Tool Wear in A Ceramic Micro-Drilling Process Using Image Processing Methods

Dar-Yuan Chang1* Kuo-Ho Su1 Chyn-Shu Deng2 1Department of Mechanical Engineering, Chinese Culture University 2Department of Mechanical Engineering, National United University

* E-mail: zdy4@faculty.pccu.edu.tw

Abstract - A micro-hole array is a critical feature on a probe head for micro-probe positioning in vertical probe cards. The precision of fabricating micro-holes affects the final positions of needle tips, and has a significant influence on the correctness of testing results. In the industry, the probe head is made of ceramic and the micro-hole array is mainly machined by a mechanical micro-drilling process. Thus, measuring tool wear and the precision of the micro-hole is an important task in probe head fabrication. This study presents a computer vision system that uses image processing methods to evaluate the micro-drill wear. Five experiments with different drilling length and two trials of long-drilling were implemented. Wear measurements of the cutting lip and wear land discussing by image methods provide practical references for ceramic micro-drilling to fully fulfil the tool ultimate utilization. Keywords: Micro-drilling, Tool wear, Image processing, Computer vision.

I. INTRODUCTION

A micro-hole is an essential feature in precision industrial products, and acts a micro-channel for fluid delivery or a probe guide hole for positioning. Example include the micro-pump module in vehicle power systems [1], the wax jet head in a 3-D printer for model prototyping [2], and the micro-holes on a probe head for wafer probe cards [3]. These devices require a micro-hole array to perform product functions.

In wafer testing, a probe card acts as a contact medium between the test machine and wafer. Micro-probes are mounted on a probe-module, and detect the electrical characteristics of the examined die through real contacts with their corresponding welding pads. Reliable testing results are dependent on the precision of micro-probes. A probe head, which made of isolation ceramic mainly, is a substantial component in vertical cobra probe cards. Positions of the needle tips must be controlled by precisely positioned micro-holes on the probe head.

A micro-holes array can be fabricated by electrical-discharge machining (EDM), laser-beam machining (LBM), electrochemical machining(ECM), electron-beam machining (EBM), or mechanical micro-drilling. Since non-traditional processes require a high installation capital and machining methods lack flexibility, most industries use the mechanical micro-drilling process to fabricate micro-hole arrays. Figure 1 shows a ceramic probe head with 3358 micro-holes.

Manufacturing variations in the workpiece primarily result from machine rigidity, mechanism precision, and spindle rolling deviation. In micro-machining, factors such as the deflection of tool body, abrasion wear on tool edges, and unstable vibrations caused by tip breakage all affect the results. To produce high-quality micro-holes, the micro-

drills used in micro-drilling must overcome the problems of tool deflection and buckling, chip clogging, and tool bluntness from build-up edges.

Chen [4] developed a finite element model for analyzing the buckling load and critical speed of micro-drill bits in multi-layer PCBs micro-drilling. This model accounts for the parameters of the helix angle, geometrical dimensions, cross-sectional properties, rotation speed, boundary conditions, axial load, rotary inertia and shear deflection. Huang [5] established a time-dependent vibration model for micro-drilling. This method uses a moving Winkler-type elastic foundation to approximate the drilling process, and considers the rotating speed, pretwisted angle, and thrust force effects of the micro-drill. Kudla [6] investigated the deformations and strength of miniature drills in the case of bending, torsion, and complex load. The bending force involving destruction of steel drills is approximately twice as large as for similar carbide drills, and the deformations of the carbide drills in torsion test are almost proportional to the loading in the testing range. In 2011, Kudla [7] reported experimental investigations into the deformation, strength, and fracture of the cutting parts of micro-drills in static and dynamic conditions. The typical reason for tool destruction during drilling is excessive stress from cutting forces.

Figure 1. The probe head of a vertical probe card.

Since ceramics have poor machinability due to their thermal conductivity, high brittleness and hardness, Lee et al. [8] investigated the drilling behaviours of alumina green bodies using WC diamond grit abrasive micro-drills. Experimental results show that the sintering shrinkage in diameter was 13% for a 187 m micro-hole. In another

study, Lee et al. [9] developed a tool life model for wet micro-drilling using abrasive micro-drills in which a large wear on the side micro-drill induced a rapid increase in axial force. Jadoun et al. [10] presented ceramic drilling

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experiments assisting with ultrasonic to find the optimum parameters for maximum cutting ratio, which is the ratio of material removal rate (MRR) to tool wear rate (TWR). The MRR and TWR were evaluated based on weight loss by an electronic balance. In our former study [11], thousand-micron grade micro-holes were fabricated by peck-drilling at shallow machining depths and continuous cooling using a HSS twist drill. The proper drill returning distance to provide sufficient space for heat dispersion and keep tool sharpness in ceramic drilling is two times of the machining depth.

A machine vision provides an innovative solution in industrial automation and inspection in recent period. Researchers have reported several computer vision systems for the micro-drill manufacturing. Tien and Yeh [12] established a translation, rotation, and template-free automated visual inspection scheme that detects micro-drill defects using the eigenvalue of covariance matrices. This method can also monitor the grinding levels of chisel edges, cutting lips and drill facets. Huang et al. [13] proposed a method of micro-drill resharpening that combines the double-wheel moving-drill fixed wheel method with the mechanical visual technique to achieve the sharpening goal. The restriction of this system is that the characteristics of planar micro-drill must be clear to allow machine vision processing. Watanabe and Masuda [14] used a high speed video camera to observe the micro-drilling behavior upon contact with a work surface dynamically for printed circuit boards. They found that the radial run-out is insensitive to drill wear and hole quality, due to the centripetal action.

Yao et al. [15] implemented wear tests to verify the performance of the micro-drills on a deposited TiN/AlN nano-multilayer. They used the wear depth of drill lip and the friction coefficient of multilayer on a specific test period to evaluate the effects of various coating elements. Lee et al. [9] constructed a tool life model based on a chip flow model of a worn-out micro-drill tip to predict the number of micro-holes available in ceramic green bodies. Su et al. [16] developed an automated measurement scheme using a vision system to examine the flank wear of worn-out micro-drills used in printed circuit board drilling. The proposed image method computers flank wear measures, such as wear area and wear height to evaluate tool life. Atli et al. [17] proposed a vision system for drilling tool condition monitoring. Their method employs a Canny edge detector to extract tool features from the images acquired by a high-speed CCD camera, and any deviations from linearity metric are used to measure the tool wear.

This study develops a computer vision system through the image processing tools to evaluate the micro-drill wear condition. Seven tool wear experiments were implemented with different drilling holes to investigate the effects of various drilling hits on the wear of the chisel edge, cutting lip, wear land, and build-up edges (BUEs).

II. MICRO-DRILLING EXPERIMENTS

In our former study [11], a series of 111 m micro-

hole experiments in alumina ceramic micro-drilling had implemented to investigate the effects of process parameters on hole characteristics. The parameters analyzed in that

study include the spindle revolution speed, drill feed rate, peck-drilling return distance, and centering drill depth. Results reveal the following design criteria. (1) The drill feed rate is the most significant factor affecting

the quality in hole diameter, followed by the peck-drilling return distance. A low spindle speed, large drill feed rate, large return distance in peck-drilling, and shallow centering drill depth lead to good performance.

(2) The drill feed rate is also the factor with greatest effect on roundness. Others factors contribute little. A small drill feed rate is useful to obtain a low roundness error.

(3) The taper of a micro-hole can be calculated based on the plate thickness and diameters of the entrance side and exit side. Experimental results show that a high spindle speed, large drill feed rate, large drilling return distance, and deep centering drill depth achieve a good taper effect. Among these four factors, the depth of the centering drill is the most significant one. The reasons for tool change in micro-drilling include

drill breakages from a high cutting force and build-up edges on the chisel edge, drill body deflection due to buckling in the high slenderness ratio drills, plastic deformation on the cutting edge because of the high temperature and pressure in the heat affected zone, and the wear of cutting edges due to an excessive drilling length. In the discussions of [11], When making a thousand-micron grade micro-hole on a thin alumina ceramic plate, the HSS twist drills still remain in a usable status after 100 holes drilled with a drilling length of 76.2mm. Hence, this study implements seven drill wear experiments with different drilling holes to explore the influences of the drilling length on dimensional characteristics and tool wear. Table 1 lists the experimental design adopted in this study. The hole diameter in this study is 115 m and the drilling thickness is 762 m .

Table 1. Scheme of the micro-drilling experiments.

Exp. Number of drills Drilling length (mm)

TL01 40 30.48 TL02 80 60.96 TL03 120 91.44 TL04 160 121.92 TL05 200 152.4 Long1 280 213.36 Long2 320 243.84

III. IMAGE PROCESSING METHODS

A. Image Acquisition of the Top View of Micro-drills

Machine vision has become a popular solution to feature recognition and dimension measurement in recent years. The precision of image measurements is related to the magnification of image acquisition devices. Figure 2 shows a top view image of the micro-drill of experiment ‘TL04’ which was captured by a scanning electron microscope (SEM) at a magnification of 800X. The drill body was held at an inclined angle of 14 based on the point angle 118 for a twist drill. The figure reveals clear wear traces on the chisel edge and cutting lip. The build-up edges on the chisel edge resulted from the heavy pressure and high heat are found. The left facets have obvious accumulated chips.

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Figure 2. The top view image of the micro-drill in experiment

‘TL04’ captured by SEM (800X).

B. Image Processing of the Histogram Equalization

The frequency of a specific grayscale value in a digital image can be examined from its histogram. Image contrast can be improved through a grayscale uniformized treatment, that is, the histogram equalization. Figure 3 shows the tool image of experiment｀TL04＇after histogram equalization. The wear regions are easy to recognize compared to the original image in Fig.2.

The relationship between the actual length and its corresponding pixels in the image can be derived from the standard point marks on the bottom in Fig.3. The length of the first and end marks is 50 m and crosses 246 pixels.

Thus, per pixel indicates 0.2 m and a square of 20.041 m .

C. Image Processing of the Unsharp Masking

Figure 4 shows the image after unsharp masking. The characteristic boundaries in this image are more distinct than the image after histogram equalization (Fig.3). However, the grayscale values are concentrated within 68~153. Thus, a histogram equalization process is necessary to improve the image contrast, as Fig.5 shows. Wear on the chisel edge and cutting lip is clearly recognized, and the build-up edges on the left of the chisel edge are more easily distinguished. The original image captured by SEM is showed in Fig.2.

Figure 3. The image of the micro-drill in experiment ‘TL04’ by

histogram equalization.

Figure 4. The image of the micro-drill in experiment ‘TL04’ by

unsharp masking.

D. Image Processing of the Regions of Interest (ROI)

Following the processes mentioned above, the image of the drill top view was first captured by the SEM device, and the unsharp masking was then used to facilitate characteristic recognition. Finally, the histogram equalization was implemented to improve image contrast.

Based on the symmetry of the twist micro-drills, this study only investigates the wear on the chisel edge and cutting lip of the right-top region from the centering point (point ‘o’). The regions of interest processing (ROI) was applied to obtain the image for wear observation, as Fig.6 shows. Two datum lines drawing based on the chisel edge and boundary of facets from the centering point are used for wear evaluation. A reference line drawn from the intersection of the chisel edge and the cutting lip (point ‘p’) with an angle of 45 to the horizontal axis is used to divide the wear into a chisel edge region and a cutting lip region. The characteristics to the left of the chisel edge are the BUEs. According to the degree of accumulation, build-up edges can be divided into a stable BUEs in block and unstable BUEs in gray.

Figure 5. The image of the micro-drill in experiment ‘TL04’ by

unsharp masking based on Fig.3.

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Figure 6. Wear illustration of a ROI image of the worn-out drill of

experiment ‘TL04’.

IV. TOOL WEAR EVALUATIONS

This study uses an image measuring tool to identify the wear projected areas (including the chisel edge, cutting lip, stable BUEs, and unstable BUEs) and measure the length of wear land (the referring line drew by hidden line in Fig.6) from the top view images of the drill. Table 2 lists the evaluation results.

Because the high slenderness ratio micro-drill causes a high cutting force on the chisel edge and the BUEs influence the drilling efficiency, the volume of the chisel edge wear was unproportionate to the number of drilled holes. In contrast, the wear projected area of the cutting lip and the length of wear land show a rising trend as the number of drilled holes increases (Fig.7 and Fig.8). Two referable equations are regressed as follows

83.1336.12W xearLip, (1)

026.56444.0W xearLand, (2)

where x is the quotient of the number of micro-holes divided by the increment of each experiment. In this study, the increment of x is 40.

To explore the effects of drilling length, this study implemented a long-drilling experiment involving 280 holes. Equations (1) and (2) indicate that the predictions of wear projected area of the cutting lip and the length of wear land were 2222.03 m and 9.5358 m , respectively. The

actual measurements were 9.5358 m and 9.773 m . Both

values were larger than predicted. The prediction errors are -13.28 % and -2.42%.

Table 2. Results of wear evaluations.

Exp. Number of holes

Wears projected area 2( )m

Chisel edge Cutting lip TL01 40 163.95 142.25 TL02 80 136.21 161.91 TL03 120 157.53 172.38 TL04 160 185.18 190.77 TL05 200 156.81 190.82

Long1 280 203.78 256.05

Exp. Number of holes

Stable BUEs 2( )m Length of

wear land ( m )

TL01 40 92.93 5.181 TL02 80 153.94 6.764 TL03 120 103.28 7.042 TL04 160 163.41 8.048 TL05 200 134.83 7.761

Long1 280 153.03 9.773

Figure 7. Lip wear projected area of the micro-drills used in experiments.

Figure 8. Wear land length of the micro-drills used in experiments.

The forming mechanisms and fall-off timing of BUEs are very complicated. This study only observes the relationship between the BUEs and the number of holes drilled. Figure 9 shows the ROI images of the BUEs of each experimental drill. In the low-number drillings, BUEs are sparse; nevertheless, the BUEs accumulate steadily (the deep black parts in Fig. 9) as the number of holes increased. Figure 9(d) indicates that areas of the stable BUEs and the chisel edge of the drill of experiment ‘TL04’ are quite near (refer Table 2). However, the unstable BUEs of the drill of ‘TL05’nearly fell off, as Fig.9(e) shows. Fig.9(f) indicates that the dill of experiment ‘Long1’ , which performed 280 drillings, had a wide wearing area on the chisel edge.

(a) TL01, 40 holes; (b) TL02, 80 holes;

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(c) TL03, 120 holes;

(d) TL04, 160 holes;

(e) TL05, 200 holes;

(f) Long1, 280 holes.

Figure 9. Images of the BUEs on the drill chisel edge after various drillings.

Figure 10 depicts a worn-out drill after 320 drillings (experiment ‘Long2’). The chisel edge is out of shape and BUEs have fallen off entirely. At this point, the machining mechanism for material removal was not by cutting, but merely the abrasion effect. The tool cannot be used again.

Figure 10. Image of the chisel edge after 320 drillings used in

experiment ‘Long2’.

V. CONCLUSIONS

This study presents a computer vision system to tool wear evaluation for the micro-drills used in ceramic micro-drilling. Original images captured by a scanning electronic microscope were processed by image processing methods including grayscale treatment, threshold operation, binary processing, histogram equalization, unsharpen masking, and regions of interest to derive the images for characteristic recognitions and measurements. Results indicate the wear region of the cutting lip and the length of wear land are increasing progressively, and the BUEs on the drill chisel edge in different drilling holes have a clearly observation. The proposed procedure can effectively investigate the tool wear in ceramic micro-drilling and improve the precision in micro-hole array fabrication.

ACKNOWLEDGEMENT

The authors would like to express their appreciation to Certain Micro Application Technology Incorporation for their assistance in the micro-drilling process. The authors gratefully acknowledge the financial support of the National Science Council Republic of China under Grant NSC 99-2221-E-034-013.

REFERENCES

[1] L. Lin, C. Diver, J. Atkinson, R. Giedl-Wagner, H.J. Helml, “Sequential laser and EDM micro-drilling for next generation fuel injection nozzle manufacture,” Annals of the CIRP, 55 (2006) 179-182.

[2] 3D Bastech, Inc., available at http://www.bastech.com/3dprinting , last accessed on September 23, 2011.

[3] JEM America Corp., available at http://www.jemam.com/vertical.htm, last accessed on October 25, 2011.

[4] W.R. Chen, “Parametric studies on buckling loads and critical speeds of microdrill bits,” International Journal of Mechanical Sciences, 49 (2007) 935-349.

[5] B.W. Huang, “The drilling vibration behavior of a twisted microdrill,” Journal of Manufacturing Science and Engineering, 126 (2004) 719-726.

[6] L.A. Kudla, “Deformations and strength of miniature drills,” Proceeding of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 220 (2006) 389-396.

[7] L.A. Kudla, “Fracture phenomena of microdrills in static and dynamic conditions,” Engineering fracture mechanics, 78 (2011) 1-12.

[8] D.G. Lee, H.G. Lee, P.J. Kim, K.G. Bang, “Micro-drilling of alumina green bodies with diamond grit abrasive micro-drills,” International Journal of Machine Tools & Manufacture, 43 (2003) 551-558.

[9] H.G. Lee, D.G. Lee, “Tool life model for abrasive wet micro-drilling of ceramic green bodies,” International Journal of Machine Tools & Manufacture, 44 (2004) 839-846.

[10] R.S. Jadoun, P. Kumar, B.K. Mishra, R.C.S. Mehta, “Optimization of process parameters for ultrasonic drilling of advanced engineering ceramics using the Taguchi approach,” Engineering Optimization, 38 (2006), 771-787.

[11] D.Y. Chang, S.Y. Lin, “Tool wear, hole characteristics and manufacturing tolerance in alumina ceramic micro-drilling process,” Materials and Manufacturing Processes, DOI:10.1080/10426914.2011.577881 (2011).

[12] F.C. Tien, C.H. Yeh, “Using eigenvalues of covariance metrices for automated visual inspection of microdrills,” International Journal of Advanced Manufacturing Technology, 26 (2005) 741-749.

[13] C.K. Huang, Y.S. Tarng, C.Y. Chiu, A.P. Huang, “Investigation of machine vision assisted automatic resharpening process of micro-drills,” Journal of Materials Processing Technology, 209 (2009) 5944-5954.

[14] H. Watanabe, H. Tsuzaka, M. Masuda, “Microdrilling for printed circuit boards (PCBs) – influence of radial run-out of microdrills on hole quality,” Precision Engineering, 32 (2008) 329-335.

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[15] S.H. Yao, Y.L. Su, W.H. Kao, T.H. Liu, “On the micro-drilling and turning performance of TiN/AlN nano-multilayer films,” Materials Science and Engineering A, 392 (2005) 340-347.

[16] J.C. Su, C.K. Huang, Y.S. Tarng, “An automated flank wear measurement of microdrills using machine vision,” Journal of Materials Processing Technology, 180 (2006) 328-335.

[17] A.V. Atli, O. Urhan, S. Ertürk, M. Sönmez, 2006, “A computer vision-based fast approach to drilling tool condition monitoring,” Proceeding of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 220 (2006) 1409-1415.

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