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An automated flank wear measurement of microdrills using machine vision
Journal of Materials Processing Technology 180 (2006) 328–335
An automated flank wear measurement of microdrills using machine vision J.C. Su ∗ , C.K. Huang 1 , Y.S. Tarng 2 Department of Mechanical Engineering, National Taiwan University of Science and Technology, 43, Keelung Road, Section 4, Taipei 106, Taiwan, ROC Received 10 March 2006; received in revised form 8 June 2006; accepted 6 July 2006
Abstract The objective of this study is to develop an automated flank wear measurement scheme using vision system for a microdrill. The images of worn-out microdrills were captured after the hole-drilling tests on a 10-layered printed circuit board (PCB). The models were evaluated and validated based on the acquired image with a computer-based acquisition system. Edge detection was employed to extract the boundary with a pair of edge points, including both raising and falling edges, to compute the height of the cutting plane. The flank wear area, average flank wear height, and maximum wear height are computed by using this approach to evaluate the tool life. Experimental results show that the proposed scheme is reliable and effective for the automated frank wear measurement of microdrill in PCB production. © 2006 Elsevier B.V. All rights reserved. Keywords: Microdrill; Machine vision; Flank wear measurement; Edge detection; Image processing; PCB
1. Introduction Measuring the wear of the microdrill (diameter of 0.3–0.02 mm) is important in a printed circuit board (PCB) production. While a drill begins to wear, the cutting forces increases and the temperature of the drill raises, which speeds up the physical and chemical reactions associated with drill wear, and causes rapid deterioration of the drill quality [1]. In PCB manufacturing, a worn-out microdrill damages the quality of the surface finish and the dimensions of the drilled hole. Tool wear not only reduces the part geometry accuracy directly but also increases the cutting forces drastically. The diagram of the microdrill and its characteristics are shown in Fig. 1. The cutting plane (also called first facet or lip relief plane) is consisted of four edges, of which the cutting lip and chisel edge are two important cutting edges on the first facet. The centering point is the first contact point of a microdrill with the material surface, and cutting lips are the major cutting edges for material removal. Chisel edges are comprised of two intersecting planes that define two primary cutting edges of the microdrill. They remove the material ∗
Corresponding author. Tel.: +886 2 2737 141x7348; fax: +886 2 2737 6460. E-mail addresses: op [email protected], [email protected] (J.C. Su), [email protected] (C.K. Huang), [email protected] (Y.S. Tarng). 1 Tel.: +886 2 2737 141x7348; fax: +886 2 2737 6460. 2 Tel.: +886 2 2737 6456; fax: +886 2 2737 6460. 0924-0136/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2006.07.001
by extrusion and cut at a highly negative rake angle. Moreover, for smaller diameters, various drilling parameters, such as web thickness, point angle, spindle speed, and feed, need to be further analyzed. The effect of retraction rate for the influence on cutting life is also concerned. Many researchers have used various sensors, acoustic emission (AE) [5], dynamometers [6], vibration [7], ultrasonic vibrations [8], and motor spindle speed and power and power consumption [14] to monitor drill wear. Lin and Ting [3] established the relationship between force signals and flank wear and other cutting parameters when drilling a copper alloy. Ertunc and Loparo [4] developed a decision fusion center algorithm, which combines the outputs of the individual methods to make a global decision for the wear status of the drill. Li et al. [10] presented a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. Several researchers have examined the usage of machine vision for the measurement of tool wear. For example, Nickel et al. [11] presented the machining performance of HSS twist drills that were plasma-nitrided before applying PVD TiN coating, and evaluated and compared it with that of commercially TiNcoated drills. The optical methods were essentially off-line and involved the examination of geometry using a specially adapted toolmaker’s microscope for the digital processing of the tool tip image. Pedersen [12] reported that the determination of the wear land is usually based on the high intensity of the reflected light from the wear land. Jeon and Kim [13] employed a suit-
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Fig. 1. Schematic diagram of a microdrill.
able thresholding technology to convert the captured grey image into binary image. Subsequently, the amount of flank wear was captured using the binary, where the wear land is white and its background is black. Hazra et al. [2] used three silhouette images of flank surface to measure the geometry of drill points of drills, and fitted the coordinates to a mathematical model of the drill point geometry, which was optimized by genetic algorithm, to estimate five geometry parameters. Based on the above review, previous researches using various sensors mainly emphasized on the variations of monitors on the tool immediately during the machining process. Other researchers using different algorithms based on vision system measured the degree of tool wear, but they focused only on the measurement of the large-size of drill wear (d > 0.5 mm). A few micrometers of the wear error could affect the quality of the microdrills. Therefore, the primary objective of this research presents a machine vision system to develop an innovation method for measuring flank wear of microdrills and to meet the demand for analyzing the relationship between drilling parameters and tool life. The remainder of this thesis is organized as follows: the experimental set-up for measuring tool wear is established in Section 2. Section 3 describes measuring wear of microdrill approach using two-dimensional edge diction lines. In Section 4, the measured results are presented to confirm the effectiveness of the
proposed method. Finally, Section 5 summarizes the conclusion of this study. 2. Experimental set-up Machine vision is the direct method used for tool wear measurement. The experimental of the present study is comprised of two major procedures: (1) several PCB hole-drilling tests with given diameter of 0.2 mm, and the images of flank wear are captured and (2) a machine vision system based on tool microscope to measure the flank wear. The system is detailed as follows.
2.1. Procedure I Several hole-drilling tests were carried out using drilling machine (XL 1-24 Lin) with tungsten carbide microdrills (diameters of 0.2 mm) on a 10-layered print circuit board machining. The drilling parameters include a web thickness of 0.32%, point angle of 135◦ , spindle speed of 180,000 rpm, feed of 20 m/rev, and retraction rate 10 m/min. In the PCB hole-drilling tests, the images of the flank wear on the cutting plane were captured by tool microscope after hole drilling of 1000, 2000, . . ., 9000 hits, respectively. The captured images on different periods of flank wear further measured and analyzed.
2.2. Procedure II Fig. 2 shows the set-up of the vision system for measuring flank wear. The system components include a Pentium IV 2.3 GHz personal computer, frame grabber (NI-1407), CCD (Teli CS8530), toolmaker microscope, circular lighting, and drill holder. The images captured by the CCD camera were transformed
Fig. 2. Experimental set-up for the measuring flank wear using the toolmaker microscope.
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into an eight-bit monochrome format by using the grabber and then stored to the memory of the computer. Each digital image of the microdrill is consisted of a 640 × 480 pixel array with a grayscale value, ranging from 0 to 255. By operating on these arrays of pixels, the desired features and information from the images were extracted. In addition, since microdrill is in three-dimensional geometry, the cutting plane with an angular deviation would have cosine error. Therefore, the drill holder is designed to make cutting plane perpendicular to an observing direction in order to eliminate the cosine error.
3. Measuring method Machine vision is the direct method used for tool wear measurement. The extent of flank wear can be measured as the distance between the top of the cutting edge and the bottom of the area where flank wear occurs. A simple but effective method of measuring flank wear is using edge detection to identify the width change of the cutting plane. The image can be divided into two parts: object (cutting plane including noise) and background. A rising edge is characterized by an increase in the grayscale value as the edge detection crosses the object; a falling edge is characterized by a decrease in the grayscale value as the edge detection is away from the object. Therefore, an effective alternative to measure the cutting plane width is to represent each column of the image by a sequence of lengths that runs the rising and falling edges successively. The discontinuities are typically associated with abrupt changes in pixel intensity values that characterize the boundaries of the objects in a scene. Many edge detection methods have been proposed, such as Sobel operator, Robert operator, Prewitt operator, Laplacian operator, and Marr-Hildreth operator. An automated visual measuring system often operates in constrained and controlled environments. Therefore, the object’s position can be expected where edge detection is needed to extract feature from a captured an image. For this reason, the study has designed the following edge detection algorithm which can specify search region to analyze the pixels. This can help to reduce the amount of time spent on compute. 3.1. One-dimensional edge detection Edge detection is one of the most widely used visual measurement tools. One-dimensional edge detection is introduced to identify and locate discontinuities in order to measure the flank wear of the cutting plane with in the pixel intensities of captured images. We used three parameters to define the edge: (1) filter width, (2) contrast, and (3) steepness, as shown in Fig. 3. They are defined as follows: (1) Filter width. It is the average number of pixels on each side of the edge by setting the filter with parameter “m”. Many factors could affect the value of pixels. For example, noise and dust could cause excessive noise interference or linear profile-intruding peaks. If the image contains too much noise, a large filter width is used to reduce the effect of noise along the profile. To search for more sensitive changes, it can be done by adjusting the filter width. (2) Contrast. It is the difference between the average grayscale values on each side of the given point. To find the edge,
Fig. 3. One-dimensional and two-dimensional edge detections.
the edge strength (contrast) is computed along the onedimensional pixel by pixel. If there is m number of pixels and the coordinates of a pixel value are f(xi , yj ), then the average value of m pixel values W can be calculated in the following equation: m f (xi , yj ) W = i=1 . (1) m After computing the average, the difference between these averages is computed to determine the contrast. If the contrast at the current point is greater than the preset value for the minimum contrast for an edge, the point is stored for further analysis. (3) Steepness. It is the average number of pixels at a certain pixel distance from each side of the given point. This number corresponds to the expected transition region in the edge profile, which marks the start and the end of the variation in grayscale values. If m is the filter width, W the average value of m pixels, C the contrast level, and S is the steepness, then: Step 1: Calculate W by averaging the number of pixels, m, at each analyzed point, S, is the steepness, and m and S are both presetting values m f (xi , yj ) W(k) = i=1 , m 2m+S f (xi , yj ) W(k + 1) = i=S+m , (k = 1, 2, 3, . . .), m (2) where k denotes the current iteration number, and W(k)and W(k+1) represent two consecutive steps along the line search in a generated sequence of computing the average of the number of pixels. Step 2: Calculate the difference in W to see if it is greater than the pre-setting contrast C by using the equation below: 1 if W ≥ C W = W(K + 1) − W(k) = , (3) 0 if W < C
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where W > C denotes a trigger that indicates a located edge, while W < C denotes no trigger indicating the absence of an edge. Moreover, W > C denotes rising edge, whereas W < C denotes falling edge. Step 3: The correct edge position should be within the range of S (see Fig. 3). While a trigger, the point is stored for further analysis. Starting from this point, successive points are analyzed until the contrast reaches a maximum value and then falls below that value. The point where the contrast reaches the maximum value is tagged as the start edge location. The value of the steepness, S, is added to the start edge location to obtain the end of edge location. Step 4: Repeat Steps 1–3 for all the points along the search line profile. This method is able to detect the edge by calculating the grayscale along the linear profile and analyzing the average grayscale values of these pixels. These average values are calculated with the number of pixels of the filter width. A larger filter width indicates the decreased noise effects on the result of the edge detection. In contrast, a smaller filter width indicates a more sensitive effect. 3.2. Two-dimensional edge detection The edge detection algorithm can be extended to a number of search lines to detect edge variation in two-dimensional search T =
Fig. 4. Standard circle with Dt = 0.7mm to be calibrated using the least square method.
(a) Acquire the images of microdrill. (b) Spatial calibration. Spatial calibration is the process of computing pixels for real-world unit transformations. Applying the least square method to the coordinates of the circle gives the diameter of the object. The physical length of the object corresponding to each pixel can be obtained from the following equation, where T denotes the conversion factor:
The diameter of the standard circle in real-world unit Ds (mm) = . The measured diameter of the standard circle in the image unit Dd (pixel)
regions by defining the separation between the lines. Changes in grayscale value will provide information on the relative coordinates of the objects to be measured. Eq. (2) can be modified as follows: m n i=1 j=1 f (xi , yj ) W(k) = , m 2m+S 2n i=S+m j=n f (xi , yj ) W(k + 1) = , (k = 1, 2, 3, . . .), m (4) where n specifies the number of degrees that separates two consecutive search lines of a given length. In these edge detection variations, the two-dimensional search area is covered by a number of search lines which the edge detection is performed. The number of the search lines is controlled in the search region by separating the lines.
(5)
The calibration template is employed for calibration of a standard circle as shown in Fig. 4. (c) Rotate the image Angular deviations may occur when capturing the image of cutting plane. As seen in Fig. 5(a), the image is needs to be rotated at an angle θ so that the bottom of the cutting plane is perpendicular to the horizontal axis. Rotating the image involves the following procedures. 1. Find the reference line. Two-dimensional edge detection is employed to search the bottom edge of the cutting plane. A set of edge points is fitted into a reference line. The x coordinates of all the located edges can be written as x = {x1 , x2 , x3 , . . ., xn }, while all the y coordinates can be denoted as y = {y1 , y2 , y3 , . . ., yn }. Linear fitting of all coordinates is represented by the equation: F = mx + b, where F denotes the
3.3. Measuring procedures This study has applied the edge detection to extract the boundary and to measure the flank wear of the cutting plane. The measuring process is consisted of the following five steps:
Fig. 5. The images of cutting plane with flank wear. (a) Rotate the image to horizontal and (b) the search region is confined by the red rectangle to avoid noise effect in the measurement results.
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optimum value after fitting, m the slope, and b is the intercept. However, not all of edge points conform exactly to the robust fitting line owing to noise interference or discontinuous line profile. Therefore, in order to ensure that all the effective edge points obtained conform to a linear line, the mean square distance (MSD) is used to evaluate the fitting results as 1 (fi − yi )2 n n−1
MSD =
(6)
i=0
where fi = mxi + b with f denotes the functional value after fitting, m the slope, b the intercept, xi and yi being the coordinates, and n representing the number of data. 2. Calculate the angle θ between the reference line and the horizontal X-axis. 3. Rotate the angle θ. (d) Construct a search region To reduce the image processing time and memory requirement, the search region of two-dimensional edge detection is confined by standard contour (rectangle) to focus image processing and analysis on the part of the image. It is confined to cutting plane outlined by the red rectangle as seen in Fig. 5(b). (e) Determine the starting point for measurement The shape of the boundary of cutting plane can be described by using a pair of linked edges. Features of the worn micro-
Fig. 6. The contour of cutting plane. (a) Original countour of the cutting plane, (b) the worn-out of cutting plane, (c) original image with edge detection search lines and (d) the image of flank wear with edge detection search lines.
drills such as area and height of the worn area are measured to identify the tool wear. Measuring flank wear using a set of boundary points can be distinguished into three methods—measuring the flank wear area, the average wear height, and maximum wear height to evaluate the tool life shown in the following subsection.
Table 1 The relationship between the height of cutting plane and the number of hits (unit: m) Original d1 d5 d10 d15 d20 d25 d30 d35 d40 d45 d50 d55 d60 d65 d70 d75 d80 d85 d90 d95 d100 Area (m2 )
1000 hits
3000 hits
5000 hits
7000 hits
9000 hits
11000 hits
2 4 9 13 17 21 25 29 29 29 29 29 28 28 28 28 28 27 27 25 11
0 4 7 11 14 18 22 24 24 24 24 23 22 23 23 21 21 19 18 16 8
0 3 5 9 12 14 16 19 21 21 21 21 19 19 20 18 19 17 15 13 0
0 3 4 7 9 12 16 19 19 18 18 17 17 16 16 14 14 12 12 7 0
0 2 4 6 8 10 13 16 18 17 17 15 14 13 12 11 10 9 8 6 0
0 3 5 7 8 10 11 12 14 15 15 14 12 12 10 9 10 9 8 5 0
0 0 0 3 3 4 6 8 9 10 10 9 8 7 6 5 4 4 4 0 0
2309
1822
1513
1258
1044
927
501
(m2 )
0
487
796
1051
1265
1382
1807
Average wear height (m)
0
5
8
10
13
14
18
Max wear height (m)
0
10
13
18
19
21
24
Wear area
Note: The conversion factor T = 0.996 m/pixel; round towards nearest integer.
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3.4. Measuring the flank wear area The area of flank wear which compared to the area of cutting plane for the differences between worn-out and not worn-out can also be an index to evaluate tool life [9]. Suppose that a 2D contour of flank wear with 2k edge points is represented by the set {Pij = (xij , yij ) for i = 1, 2, 3, . . ., k and j = 1, 2}. Pij is a sequence of paired edge coordinates (x, y) in the image as shown in Fig. 6. Edge detection search line crosses the front contours and back contours of the image of cutting plane vertically to generate a sequence of paired edge coordinates. A set of scan lines start
333
from the lower-left point of corner to the upper-right of corner on the cutting plane. The length d between a paired of edge coordinates is written as di = Pi1 Pi2 = |Pi1 − Pi2 |.
(7)
Therefore, the widths n of the intervals approach zero (n refers to the width of two separated search lines), and an approximated area D of cutting plane can be written as follows: D=
k−1
di .
i=0
Fig. 7. Flank wear measurement results.
(8)
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The original area of cutting plane subtracts the area of cutting plane with worn-out is the flank wear area. The flank wear area can be written as follows: Dflank wear = Do − Dw ,
(9)
where Do represents the area of original image and Dw is the area of image of cutting plane with worn-out. It is the area of the accumulation of all line segments. The images of microdrills are captured and the boundary of the cutting plane is extracted using edge diction to reconstruct the cutting plane area from its line of plane integrals. 3.5. Measuring the average wear height VBave The basic concept is to calculate the distance of a pair coordinates namely the height of cutting plane along X-axis direction from the rising edge to the falling edge in a down-to-up scan of a column. Therefore, the average wear height VBave can be written as Dflank wear VBave = , (10) l where l is the number of scan lines with wear. Measuring the average wear height is also an approach to evaluate the tool life. 3.6. Measuring maximum wear height VBmax Maximum wear height VBmax can be written as follows: VBmax = max{dio − diw |,
i = 1, 2, . . . , k},
(11)
where do is the height of original cutting plane and dw is the height of cutting plane with cutting plane. A vision-based approach is presented to measure flank wear directly, which is defined by measuring the maximum distance between the cutting lip and the bottom edge of the wear land. 4. Measurement results and discussion Table 1 shows the measurement results of using the proposed edge detection to compute the height of cutting plane between a pair of edge points in drilling tests from 1000 to 11,000 hits. Three parameters of the wear (flank wear area, average wear height VBave , and maximum wear height VBmax ) were computed to evaluate the tool life and measure the degree of wear. The maximum wear height usually occurs at the corners of the cutting lips because these are the locations of the highest cutting speed. The second wear region occurred at chisel edge because it is the first contact point of a microdrill with the penetration resistance which causes wear. This method can measure the height of cutting plane change for each position. The images of cutting plane with flank wear, including the original image, were detected by using the edge detection with a pair of edge points, as shown in Fig. 7. The images were captured by using the same lighting condition, and the right image is the edge image which shows that the edge points with red mark overlapping the image were linked into the boundary of the cutting plane. Although the image of cutting plane undergone 6000 hits
Fig. 8. The height of the cutting plane change along X-axis in hole-drilling test.
contained excessive noise, the edge detector still detected a pair of edge points on the boundary of the cutting plane. The main reason is the edge detection is confined in a small rectangle to avoid the noise effect on the measurement results. Additionally, the parameter of edge detection and filter width could be set at a greater value to reduce the influence of rapid grey value change. In general, as the number of hole-drilling tests increases (wear time increases), the height of the cutting plane reduces gradually as illustrates in Fig. 8. As shows in Fig. 9, the computed flank wear area in Eq. (9) was included in the experiments of PCB hole-drilling tests. When the number of hits was added, the flank wear area is increased. First, there was a high wear rate in the initial stage of drilling. Next, the flank wear area rose with a nearly steady-state wear. Finally, a rapid wear rate was shown in the final stage until the microdrill reached catastrophic failure. The curves are very similar to the Taylor curve (VTn = C). The cutting speed is more quickly where the radius of the cutter is larger, and the speed of wear becomes faster. Moreover, the average wear height and the maximum wear height computed using Eqs. (10) and (11) are
Fig. 9. The curves of the flank area wear.
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on a 10-layered PCB. This method has not only measured the flank wear area, the average flank wear height VBavg and maximum wear height VBmax but also the height of cutting plane changed for each position. The wear curve of the experiments of PCB hole-drilling trials is very similar to the Taylor curve. The measuring time, including computing the flank wear for a microdrill, is less than 1 s. The proposed measurement system with a resolution of 0.996 m/pixel has successfully measured the frank wear of microdrill in PCB production. In order to extend to the measurement on microdrill of less than 0.2 mm for a longer period, a frame capture of 1024 × 1024 resolution can be employed together with suitable lens and lighting source. References Fig. 10. The curves of the average wear height.
Fig. 11. The curves of the maximum wear height.
plotted in Figs. 10 and 11, respectively. The results were similar as the above results of flank area wear. 5. Conclusions This study has proposed a new machine vision-aided system for measuring the flank wear in a PCB production, and proved the feasibility of measuring frank wear with the hole-drilling tests
[1] B.Y. Lee, H.S. Liu, Y.S. Tarng, An abductive network for predicting tool life in drilling, Ind. Appl., IEEE Trans. 35 (1) (1999) 190–195. [2] L. Hazra, et al., Inspection of reground drill point geometry using three silhouette images, J. Mater. Process. Technol. 127 (2) (2002) 169– 173. [3] S.C. Lin, C.J. Ting, Tool wear monitoring in drilling using force signals, Wear 180 (1) (1995) 53–60. [4] H.M. Ertunc, K.A. Loparo, A decision fusion algorithm for tool wear condition monitoring in drilling, Int. J. Mach. Tools Manuf. 41 (2001) 1347–1362. [5] C. Everson, S.H. Cheraghi, The application of acoustic emission for precision drilling process monitoring, Int. J. Mach. Tools Manuf. 39 (3) (1999) 371–387. [6] H.M. Ertunc, C. Oysu, Drill wear monitoring using cutting force signals, Mechatronics 14 (2004) 533–548. [7] T.I. El-Wardany, D. Gao, M.A. Elbestawi, Tool condition monitoring in drilling using vibration signature analysis, Int. J. Mach. Tools Manuf. 36 (6) (1996) 687–711. [8] S.R. Hayashi, C.E. Thomas, D.G. Wildes, Tool break detection by monitoring ultrasonic vibrations, Ann. CIRP 37 (1) (1988) 61–64. [9] C.N. Ramirez, R.J. Thornh, Automated measurement of flank wear of circuit board drills, ASME J. Electric Packaging 114 (1992) 93–96. [10] X. Li, S. Dong, P.K. Venuvinod, Hybrid learning for tool wear monitoring, Introduction J. Adv. Manuf. Technol. 16 (2000) 303–307. [11] J. Nickel, A.N. Shuaib, B.S. Yilbas, S.M. Nizam, Evaluation of the wear of plasma-nitrided and TiN-coated HSS drills using conventional and microPIXE techniques, Wear 239 (2000) 155–167. [12] K.B. Pedersen, Wear measurement of cutting tools by computer vision, Int. J. Mach. Tools Manuf. 30 (1) (1990) 131–139. [13] J.U. Jeon, S.W. Kim, Optical flank wear monitoring of cutting tools by image processing, Wear 127 (1988) 117–207. [14] D.E. Dimla, The correlation of vibration signal features to cutting tool wear in a metal turning operation, Introduction J. Adv. Manuf. Technol. 19 (10) (2002) 705–713.
An automated flank wear measurement of microdrills using machine vision J.C. Su ∗ , C.K. Huang 1 , Y.S. Tarng 2 Department of Mechanical Engineering, National Taiwan University of Science and Technology, 43, Keelung Road, Section 4, Taipei 106, Taiwan, ROC Received 10 March 2006; received in revised form 8 June 2006; accepted 6 July 2006
Abstract The objective of this study is to develop an automated flank wear measurement scheme using vision system for a microdrill. The images of worn-out microdrills were captured after the hole-drilling tests on a 10-layered printed circuit board (PCB). The models were evaluated and validated based on the acquired image with a computer-based acquisition system. Edge detection was employed to extract the boundary with a pair of edge points, including both raising and falling edges, to compute the height of the cutting plane. The flank wear area, average flank wear height, and maximum wear height are computed by using this approach to evaluate the tool life. Experimental results show that the proposed scheme is reliable and effective for the automated frank wear measurement of microdrill in PCB production. © 2006 Elsevier B.V. All rights reserved. Keywords: Microdrill; Machine vision; Flank wear measurement; Edge detection; Image processing; PCB
1. Introduction Measuring the wear of the microdrill (diameter of 0.3–0.02 mm) is important in a printed circuit board (PCB) production. While a drill begins to wear, the cutting forces increases and the temperature of the drill raises, which speeds up the physical and chemical reactions associated with drill wear, and causes rapid deterioration of the drill quality [1]. In PCB manufacturing, a worn-out microdrill damages the quality of the surface finish and the dimensions of the drilled hole. Tool wear not only reduces the part geometry accuracy directly but also increases the cutting forces drastically. The diagram of the microdrill and its characteristics are shown in Fig. 1. The cutting plane (also called first facet or lip relief plane) is consisted of four edges, of which the cutting lip and chisel edge are two important cutting edges on the first facet. The centering point is the first contact point of a microdrill with the material surface, and cutting lips are the major cutting edges for material removal. Chisel edges are comprised of two intersecting planes that define two primary cutting edges of the microdrill. They remove the material ∗
Corresponding author. Tel.: +886 2 2737 141x7348; fax: +886 2 2737 6460. E-mail addresses: op [email protected], [email protected] (J.C. Su), [email protected] (C.K. Huang), [email protected] (Y.S. Tarng). 1 Tel.: +886 2 2737 141x7348; fax: +886 2 2737 6460. 2 Tel.: +886 2 2737 6456; fax: +886 2 2737 6460. 0924-0136/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2006.07.001
by extrusion and cut at a highly negative rake angle. Moreover, for smaller diameters, various drilling parameters, such as web thickness, point angle, spindle speed, and feed, need to be further analyzed. The effect of retraction rate for the influence on cutting life is also concerned. Many researchers have used various sensors, acoustic emission (AE) [5], dynamometers [6], vibration [7], ultrasonic vibrations [8], and motor spindle speed and power and power consumption [14] to monitor drill wear. Lin and Ting [3] established the relationship between force signals and flank wear and other cutting parameters when drilling a copper alloy. Ertunc and Loparo [4] developed a decision fusion center algorithm, which combines the outputs of the individual methods to make a global decision for the wear status of the drill. Li et al. [10] presented a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. Several researchers have examined the usage of machine vision for the measurement of tool wear. For example, Nickel et al. [11] presented the machining performance of HSS twist drills that were plasma-nitrided before applying PVD TiN coating, and evaluated and compared it with that of commercially TiNcoated drills. The optical methods were essentially off-line and involved the examination of geometry using a specially adapted toolmaker’s microscope for the digital processing of the tool tip image. Pedersen [12] reported that the determination of the wear land is usually based on the high intensity of the reflected light from the wear land. Jeon and Kim [13] employed a suit-
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Fig. 1. Schematic diagram of a microdrill.
able thresholding technology to convert the captured grey image into binary image. Subsequently, the amount of flank wear was captured using the binary, where the wear land is white and its background is black. Hazra et al. [2] used three silhouette images of flank surface to measure the geometry of drill points of drills, and fitted the coordinates to a mathematical model of the drill point geometry, which was optimized by genetic algorithm, to estimate five geometry parameters. Based on the above review, previous researches using various sensors mainly emphasized on the variations of monitors on the tool immediately during the machining process. Other researchers using different algorithms based on vision system measured the degree of tool wear, but they focused only on the measurement of the large-size of drill wear (d > 0.5 mm). A few micrometers of the wear error could affect the quality of the microdrills. Therefore, the primary objective of this research presents a machine vision system to develop an innovation method for measuring flank wear of microdrills and to meet the demand for analyzing the relationship between drilling parameters and tool life. The remainder of this thesis is organized as follows: the experimental set-up for measuring tool wear is established in Section 2. Section 3 describes measuring wear of microdrill approach using two-dimensional edge diction lines. In Section 4, the measured results are presented to confirm the effectiveness of the
proposed method. Finally, Section 5 summarizes the conclusion of this study. 2. Experimental set-up Machine vision is the direct method used for tool wear measurement. The experimental of the present study is comprised of two major procedures: (1) several PCB hole-drilling tests with given diameter of 0.2 mm, and the images of flank wear are captured and (2) a machine vision system based on tool microscope to measure the flank wear. The system is detailed as follows.
2.1. Procedure I Several hole-drilling tests were carried out using drilling machine (XL 1-24 Lin) with tungsten carbide microdrills (diameters of 0.2 mm) on a 10-layered print circuit board machining. The drilling parameters include a web thickness of 0.32%, point angle of 135◦ , spindle speed of 180,000 rpm, feed of 20 m/rev, and retraction rate 10 m/min. In the PCB hole-drilling tests, the images of the flank wear on the cutting plane were captured by tool microscope after hole drilling of 1000, 2000, . . ., 9000 hits, respectively. The captured images on different periods of flank wear further measured and analyzed.
2.2. Procedure II Fig. 2 shows the set-up of the vision system for measuring flank wear. The system components include a Pentium IV 2.3 GHz personal computer, frame grabber (NI-1407), CCD (Teli CS8530), toolmaker microscope, circular lighting, and drill holder. The images captured by the CCD camera were transformed
Fig. 2. Experimental set-up for the measuring flank wear using the toolmaker microscope.
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into an eight-bit monochrome format by using the grabber and then stored to the memory of the computer. Each digital image of the microdrill is consisted of a 640 × 480 pixel array with a grayscale value, ranging from 0 to 255. By operating on these arrays of pixels, the desired features and information from the images were extracted. In addition, since microdrill is in three-dimensional geometry, the cutting plane with an angular deviation would have cosine error. Therefore, the drill holder is designed to make cutting plane perpendicular to an observing direction in order to eliminate the cosine error.
3. Measuring method Machine vision is the direct method used for tool wear measurement. The extent of flank wear can be measured as the distance between the top of the cutting edge and the bottom of the area where flank wear occurs. A simple but effective method of measuring flank wear is using edge detection to identify the width change of the cutting plane. The image can be divided into two parts: object (cutting plane including noise) and background. A rising edge is characterized by an increase in the grayscale value as the edge detection crosses the object; a falling edge is characterized by a decrease in the grayscale value as the edge detection is away from the object. Therefore, an effective alternative to measure the cutting plane width is to represent each column of the image by a sequence of lengths that runs the rising and falling edges successively. The discontinuities are typically associated with abrupt changes in pixel intensity values that characterize the boundaries of the objects in a scene. Many edge detection methods have been proposed, such as Sobel operator, Robert operator, Prewitt operator, Laplacian operator, and Marr-Hildreth operator. An automated visual measuring system often operates in constrained and controlled environments. Therefore, the object’s position can be expected where edge detection is needed to extract feature from a captured an image. For this reason, the study has designed the following edge detection algorithm which can specify search region to analyze the pixels. This can help to reduce the amount of time spent on compute. 3.1. One-dimensional edge detection Edge detection is one of the most widely used visual measurement tools. One-dimensional edge detection is introduced to identify and locate discontinuities in order to measure the flank wear of the cutting plane with in the pixel intensities of captured images. We used three parameters to define the edge: (1) filter width, (2) contrast, and (3) steepness, as shown in Fig. 3. They are defined as follows: (1) Filter width. It is the average number of pixels on each side of the edge by setting the filter with parameter “m”. Many factors could affect the value of pixels. For example, noise and dust could cause excessive noise interference or linear profile-intruding peaks. If the image contains too much noise, a large filter width is used to reduce the effect of noise along the profile. To search for more sensitive changes, it can be done by adjusting the filter width. (2) Contrast. It is the difference between the average grayscale values on each side of the given point. To find the edge,
Fig. 3. One-dimensional and two-dimensional edge detections.
the edge strength (contrast) is computed along the onedimensional pixel by pixel. If there is m number of pixels and the coordinates of a pixel value are f(xi , yj ), then the average value of m pixel values W can be calculated in the following equation: m f (xi , yj ) W = i=1 . (1) m After computing the average, the difference between these averages is computed to determine the contrast. If the contrast at the current point is greater than the preset value for the minimum contrast for an edge, the point is stored for further analysis. (3) Steepness. It is the average number of pixels at a certain pixel distance from each side of the given point. This number corresponds to the expected transition region in the edge profile, which marks the start and the end of the variation in grayscale values. If m is the filter width, W the average value of m pixels, C the contrast level, and S is the steepness, then: Step 1: Calculate W by averaging the number of pixels, m, at each analyzed point, S, is the steepness, and m and S are both presetting values m f (xi , yj ) W(k) = i=1 , m 2m+S f (xi , yj ) W(k + 1) = i=S+m , (k = 1, 2, 3, . . .), m (2) where k denotes the current iteration number, and W(k)and W(k+1) represent two consecutive steps along the line search in a generated sequence of computing the average of the number of pixels. Step 2: Calculate the difference in W to see if it is greater than the pre-setting contrast C by using the equation below: 1 if W ≥ C W = W(K + 1) − W(k) = , (3) 0 if W < C
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where W > C denotes a trigger that indicates a located edge, while W < C denotes no trigger indicating the absence of an edge. Moreover, W > C denotes rising edge, whereas W < C denotes falling edge. Step 3: The correct edge position should be within the range of S (see Fig. 3). While a trigger, the point is stored for further analysis. Starting from this point, successive points are analyzed until the contrast reaches a maximum value and then falls below that value. The point where the contrast reaches the maximum value is tagged as the start edge location. The value of the steepness, S, is added to the start edge location to obtain the end of edge location. Step 4: Repeat Steps 1–3 for all the points along the search line profile. This method is able to detect the edge by calculating the grayscale along the linear profile and analyzing the average grayscale values of these pixels. These average values are calculated with the number of pixels of the filter width. A larger filter width indicates the decreased noise effects on the result of the edge detection. In contrast, a smaller filter width indicates a more sensitive effect. 3.2. Two-dimensional edge detection The edge detection algorithm can be extended to a number of search lines to detect edge variation in two-dimensional search T =
Fig. 4. Standard circle with Dt = 0.7mm to be calibrated using the least square method.
(a) Acquire the images of microdrill. (b) Spatial calibration. Spatial calibration is the process of computing pixels for real-world unit transformations. Applying the least square method to the coordinates of the circle gives the diameter of the object. The physical length of the object corresponding to each pixel can be obtained from the following equation, where T denotes the conversion factor:
The diameter of the standard circle in real-world unit Ds (mm) = . The measured diameter of the standard circle in the image unit Dd (pixel)
regions by defining the separation between the lines. Changes in grayscale value will provide information on the relative coordinates of the objects to be measured. Eq. (2) can be modified as follows: m n i=1 j=1 f (xi , yj ) W(k) = , m 2m+S 2n i=S+m j=n f (xi , yj ) W(k + 1) = , (k = 1, 2, 3, . . .), m (4) where n specifies the number of degrees that separates two consecutive search lines of a given length. In these edge detection variations, the two-dimensional search area is covered by a number of search lines which the edge detection is performed. The number of the search lines is controlled in the search region by separating the lines.
(5)
The calibration template is employed for calibration of a standard circle as shown in Fig. 4. (c) Rotate the image Angular deviations may occur when capturing the image of cutting plane. As seen in Fig. 5(a), the image is needs to be rotated at an angle θ so that the bottom of the cutting plane is perpendicular to the horizontal axis. Rotating the image involves the following procedures. 1. Find the reference line. Two-dimensional edge detection is employed to search the bottom edge of the cutting plane. A set of edge points is fitted into a reference line. The x coordinates of all the located edges can be written as x = {x1 , x2 , x3 , . . ., xn }, while all the y coordinates can be denoted as y = {y1 , y2 , y3 , . . ., yn }. Linear fitting of all coordinates is represented by the equation: F = mx + b, where F denotes the
3.3. Measuring procedures This study has applied the edge detection to extract the boundary and to measure the flank wear of the cutting plane. The measuring process is consisted of the following five steps:
Fig. 5. The images of cutting plane with flank wear. (a) Rotate the image to horizontal and (b) the search region is confined by the red rectangle to avoid noise effect in the measurement results.
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optimum value after fitting, m the slope, and b is the intercept. However, not all of edge points conform exactly to the robust fitting line owing to noise interference or discontinuous line profile. Therefore, in order to ensure that all the effective edge points obtained conform to a linear line, the mean square distance (MSD) is used to evaluate the fitting results as 1 (fi − yi )2 n n−1
MSD =
(6)
i=0
where fi = mxi + b with f denotes the functional value after fitting, m the slope, b the intercept, xi and yi being the coordinates, and n representing the number of data. 2. Calculate the angle θ between the reference line and the horizontal X-axis. 3. Rotate the angle θ. (d) Construct a search region To reduce the image processing time and memory requirement, the search region of two-dimensional edge detection is confined by standard contour (rectangle) to focus image processing and analysis on the part of the image. It is confined to cutting plane outlined by the red rectangle as seen in Fig. 5(b). (e) Determine the starting point for measurement The shape of the boundary of cutting plane can be described by using a pair of linked edges. Features of the worn micro-
Fig. 6. The contour of cutting plane. (a) Original countour of the cutting plane, (b) the worn-out of cutting plane, (c) original image with edge detection search lines and (d) the image of flank wear with edge detection search lines.
drills such as area and height of the worn area are measured to identify the tool wear. Measuring flank wear using a set of boundary points can be distinguished into three methods—measuring the flank wear area, the average wear height, and maximum wear height to evaluate the tool life shown in the following subsection.
Table 1 The relationship between the height of cutting plane and the number of hits (unit: m) Original d1 d5 d10 d15 d20 d25 d30 d35 d40 d45 d50 d55 d60 d65 d70 d75 d80 d85 d90 d95 d100 Area (m2 )
1000 hits
3000 hits
5000 hits
7000 hits
9000 hits
11000 hits
2 4 9 13 17 21 25 29 29 29 29 29 28 28 28 28 28 27 27 25 11
0 4 7 11 14 18 22 24 24 24 24 23 22 23 23 21 21 19 18 16 8
0 3 5 9 12 14 16 19 21 21 21 21 19 19 20 18 19 17 15 13 0
0 3 4 7 9 12 16 19 19 18 18 17 17 16 16 14 14 12 12 7 0
0 2 4 6 8 10 13 16 18 17 17 15 14 13 12 11 10 9 8 6 0
0 3 5 7 8 10 11 12 14 15 15 14 12 12 10 9 10 9 8 5 0
0 0 0 3 3 4 6 8 9 10 10 9 8 7 6 5 4 4 4 0 0
2309
1822
1513
1258
1044
927
501
(m2 )
0
487
796
1051
1265
1382
1807
Average wear height (m)
0
5
8
10
13
14
18
Max wear height (m)
0
10
13
18
19
21
24
Wear area
Note: The conversion factor T = 0.996 m/pixel; round towards nearest integer.
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3.4. Measuring the flank wear area The area of flank wear which compared to the area of cutting plane for the differences between worn-out and not worn-out can also be an index to evaluate tool life [9]. Suppose that a 2D contour of flank wear with 2k edge points is represented by the set {Pij = (xij , yij ) for i = 1, 2, 3, . . ., k and j = 1, 2}. Pij is a sequence of paired edge coordinates (x, y) in the image as shown in Fig. 6. Edge detection search line crosses the front contours and back contours of the image of cutting plane vertically to generate a sequence of paired edge coordinates. A set of scan lines start
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from the lower-left point of corner to the upper-right of corner on the cutting plane. The length d between a paired of edge coordinates is written as di = Pi1 Pi2 = |Pi1 − Pi2 |.
(7)
Therefore, the widths n of the intervals approach zero (n refers to the width of two separated search lines), and an approximated area D of cutting plane can be written as follows: D=
k−1
di .
i=0
Fig. 7. Flank wear measurement results.
(8)
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The original area of cutting plane subtracts the area of cutting plane with worn-out is the flank wear area. The flank wear area can be written as follows: Dflank wear = Do − Dw ,
(9)
where Do represents the area of original image and Dw is the area of image of cutting plane with worn-out. It is the area of the accumulation of all line segments. The images of microdrills are captured and the boundary of the cutting plane is extracted using edge diction to reconstruct the cutting plane area from its line of plane integrals. 3.5. Measuring the average wear height VBave The basic concept is to calculate the distance of a pair coordinates namely the height of cutting plane along X-axis direction from the rising edge to the falling edge in a down-to-up scan of a column. Therefore, the average wear height VBave can be written as Dflank wear VBave = , (10) l where l is the number of scan lines with wear. Measuring the average wear height is also an approach to evaluate the tool life. 3.6. Measuring maximum wear height VBmax Maximum wear height VBmax can be written as follows: VBmax = max{dio − diw |,
i = 1, 2, . . . , k},
(11)
where do is the height of original cutting plane and dw is the height of cutting plane with cutting plane. A vision-based approach is presented to measure flank wear directly, which is defined by measuring the maximum distance between the cutting lip and the bottom edge of the wear land. 4. Measurement results and discussion Table 1 shows the measurement results of using the proposed edge detection to compute the height of cutting plane between a pair of edge points in drilling tests from 1000 to 11,000 hits. Three parameters of the wear (flank wear area, average wear height VBave , and maximum wear height VBmax ) were computed to evaluate the tool life and measure the degree of wear. The maximum wear height usually occurs at the corners of the cutting lips because these are the locations of the highest cutting speed. The second wear region occurred at chisel edge because it is the first contact point of a microdrill with the penetration resistance which causes wear. This method can measure the height of cutting plane change for each position. The images of cutting plane with flank wear, including the original image, were detected by using the edge detection with a pair of edge points, as shown in Fig. 7. The images were captured by using the same lighting condition, and the right image is the edge image which shows that the edge points with red mark overlapping the image were linked into the boundary of the cutting plane. Although the image of cutting plane undergone 6000 hits
Fig. 8. The height of the cutting plane change along X-axis in hole-drilling test.
contained excessive noise, the edge detector still detected a pair of edge points on the boundary of the cutting plane. The main reason is the edge detection is confined in a small rectangle to avoid the noise effect on the measurement results. Additionally, the parameter of edge detection and filter width could be set at a greater value to reduce the influence of rapid grey value change. In general, as the number of hole-drilling tests increases (wear time increases), the height of the cutting plane reduces gradually as illustrates in Fig. 8. As shows in Fig. 9, the computed flank wear area in Eq. (9) was included in the experiments of PCB hole-drilling tests. When the number of hits was added, the flank wear area is increased. First, there was a high wear rate in the initial stage of drilling. Next, the flank wear area rose with a nearly steady-state wear. Finally, a rapid wear rate was shown in the final stage until the microdrill reached catastrophic failure. The curves are very similar to the Taylor curve (VTn = C). The cutting speed is more quickly where the radius of the cutter is larger, and the speed of wear becomes faster. Moreover, the average wear height and the maximum wear height computed using Eqs. (10) and (11) are
Fig. 9. The curves of the flank area wear.
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on a 10-layered PCB. This method has not only measured the flank wear area, the average flank wear height VBavg and maximum wear height VBmax but also the height of cutting plane changed for each position. The wear curve of the experiments of PCB hole-drilling trials is very similar to the Taylor curve. The measuring time, including computing the flank wear for a microdrill, is less than 1 s. The proposed measurement system with a resolution of 0.996 m/pixel has successfully measured the frank wear of microdrill in PCB production. In order to extend to the measurement on microdrill of less than 0.2 mm for a longer period, a frame capture of 1024 × 1024 resolution can be employed together with suitable lens and lighting source. References Fig. 10. The curves of the average wear height.
Fig. 11. The curves of the maximum wear height.
plotted in Figs. 10 and 11, respectively. The results were similar as the above results of flank area wear. 5. Conclusions This study has proposed a new machine vision-aided system for measuring the flank wear in a PCB production, and proved the feasibility of measuring frank wear with the hole-drilling tests
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