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Development of monitoring system on the diamond tool wear
International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Development of monitoring system on the diamond tool wear In-Hyu Choi, Jeong-Du Kim* Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Taejon, 305701, South Korea Received 24 February 1997; in final form 10 October 1997
Abstract Recently, ultra-precision machining using a single crystal diamond tool has been developing very rapidly, especially in the fields of production processes for optical or magnetic parts such as magnetic discs, laser mirrors, polygon mirrors and copier drums. As a result, it has been successfully extended to machine various soft materials, generating mirror-like surfaces to sub-micron geometric accuracy with the ultraprecision CNC machine and the single crystal diamond tool. With the real cutting operation, the geometric accuracy and the surface finish attainable in machined surfaces are mainly determined by both of the sharpness of a cutting tool and stability of the machine vibration. In this study, for monitoring the progress of machining state for assuring the machining accuracy and the surface quality, a new monitoring method of machining states in face-cutting with diamond tool is proposed, using the frequency response of multisensors signal, which includes wear state of tool in terms of the energy within the specific frequency band. A magnetic disc is machined on the ultra-precision lathe. 1998 Published by Elsevier Science Ltd. All rights reserved.
1. Introduction Recently, ultra-precision machining technology using a single-crystal diamond tool has developed rapidly in the fields of production processes of optical and magnetic parts such as * Corresponding author. 0890-6955/98/$—see front matter 1998 Published by Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 0 - 6 9 5 5 ( 9 7 ) 0 0 0 7 6 - X
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I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
magnetic discs, laser mirrors, polygon mirrors and copier drums. This has been successfully extended to machine various soft materials such as aluminum and copper, generating mirror-like surfaces with sub-micron geometric accuracy using both an ultra-precision CNC machine and a single-crystal diamond tool [1,2]. Surface quality, including the geometric accuracy and the surface finish, obtained by ultra-precision machining is determined by tool edge sharpness and machine stability. Many studies using single-crystal diamond tools have now been reported. In practical usage, surface roughness is able to achieve up to 10 nm; and at the laboratorial step, up to 1 nm [3,4]. The surface profile is basically made of repetitive tool pass on the face perpendicular to cutting direction. That process is very difficult to identify, because surface generating process is considered as wholly duplicating of diamond tool geometry. It is difficult to understand and is affected by complicated inter-attraction [5]. It is known that diamond has little attracting force to various materials, and its cutting edge is capable of duplicating wholly its own shape onto the target surface. Even micro-chipping or small irregularity by cohesive abrasives in the cutting edge causes damage on machined surfaces [6–8]. In this study, in order to minimize material loss by damaging the mirror-like product machined by the ultra-precision turning process, and to provide information about the regrinding time of a diamond tool with on-line, diamond tool wear, a monitoring system has been developed. A new method of monitoring states in face-cutting with a diamond tool is proposed to monitor the tool wear for ensuring machining accuracy, using frequency response of multi-sensors signal, which will include wear features in terms of the energy within the specific frequency band. The wear pattern of diamond tool is also investigated when a magnetic disc is machined by the ultraprecision lathe. 2. Experimental device and method For monitoring the tool wear in the ultra-precision lathe, the circular plate (outer diameter 125 mm, inner diameter 60 mm), is used as the workpiece, which is aluminum plate for computer hard disk. The algorithm used is made up of the data acquisition program to sample out from two-sensors at once, the judgement program to show the monitoring state on screen, and finally the treatment program to cope with the wear events. Here, the tool dynamometer (Kistler 50119257B) for measuring main cutting force, and the accelerometer (piezo-film type) with sensitivity 8.3 mV g−1 for measuring the vibration of tool toward main cutting force is attached. Two parameters selected for detecting tool wear have various forms when cutting. It is possible to distinguish tool wear from normal states by observing the frequency response of cutting force and of acceleration. Data sampled with 5 kHz using A/D convertor (PCL-812PG). Table 1 shows specification of sensors and cutting conditions involved in this wear monitoring experiment. Since diamond tool wear occurs slowly and its volume is extremely little, the experiment is carried out on the ultra-precision lathe, which has an aerostatic bearing to rotate main spindle and a numerical controller with a feed of resolution 0.1 m. Fig. 1 shows flowchart for tool wear monitoring of diamond turning. The cutting signals are sampled simultaneously with 5 kHz by using A/D converter from sensors. During one sampling, cutting states and whether tool wear occurs or not, are determined. If normal, the next sampling
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515 Table 1 Experimental specification for tool wear monitoring Specification
Condition
Lathe Workpiece Tool Tool dynamometer Accelerometer Cutting velocity Depth of cut Feedrate
Ultra-precision lathe Aluminum (for hard disk) t = 2 mm, = 125 mm Single-crystal diamond (s-type) Kistler 9257B Pennwalt Co. ACH-01 (Piezo-film type: 9.7 mV g ⫺ 1) 251 m min ⫺ 1 20 m 50 mm min ⫺ 1
Fig. 1. Flowchart for tool wear monitoring of diamond turning.
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progresses and wear monitoring is repeated. When tool wear occurs, a control action to inform regrinding of tool is followed. The cutting conditions for monitoring are set with cutting speed 251 m min−1, depth of cut 20 m, feed rate 50 mm min−1. Fig. 2 shows a schematic diagram for monitoring the wear in the ultra-precision lathe including the geometry of single-crystal diamond tool to be used in this experiment, with edge length 1.5 mm, horizontal install angle 0°, rake angle and clearance angle 2° and 5° respectively. 3. Tool monitoring analysis In order to extract tool wear signals from cutting force and acceleration signal during diamond turning, we used the special band frequency energy. That is, when a low frequency limit, fL, and a high frequency limit, fH are set, average energy between those is described as Eq. (1) [9].
2xB ⫽
冕
fH
Gx(f)df
(1)
fL
where, Gx(f) is a PSD function of raw signal, x(t). If Band–Pass–Filter is used to cut off both of below a low frequency limit, fL, and above a high frequency limit, fH and to monitor directly x(t)BPF, energy of band pass signals appears as [Eq. (2)].
Fig. 2.
Schematic diagram for monitoring the tool wear in the ultraprecision lathe.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
冕
509
T
2xBPF
⫽
冕
1 2 ⫽ lim x (t)BPFdt t→⬁ T
(2)
0
fH
Gx(f)df ⫽ 2xB
fL
That equation is changed for performing sampling operation within the computer system into desecrate signal processing form Eq. (3), which is average energy with data number, N.
冘 N
2xBPF ⫽
1 x2 N n ⫽ 1 nBPF
(3)
Tool wear is only sensitive to an average energy in special band, from which frequency domain feature is extracted about wear. To apply sampling to continuous signal, recurrence algorithm is selected like Eq. (4).
冘 N
2xBPF ⫽
1 x2 N n ⫽ 1 nBPF
冉
n⫺1 1 ⫽ n n⫺1 ⫽
冘 冊
n⫺1
x2iBPF ⫹
i⫽1
1 2 x n nBPF
(4)
1 n⫺1 2 n ⫺ 1 ⫹ x2nBPF n n
Where initial value 20 ⫽ 0. Using Eq. (4), the average energy of wear signal is continuously monitored. As cutting is started, workpiece and chip have friction with tool face, thus vibrating the cutting system. Fig. 3 shows typical PSD diagram of the machining signal in aspect of diamond tool wear with time steps. Spectrum peaks in the lower frequency region, f1, are utilized as an index presenting various wear stages, which have generally five stages; initial wear, normal wear, micro fracture, wholly wear and tool breakage. Therefore, that is used as an index of tool wear in diamond turning.
4. Experimental results and considerations Fig. 4 shows the frequency responses of cutting force signal, including characteristic of phasefrequency and power amplitude-frequency. We know that cutting force signal has a periodic phase fluctuation from Fig. 4(a) and its power amplitude has a constant level on overall frequency range
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Fig. 3. Typical PSD diagram of the machining signal for wear monitoring.
Fig. 4. Frequency response of cutting force signals.
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511
from Fig. 4(b), but the small increase below 250 Hz implies that the characteristic of tool wear is added to normal signal. Fig. 5 shows the frequency responses of acceleration signal, including the characteristic of phase-frequency and power amplitude-frequency. The phase of acceleration signal is similar to that of cutting force signal, and its amplitude has little change. This fact implies that cutting force signal is more sensitive than the acceleration signal to wear action in the cutting edge. Fig. 6 plots series of frequency responses of cutting force signal in different cutting length (or cutting time), which is sampled on cutting aluminum with a diamond tool. From this, wear features of diamond tool are extracted by observing the energy of frequency band relating to wear mechanism and the overall distribution of their peaks. The maximum peak appears around 100 Hz and decreases with cutting time. Fig. 7 plots a series of frequency responses of acceleration signal in direction of main cutting in different cutting length (or cutting time), which is sampled on cutting aluminum with a diamond tool. The maximum peak appears around 100 Hz in the form of frequency responses of the cutting force signal. Therefore this frequency, 100 Hz, is considered as the natural frequency in the tool– workpiece system. Fig. 8 shows wear patterns of diamond tool with cutting time in machining aluminum using
Fig. 5. Frequency response of acceleration signals.
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I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Fig. 6.
Spectrum analysis of cutting force signal for tool wear monitoring.
microscope photographs of cutting edge which include rake face and flank face of tool after machining for about 20 and 40 min. Fig. 8(a) shows wear patterns of diamond tool in cutting 20 m, cutting speed 251 m min−1, and cutting time 20 min; showing small craters of wear in rake face. Fig. 8(b) shows rake face and cutting edge line of tool have not changed in overall shape after machining for about 20 min. Fig. 8(c) shows a flank face which is free from severe wear but having several micro-chippings in edge. Tool life of diamond is mainly determined faster by observing its chipping, than by the wear shown on aluminum when machining. Therefore, the micro-chipping in the cutting edge is considered as the indicator of tool life in diamond turning because of the ease in detecting them. Fig. 8(d)–(f) shows microscope photographs of cutting edge which include rake face and flank one of tool after machining for about 40 min. Fig. 8(d) shows wear patterns of diamond tool in cutting 20 m, cutting speed 251 m min−1, and cutting time 40 min, showing micro-chipping in the cutting edge and a large breakage in the corner. This pattern is dominated by chipping in the cutting edge observed in both rake face and flank face. Fig. 8(e) describes rake face and cutting edge line of tool, with micro-chipping and large chipping in the corner. Wear in flank face as shown in Fig. 8(f) does not relatively progress. Generally, wear is occurring at the three main faces on the cutting edge involving the wear process of rake face, upper flank face, and side flank face. At the point intersecting these three faces, wear is most severe and extending from that edge point.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
513
Fig. 7. Spectrum analysis of acceleration signal for tool wear monitoring.
5. Conclusions The frequency response of two signals are investigated in order to monitor diamond tool wear during a magnetic disc being machined by the ultra-precision lathe. Conclusions are a result of analysing tool wear patterns and experiments, and are as follows. 1. A new method of monitoring states in face-cutting with diamond tool is developed using frequency response of multi-sensors signal, including the algorithm extracting wear feature in terms of the energy within the specific frequency band. 2. Wear features of diamond tool are dominated by micro-chipping rather than crater wear and flank wear, with the observation of the energy of frequency band relating to tool wear.
References [1] J.M. Oomen, J. Eisses, Wear of monocrystalline diamond tools during ultraprecision machining of nonferrous metals, Precision Engineering 14 (4) (1992) 206–218. [2] R. Wada, H. Kodama, K. Nakamura, Y. Mizutani, Y. Shimura, Wear characteristics of single crystal diamond tool, Annals of the CIRP 29 (1) (1980) 47–52. [3] N. Ikawa, S. Shimada, H. Morooka, Technology of diamond tool for ultraprecision metal cutting, JSPE 21 (4) (1987) 233–238. [4] T.J. Ko, D.W. Cho, J.M. Lee, Fuzzy pattern recognition for tool wear monitoring in diamond turning, Annals of the CIRP 41 (1) (1992) 125–128.
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I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Fig. 8. Wear characteristics of diamond tool with cutting time in cutting A1.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
515
[5] C.J. Wong, Fracture and wear of diamond cutting tools, J. of Engineering Materials and Technology Trans. of the ASME 103 (1981) 341–345. [6] J.-D. Kim, I.-H. Choi, Development of a tool failure detection system using multi-sensors, Int. J. Mach. Tools Manufact. 36 (8) (1996) 861–870. [7] F. Giusti, M. Santochi, G. Tantussi, On-line sensing of flank and crater wear of cutting tools, Annals of the CIRP 36 (1) (1987) 41–44. [8] J. Colgan, H. Chin, K. Dansi, S.R. Hayashi, On-line tool breakage detection in turning: A multi-sensor method, J. of Engineering for Industry, Trans. of the ASME 116 (1994) 117–123. [9] C.Y. Jiang, Y.Z. Zhang, H.J. Xu, In-process monitoring of tool wear stages by the frequency band-energy method, Annals of the CIRP 36 (1) (1987) 45–48.
Development of monitoring system on the diamond tool wear In-Hyu Choi, Jeong-Du Kim* Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Taejon, 305701, South Korea Received 24 February 1997; in final form 10 October 1997
Abstract Recently, ultra-precision machining using a single crystal diamond tool has been developing very rapidly, especially in the fields of production processes for optical or magnetic parts such as magnetic discs, laser mirrors, polygon mirrors and copier drums. As a result, it has been successfully extended to machine various soft materials, generating mirror-like surfaces to sub-micron geometric accuracy with the ultraprecision CNC machine and the single crystal diamond tool. With the real cutting operation, the geometric accuracy and the surface finish attainable in machined surfaces are mainly determined by both of the sharpness of a cutting tool and stability of the machine vibration. In this study, for monitoring the progress of machining state for assuring the machining accuracy and the surface quality, a new monitoring method of machining states in face-cutting with diamond tool is proposed, using the frequency response of multisensors signal, which includes wear state of tool in terms of the energy within the specific frequency band. A magnetic disc is machined on the ultra-precision lathe. 1998 Published by Elsevier Science Ltd. All rights reserved.
1. Introduction Recently, ultra-precision machining technology using a single-crystal diamond tool has developed rapidly in the fields of production processes of optical and magnetic parts such as * Corresponding author. 0890-6955/98/$—see front matter 1998 Published by Elsevier Science Ltd. All rights reserved. PII: S 0 8 9 0 - 6 9 5 5 ( 9 7 ) 0 0 0 7 6 - X
506
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
magnetic discs, laser mirrors, polygon mirrors and copier drums. This has been successfully extended to machine various soft materials such as aluminum and copper, generating mirror-like surfaces with sub-micron geometric accuracy using both an ultra-precision CNC machine and a single-crystal diamond tool [1,2]. Surface quality, including the geometric accuracy and the surface finish, obtained by ultra-precision machining is determined by tool edge sharpness and machine stability. Many studies using single-crystal diamond tools have now been reported. In practical usage, surface roughness is able to achieve up to 10 nm; and at the laboratorial step, up to 1 nm [3,4]. The surface profile is basically made of repetitive tool pass on the face perpendicular to cutting direction. That process is very difficult to identify, because surface generating process is considered as wholly duplicating of diamond tool geometry. It is difficult to understand and is affected by complicated inter-attraction [5]. It is known that diamond has little attracting force to various materials, and its cutting edge is capable of duplicating wholly its own shape onto the target surface. Even micro-chipping or small irregularity by cohesive abrasives in the cutting edge causes damage on machined surfaces [6–8]. In this study, in order to minimize material loss by damaging the mirror-like product machined by the ultra-precision turning process, and to provide information about the regrinding time of a diamond tool with on-line, diamond tool wear, a monitoring system has been developed. A new method of monitoring states in face-cutting with a diamond tool is proposed to monitor the tool wear for ensuring machining accuracy, using frequency response of multi-sensors signal, which will include wear features in terms of the energy within the specific frequency band. The wear pattern of diamond tool is also investigated when a magnetic disc is machined by the ultraprecision lathe. 2. Experimental device and method For monitoring the tool wear in the ultra-precision lathe, the circular plate (outer diameter 125 mm, inner diameter 60 mm), is used as the workpiece, which is aluminum plate for computer hard disk. The algorithm used is made up of the data acquisition program to sample out from two-sensors at once, the judgement program to show the monitoring state on screen, and finally the treatment program to cope with the wear events. Here, the tool dynamometer (Kistler 50119257B) for measuring main cutting force, and the accelerometer (piezo-film type) with sensitivity 8.3 mV g−1 for measuring the vibration of tool toward main cutting force is attached. Two parameters selected for detecting tool wear have various forms when cutting. It is possible to distinguish tool wear from normal states by observing the frequency response of cutting force and of acceleration. Data sampled with 5 kHz using A/D convertor (PCL-812PG). Table 1 shows specification of sensors and cutting conditions involved in this wear monitoring experiment. Since diamond tool wear occurs slowly and its volume is extremely little, the experiment is carried out on the ultra-precision lathe, which has an aerostatic bearing to rotate main spindle and a numerical controller with a feed of resolution 0.1 m. Fig. 1 shows flowchart for tool wear monitoring of diamond turning. The cutting signals are sampled simultaneously with 5 kHz by using A/D converter from sensors. During one sampling, cutting states and whether tool wear occurs or not, are determined. If normal, the next sampling
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515 Table 1 Experimental specification for tool wear monitoring Specification
Condition
Lathe Workpiece Tool Tool dynamometer Accelerometer Cutting velocity Depth of cut Feedrate
Ultra-precision lathe Aluminum (for hard disk) t = 2 mm, = 125 mm Single-crystal diamond (s-type) Kistler 9257B Pennwalt Co. ACH-01 (Piezo-film type: 9.7 mV g ⫺ 1) 251 m min ⫺ 1 20 m 50 mm min ⫺ 1
Fig. 1. Flowchart for tool wear monitoring of diamond turning.
507
508
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
progresses and wear monitoring is repeated. When tool wear occurs, a control action to inform regrinding of tool is followed. The cutting conditions for monitoring are set with cutting speed 251 m min−1, depth of cut 20 m, feed rate 50 mm min−1. Fig. 2 shows a schematic diagram for monitoring the wear in the ultra-precision lathe including the geometry of single-crystal diamond tool to be used in this experiment, with edge length 1.5 mm, horizontal install angle 0°, rake angle and clearance angle 2° and 5° respectively. 3. Tool monitoring analysis In order to extract tool wear signals from cutting force and acceleration signal during diamond turning, we used the special band frequency energy. That is, when a low frequency limit, fL, and a high frequency limit, fH are set, average energy between those is described as Eq. (1) [9].
2xB ⫽
冕
fH
Gx(f)df
(1)
fL
where, Gx(f) is a PSD function of raw signal, x(t). If Band–Pass–Filter is used to cut off both of below a low frequency limit, fL, and above a high frequency limit, fH and to monitor directly x(t)BPF, energy of band pass signals appears as [Eq. (2)].
Fig. 2.
Schematic diagram for monitoring the tool wear in the ultraprecision lathe.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
冕
509
T
2xBPF
⫽
冕
1 2 ⫽ lim x (t)BPFdt t→⬁ T
(2)
0
fH
Gx(f)df ⫽ 2xB
fL
That equation is changed for performing sampling operation within the computer system into desecrate signal processing form Eq. (3), which is average energy with data number, N.
冘 N
2xBPF ⫽
1 x2 N n ⫽ 1 nBPF
(3)
Tool wear is only sensitive to an average energy in special band, from which frequency domain feature is extracted about wear. To apply sampling to continuous signal, recurrence algorithm is selected like Eq. (4).
冘 N
2xBPF ⫽
1 x2 N n ⫽ 1 nBPF
冉
n⫺1 1 ⫽ n n⫺1 ⫽
冘 冊
n⫺1
x2iBPF ⫹
i⫽1
1 2 x n nBPF
(4)
1 n⫺1 2 n ⫺ 1 ⫹ x2nBPF n n
Where initial value 20 ⫽ 0. Using Eq. (4), the average energy of wear signal is continuously monitored. As cutting is started, workpiece and chip have friction with tool face, thus vibrating the cutting system. Fig. 3 shows typical PSD diagram of the machining signal in aspect of diamond tool wear with time steps. Spectrum peaks in the lower frequency region, f1, are utilized as an index presenting various wear stages, which have generally five stages; initial wear, normal wear, micro fracture, wholly wear and tool breakage. Therefore, that is used as an index of tool wear in diamond turning.
4. Experimental results and considerations Fig. 4 shows the frequency responses of cutting force signal, including characteristic of phasefrequency and power amplitude-frequency. We know that cutting force signal has a periodic phase fluctuation from Fig. 4(a) and its power amplitude has a constant level on overall frequency range
510
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Fig. 3. Typical PSD diagram of the machining signal for wear monitoring.
Fig. 4. Frequency response of cutting force signals.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
511
from Fig. 4(b), but the small increase below 250 Hz implies that the characteristic of tool wear is added to normal signal. Fig. 5 shows the frequency responses of acceleration signal, including the characteristic of phase-frequency and power amplitude-frequency. The phase of acceleration signal is similar to that of cutting force signal, and its amplitude has little change. This fact implies that cutting force signal is more sensitive than the acceleration signal to wear action in the cutting edge. Fig. 6 plots series of frequency responses of cutting force signal in different cutting length (or cutting time), which is sampled on cutting aluminum with a diamond tool. From this, wear features of diamond tool are extracted by observing the energy of frequency band relating to wear mechanism and the overall distribution of their peaks. The maximum peak appears around 100 Hz and decreases with cutting time. Fig. 7 plots a series of frequency responses of acceleration signal in direction of main cutting in different cutting length (or cutting time), which is sampled on cutting aluminum with a diamond tool. The maximum peak appears around 100 Hz in the form of frequency responses of the cutting force signal. Therefore this frequency, 100 Hz, is considered as the natural frequency in the tool– workpiece system. Fig. 8 shows wear patterns of diamond tool with cutting time in machining aluminum using
Fig. 5. Frequency response of acceleration signals.
512
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Fig. 6.
Spectrum analysis of cutting force signal for tool wear monitoring.
microscope photographs of cutting edge which include rake face and flank face of tool after machining for about 20 and 40 min. Fig. 8(a) shows wear patterns of diamond tool in cutting 20 m, cutting speed 251 m min−1, and cutting time 20 min; showing small craters of wear in rake face. Fig. 8(b) shows rake face and cutting edge line of tool have not changed in overall shape after machining for about 20 min. Fig. 8(c) shows a flank face which is free from severe wear but having several micro-chippings in edge. Tool life of diamond is mainly determined faster by observing its chipping, than by the wear shown on aluminum when machining. Therefore, the micro-chipping in the cutting edge is considered as the indicator of tool life in diamond turning because of the ease in detecting them. Fig. 8(d)–(f) shows microscope photographs of cutting edge which include rake face and flank one of tool after machining for about 40 min. Fig. 8(d) shows wear patterns of diamond tool in cutting 20 m, cutting speed 251 m min−1, and cutting time 40 min, showing micro-chipping in the cutting edge and a large breakage in the corner. This pattern is dominated by chipping in the cutting edge observed in both rake face and flank face. Fig. 8(e) describes rake face and cutting edge line of tool, with micro-chipping and large chipping in the corner. Wear in flank face as shown in Fig. 8(f) does not relatively progress. Generally, wear is occurring at the three main faces on the cutting edge involving the wear process of rake face, upper flank face, and side flank face. At the point intersecting these three faces, wear is most severe and extending from that edge point.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
513
Fig. 7. Spectrum analysis of acceleration signal for tool wear monitoring.
5. Conclusions The frequency response of two signals are investigated in order to monitor diamond tool wear during a magnetic disc being machined by the ultra-precision lathe. Conclusions are a result of analysing tool wear patterns and experiments, and are as follows. 1. A new method of monitoring states in face-cutting with diamond tool is developed using frequency response of multi-sensors signal, including the algorithm extracting wear feature in terms of the energy within the specific frequency band. 2. Wear features of diamond tool are dominated by micro-chipping rather than crater wear and flank wear, with the observation of the energy of frequency band relating to tool wear.
References [1] J.M. Oomen, J. Eisses, Wear of monocrystalline diamond tools during ultraprecision machining of nonferrous metals, Precision Engineering 14 (4) (1992) 206–218. [2] R. Wada, H. Kodama, K. Nakamura, Y. Mizutani, Y. Shimura, Wear characteristics of single crystal diamond tool, Annals of the CIRP 29 (1) (1980) 47–52. [3] N. Ikawa, S. Shimada, H. Morooka, Technology of diamond tool for ultraprecision metal cutting, JSPE 21 (4) (1987) 233–238. [4] T.J. Ko, D.W. Cho, J.M. Lee, Fuzzy pattern recognition for tool wear monitoring in diamond turning, Annals of the CIRP 41 (1) (1992) 125–128.
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I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
Fig. 8. Wear characteristics of diamond tool with cutting time in cutting A1.
I.-H. Choi, J.-D. Kim / International Journal of Machine Tools & Manufacture 39 (1999) 505–515
515
[5] C.J. Wong, Fracture and wear of diamond cutting tools, J. of Engineering Materials and Technology Trans. of the ASME 103 (1981) 341–345. [6] J.-D. Kim, I.-H. Choi, Development of a tool failure detection system using multi-sensors, Int. J. Mach. Tools Manufact. 36 (8) (1996) 861–870. [7] F. Giusti, M. Santochi, G. Tantussi, On-line sensing of flank and crater wear of cutting tools, Annals of the CIRP 36 (1) (1987) 41–44. [8] J. Colgan, H. Chin, K. Dansi, S.R. Hayashi, On-line tool breakage detection in turning: A multi-sensor method, J. of Engineering for Industry, Trans. of the ASME 116 (1994) 117–123. [9] C.Y. Jiang, Y.Z. Zhang, H.J. Xu, In-process monitoring of tool wear stages by the frequency band-energy method, Annals of the CIRP 36 (1) (1987) 45–48.