• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.298-301
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• 1992

A digital image processing approach has been adopted to measure the flank wear area, which is very difficult to measure using conventional techniques. Automatic thresholding of the gray-level values of an image is very useful in automated analysis of image. 1-D entropy thresholding technique is used for image processing and analysis of the flank wear area. This strategy provides more information about drill wear conditions and should therefore have a higher reliability than previous methods. This study calulated quantitatively the flank were area of drill by computer program.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.8
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• pp.107-115
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• 2001

Drilling torque was measured indirectly using the spindle motor current and controlled in real time through feedrate manipulation in a machining center. The PID controller designed in the previous paper was applied to drilling torque control. A series of cutting experiments were performed for various cutting conditions. Experimental results showed that the drilling torque was well regulated at a given reference level by feedrate manipulation in all cutting conditions. The increase in the cutting torque and temperature according to the increase in machining depth was suppressed and the risk of the drill failure and the drill flank wear were reduced remarkably through cutting torque control. Moreover, the suggested cutting torque control system doesn\`t disturb the cutting process and is practical for industrial environment. Therefore, the proposed culling torque control system will contribute to productivity improvement in drilling process.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.148-154
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• 2001

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. A drill-wear monitoring system provides information about drill status. With the information, optimum planning for tool change is possible. And drill-wear monitoring system in needed to evaluated drilled hole quality and the wear of drill. Accordingly, this paper deals with an on-line drill wear monitoring system of the detection of tool wear with the computer vision and the area of the drill flank wear is analyzed quantitatively by the system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.29-36
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• 2004

An experimental study on drilling of stainless steel is conducted using TiAlN coated drills and HSS twist drills with several cutting conditions; feed rate, spindle rotational speed, and dry/wet cutting. The effects of number of hole on the thrust force are examined by cutting force measurement. The flank wear of the drills and the change of hole diameter are quantitatively observed using a vision system. It is found that the thrust force in drilling with TiAlN coated drills decrease under dry and wet machining, whereas the flank wear resistance is improved.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.2
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• pp.46-56
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• 1996

Drilling tests were carried out austempered ductile castiron(ADI) to clarify the factors influencing the drilling characteristics of ADI material. The machinability of material was evaluated using high speed steel drill and cobalt contained drill of 6mm diameter. The spheroidal graphite cast iron materials were austenized at 90$0^{\circ}C$ for 1 hour and then wear was kept at 375$^{\circ}C$ for 2 hours. Austempered ductile cast iron contains a great deal of retaine austenite which contributes to an improvement of impact strength, In this paper, machinability of ADI was investigated by drilling experimentation. The results obtained are as follows: a)Flank wear increases logarithmically with the increases of cutting time. b) Relation of flank wear and cutting force can be appiled to $F_z$ = 925VB + 820 for the cutting suggested condition. c) Drilling hole number of about 2 times can be reduced more step feed than ordinary feed due to the high hardness of ADI material and hardness increasing ascribed to the martensite of retained austenite.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.7
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• pp.36-44
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• 1996

Drilling tests were carried out austempered ductile cast iron(ADI) to clarify the factors influencing the drilling characteristics of ADI material. The machinability of material was evaluated using high speed steel drill and cobalt contained drill of 6mm diameter. The spheroidal graphite cast iron materials were austemized at 900 .deg. C for 1 hour and then wear was kept at 375 .deg. C for 2 hours. Austempered ductile cast iron contains a great deal of retained austenite which contribustes to an improvement of impact strength. In this paper, machinability of ADI was invastigated by drilling experimentation. The results obtained are as follows:a) Flank wear incresses logarithmically with the increases of cutting time and proportionally with the increases of cutting force. b) Drilling hole number of about 2 times can be educed more step feed than ordinary feed due to the high hardness of ADI material and hardness increasing ascribed to the martensite of retained austenite.