• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.58-63
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• 2007

A compact gun drill machine was developed to improve productivity and economical efficiency for small and medium enterprise tool makers. Gun drilling works are mainly using at molding, automobile, aircraft industry and special tool makers to make deep holes. As gun drill machines are very expensive and big burden for small tool makers, so that works used to execute through outside orders but it was required lot of cost too. Most of gun drill machines are providing for high volume and large capacity enterprises. In order to use for small and medium enterprises that compact gun drill machine was designed and developed. It could be improved product quality, productivity and manufacturing cost for small and medium enterprises by using this machine.

• 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.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.1
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• pp.65-72
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• 2005

Drilling process is one of the most common, yet complex operations among manufacturing processes. The performance of a drill is largely dependent upon drilling forces, Many researches focused on the effects of drill parameters on drilling forces. In this paper, an effective theoretical model to predict thrust and torque in drilling is presented. Also, with the predicted forces, the stress analysis of the drill tool is performed by the finite element method. The model uses the oblique cutting model for the cutting lips and the orthogonal cutting model for the chisel edge. Thrust and torque are calculated analytically without resorting to any drilling experiment, only by tool geometry, cutting conditions and material properties. The stress analysis is performed by the commercial FEM program ANSYS. The geometric modeling and the mesh generation of a twist drill are performed automatically. From the study, the effects of the variation of the geometric features of the drill and of the cutting conditions of the drilling on the drilling forces and the stress distributions in the tool are calculated analytically, which can be applicable for designing optimal drill geometry and for improving the drilling process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.109-116
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• 2002

Resently, reduction of industrial products in size and weight has increased by the application of micro-drill for gadgets of high precision and gave rise to a great interest in a micro-drilling. Due to the lack of tool stiffness and the chip packing, micro-drilling requires not only the robust tool structure which has not affected by the vibration, but also the effective drilling methods designed to prevent tool fracture from cutting troubles. Firstly, this paper presents a new manufacturing process of micro-drill for improving the product rate and an optimum shape of micro-drill for lengthening the tool life, and secondly suggests between tool life and drilling torque acquired in the inprocess monitoring system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.3
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• pp.8-13
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• 2020

Carbon fiber-reinforced plastics (CFRPs) are extremely strong and light fiber-reinforced plastics containing carbon fibers. CFRPs can be expensive to produce, but are commonly used wherever high strength-to-weight ratio and rigidity are required, such as in the aerospace, automotive, and ship superstructure industries. In CFRP drilling, the tool performance greatly varies depending on the tool shapes, cutting conditions, and diamond coating. This study developed a new type of tungsten carbide drill with multi-blade edges to evaluate the surface quality of CFRP materials according to the coating thickness of diamond-coated drills. Experiments on tool wear, surface roughness, and burr formation were conducted. The bore exit quality of a 12 mμ -coated drill was better than that of a 6 mμ -coated drill. The superior effects of the 12 mμ -coated drill and the good surface quality of CFRP were also demonstrated.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.287-293
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• 2001

Resently, reduction of industrial products in size and weight has increased by the application of micro-drill for gadgets of high precision and gave rise to a great interest in a micro-drilling. Due to the lack of tool stiffness and the chip Packing, micro-drilling requires not only the robust tool structure which has not affected by the vibration, but also the effective drilling methods designed to prevent tool fracture from cutting troubles. Firstly, this paper presents a new manufacturing process of micro-drill for improving the Product rate and an optimum shape of micro-drill for lengthening the tool life, and secondly between tool life and drilling torque acquired in the inprocess monitoring system.

• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.10-17
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• 2001

Drilling is one of the most important operations performed in the machining industry. And the material of the workpiece has a profound effect on the tool life, the surface finish produced and the overall economy of the process. Hot-rolled high strength steels have been used for automobile structural material, owing to high hardness and machinability of the material. However, in the drilling of hot-rolled high strength steels, the current knowledge of tool wear and machinability are insuf-ficient. There, it is desirable to monitor drill wear status and hole quality changes during the hole drilling process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include the drilling thrust, torque and drill wear data obtained from drilling experiments con-ducted on the workpiece. Also drill were is measured by acoustic emission system and computer vision system.