• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.176-179
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• 1998

In general, machining time by electrical discharge machining (EDM) process is much longer than that of cutting process, so rough-cut has done for the purpose of reducing machining time prior to EDM Nowadays EDM speed is improving due to the advance of EDM capacity. Therefore a new method, machining a raw material directly by EDM without rough-cut, is used widely. EDM area is varies according to the EDM position in three-dimensional EDM process, so EDM parameters should be determined adaptively based on the EDM area to increase productivity. However it is difficult to calculate EDM area corresponding to the EDM position the EDM workers who have experience in shop floor determine machining parameters by experience. This paper proposes a method for determining EDM parameters based on EDM area corresponding to EDM position.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.35-39
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• 1998

Determination of effective EDM parameters is significant to increase productivity. However, With the complexity of EDM phenomena, a universal selection method of EDM parameters has not been established yet. Moreover, No attempt has been tried before to suggest a logical method in determining essential machining parameters for effective electrical discharge machining. Peak current, one of the most significant factors in EDM, is proportional to EDM area. This paper presents a method that can be Z-map modeling from CMM data, and calculate EDM area using Z-map.

• IE interfaces
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• v.11 no.1
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• pp.145-153
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• 1998

In this paper, two parts of neural networks were proposed for determination of optimal EDM parameters. One is pattern recognition neural network that can be selecting expert neural network suitable to the EDM mode. The other is expert neural network that can be determining optimal EDM parameters such as pulse on time and pulse off time. Prior to determination of EDM parameters, Peak current, which is related to the EDM area closely, determined base on EDM area that is calculated from CAD data, firstly. Then, the other EDM parameters determined by the expert neural network that is selected to the EDM mode.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.1003-1007
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• 2002

Micro-EDM is generally used far machining micro 3-D structure. For micro-EDM, first of all, micro-electrode fabrication is needed and WEDG system is proposed for tool electrode fabrication method. When tool electrode is fabricated using WEDG system, its characteristics are under the control of many EDM parameters. Also relations between the parameters affect electrode fabrication. In this study, experiments are carried out to analyze effects of EDM parameters on micro-electrode fabrication. Experimental method and analysis are used to experimental design method. Factors used in experiments are composed of applied voltage, capacitance, wire feed rate, spindle rotating speed, machining time. As a result of experiments, wire feed rate, machining time and capacitance is proportional to gap distance(material removal), the other parameters(applied voltage, spindle rotating speed) and relations between the parameters have little influence on machining.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.44-51
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• 1998

Two-step neural network is designed for determining electrical discharge machining parameters in low erosion. The first neural network, which is used as a classification network, checks whether the current conditions are appropriate to electrical discharge machining in low tool erosion. If the conditions are appropriate to EDM in low erosion, suitable EDM parameters are generated by the second neural network. Theoretically known EDM conditions are produced and also utilized for training the second neural network. The trained neural network is tested how well suitable EDM machining conditions are generated under unknown machining situations Experimental result shows that the proposed two-step neural network approach could be effectively used for determining EDM parameters in low tool erosion. The results also have a practical contribution to EDM area in that it could be applied for maintaining low tool wear as well as obtaining maximum machining rates simultaneously.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.218-222
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• 2002

Micro-EDM is generally used far machining micro holes, pockets, and 3-D structures. For micro-EDM, first of all, micro-electrode fabrication is needed and WEDC system is proposed for tool electrode fabrication method. When tool electrode is fabricated using WEDG system, its characteristics are under the control of many EDM parameters. Also relations between the parameters affect electrode fabrication. In this study, experiments are carried out to analyze effects of EDM parameters about electrode surface integrity on micro-electrode fabrication. Experimental method and analysis are used to experimental design method. Factors used in experiments are composed of capacitance, resistance, pause time, wire feed rate, spindle rotating speed. As a result of experiments, capacitance and resistance affect electrode surface.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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• pp.58-64
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• 2000

This paper proposes a method for determining optimal EDM parameters based on discharge area from the physical model of a tool electrode. Main parameters, which affect the EDM performance, are peak value of currents, pulse-on time, and pulse-off time. Such parameters are closely dependent on the discharge area in EDM process. In this paper the discharge area is estimated from the CMM scanning data to the tool electrode. The method is very useful when any geometric information to the tool electrode is not provided from tool modeler or producer. The method consists of following four steps. First a triangulation mesh is constructed from the CMM data. Secondly, the z-map is modeled from the triangulated mesh. Thirdly, the discharge area is estimated from intersection between the z-map model and a z-height plane. Finally, the machining parameters are easily calculated by some known EDM equations to the discharge area. An example is introduced to show that the machining parameters are calculated from the CMM data to a tool electrode.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.43-49
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• 1998

Adaptive neural network approach is presented for determining Electrical Discharge Machining (EDM) parameters. Electrical Discharge Machining has been widely used with its capability of machining hard metals and tough shapes. In the past few years, EDM has been established in tool-room and large-scale production. However. in spite of it's wide application, an universal selection method of EDM parameters has not been established yet. No attempt has been tried before to suggest a logical method in determining essential machine parameters considering the machining rate and resulting surface roughness integrity. The paper presents a method, which is focusing on determining appropriate machining parameters. Depending on the electrode wear and surface roughness, an adaptive neural network is designed for providing suitable machining guideline.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.4
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• pp.74-78
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• 2008

High-speed tool steels are extensively used in tooling industries for manufacturing cutting tools, forming tools, and rolls. Electrical discharge machining (EDM) has been found to be an effective process for machining these extremely hard and difficult-to-cut materials. Extensive research has been conducted to identify the optimum machining parameters for EDM with different tool steels. This paper presents a fundamental study of the surface characteristics of SKH-51 tool steel machined by micro-EDM, with particular focus on obtaining a better surface finish. An RC pulse generator was used to obtain a better surface finish as it produces fine discharge craters. The main operating parameters studied were the gap voltage and the capacitance while the resistance and other gap control parameters were kept constant. A negative tungsten electrode was used in this study. The micro-EDM performance was analyzed by atomic force microscopy to determine the average surface roughness and the distance between the highest peak and lowest valley. The topography of the machined surface was observed using a scanning electron microscope and a digital optical microscope.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.259-260
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• 2002

In this paper, electrical discharge machined (EDM) surfaces machined with various machining parameters are characterized and simulated. Three-dimensional surface topography of EDM surfaces are measured by a stylus instrument. Surface topography is characterized with auto-correlation coefficient and height probability density functions. Then, EDM surfaces are modeled and computer-simulated by using the non-causal 2-D auto-regressive model. Simulation results show that EDM surfaces are characterized well by a few parameters.