined surface quality. Future research should be aimed to develop a robust system (including lighting, camera and faster algorithm) for real time tool condition monitoring technique. However, a noncontact and less costly tool condition monitoring technique can be established with the help of digital image processing techniques through robust machine vision system. Some established observations from the review are discussed below:

1. Diffused lighting system (such as LED light, fibre optic guided light, white light) and a high speed CCD camera should be utilized to enhance the image capturing capability in real time monitoring. 2. In direct TCM technique, image pre-processing, image thresholding, edge detection and morphological operation along with texture analysis technique can be used for getting the faster outputs. 3. Gaussian filtering along with illumination compensation technique has a good impact in image pre-processing operation. 4. Canny edge detection method can be a fast and strong edge detection technique for direct TCM. 5. Textural analysis (in both spatial and frequency domain) can be a strong and fast technique for having a good correlation with the surface roughness and tool flank wear data in case of indirect monitoring technique. 6. Pattern classification should be used to classify between sharp, semi-dull and dull tools in indirect TCM. 7. ANFIS (Adaptive Neuro Fuzzy Inference System) is a very robust tool for accurate prediction of tool wear in indirect TCM. 8. Crater wear can be measured using stereo imaging algorithm with a single camera. 9. Faster detection of the effect of vibrations, machine noise, cutting tool condition, etc. can be possible from indirect TCM in comparison with direct TCM. 10. Systematic variation of image parameters with machining time should be studied and established to get the full benefit of machine vision based tool condition monitoring approach.

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