• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.253-254
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• 2009

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.225-229
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• 2000

Burrs formed during face milling operations are very hard to characterize like other machining burrs because there are many parameters which affect the cutting process. Many researchers have tried to predict burr characteristics including burr size and shapes with various experimental conditions such as cutting speed, feed rate, in-plane exit angle, number of inserts, etc., but it still remains as a challenging problem for the complicated combination effects between the parameters. In this paper, Taguchi method, which is a systematic optimization application of design and analysis of experiments, is introduced to acquire optimum cutting parameters for burr minimization. Optimized experimental conditions are provided to show the effectiveness of this approach.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.12
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• pp.130-136
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• 2001

Burrs formed during face milling operations are very hard to characterize because there are many parameters that affect the cutting process. Many researchers have tried to predict burr characteristics including burr size and shapes with various experimental conditions such as cutting speed, feed rate, in-plane exit angle, number of inserts, etc., but it still remains as a challenging problem for the complex combined effects between the parameters. In this paper, the Taguchi method, which is a systematic optimization application in design and analysis of experiments, is introduced to acquire optimum cutting parameters for burr minimization in face milling. Also, analysis of variance (AVOVA) is employed to study the performance characteristics in more detail. Experimental verifications are provided to show the effectiveness of this approach.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.602-607
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• 2002

금형가공에서 사용되는 밀링머신의 가공시 공구와 피삭재의 접합면에서는 절삭가공의 잔유물인버(Burr)가 생성되고, 이러한 버는 작업효?ㅘ 정밀도를 감소시키며 후처리과정(Deburring)을 야기시키는 원인이 된다. 그러므로 정밀도와 작업효율을 극대화하기 위해서는 이러한 버의 생성원리를 파악함과 동시에 Exit 버 판별을 하여 최적의 가공조건을 맞추어 주어야 하는데, 이에 밀링가공을 통해 생성되는 버의 형태와 Exit 각 등을 파악, 알고리즘으로 개발하여 좀 더 효율적인 가공조건을 제시하고자 한다. 본 연구는 이제까지 개발된 실제 가공에서 사용되는 임의형상의 데이터를 통해 점, 선, 원 및 원호(Arc)에 이르는 형상을 인식하는 Exit 버 판별시스템을 소개하고자 한다. 아울러 실제 가공과 더욱 유사한 환경을 만들기 위해 단일 경로에서부터 다중경로에 이르는 복합적인 환경 구현은 물론 점과 선으로 이루어진 도형과 원(구멍)형태의 피삭재 환경을 통합, 두 가지 다른 형태의 환경이 복합적으로 존재할 경우의 Data를 인식하고 아울러 다양한 방향성을 가진 선으로 이루어진 형상에 이르기까지의 다양한 피석재 Data를 구현하고, 입력된 절삭조건을 해석하여 임의형상을 가진 다양한피삭재에서의 단일 및 다중 가공 경로 상에서 발생될 버를 해석하고 최소화 및 최적 절삭가공공정을 찾아 작업 효율성을 극대화하는 목적의 해석 알고리즘을 Windows 프로그램으로 구현하고자 한다.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.37-43
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• 2005

Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT-based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.125-133
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• 1998

The objective of this study is to propose a new approach for modelling of burr formation process during orthogonal cutting when the tool exits the workpiece. This approach is based on the rigid-plastic finite element method combined with the ductile fracture criterion and the element kill method. This approach is applied to orthogonal cutting process to predict the fracture location and the fracture angle as well as the cutting force. To validate this approach, orthogonal cutting tests inside SEM(scanning electron microscope) at very low speed are carried out using A16061-T6 to observe the behavior of the material during the chip and the burr formation. The results of the experiment are compared with those of the finite element simulation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.4
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• pp.22-28
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• 2006

Detection of exit burr is very important in manufacturing automation. In this paper, acoustic emission(AE) was used to detect the burr formation during milling. By using wavelet transformation, AE data was compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net. In order to validate the proposed scheme, the wavelet based ANN results were compared with cutting condition(cutting speed, feed, depth of cut, etc.) based ANN results.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.1
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• pp.134-140
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• 2017

CFRP has many industrial applications due to its low weight and high strength properties. CFRP is a composite material composed of carbon fibers embedded in a polymer matrix; it provides excellent resistance to fatigue wear, corrosion, and breakage due to fatigue. It is increasingly demanded in aircraft, automotive, and medical industries due to its superior properties to aluminum alloys, which were once considered the most suitable for specific applications. The basic machining methods of CFRP are drilling and route milling. However, in the case of drilling, the delamination of each layer, uncut fiber, resin burning, spalling, and exit burrs are barriers to successful application. This paper investigates the occurrence of exit burrs when drilling holes with ultrasonic vibration. Depending on design parameters such as the point angle, the characteristics of hole drilling were identified and appropriate machining conditions were considered.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.1-9
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• 2021

In machining operations, a burr is an undesirable material formed by plastic deformation in a workpiece. With the ongoing industrial developments, it has become an important issue to efficiently remove burrs. Several deburring methods have been developed to remove specific burrs that require special machining. However, to remove burrs formed while machining at the CNC machining center, deburring tools must be developed. In a previous study, a new deburring tool was developed by the authors. In this study, the influence of the rake angle and stiffness of the new deburring tool was analyzed to improve performance. The theoretical model was driven considering the rake angle and stiffness, and experiments were carried out to validate the model. Especially, conditions based on the designed rake angle and stiffness to effectively remove burrs, which is difficult, at the exit surface were suggested.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1172-1182
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• 1993

In orthogonal machining a quantitative model for burr formation process and fracture when tool exits workpiece is proposed. When no fracture during burr formation burr formation process is divided by three parts; Initiation, Development and Final burr formation. According to the properties of workpiece fracture will happen or not after initiation of burr formation. Considering the fact that fracture depends on the ductility of workpiece, the fracture strain obtained from ductile fracture criterion is used for prediction. It is verified that the fracture strain from tension test can be used as fracture criterion in burr formation without large error. For detailed observation of burr formation an experimental stage for micro orthogonal cutting inside SEM (Scanning Electron Microscope) is built. Through the comparison between model prediction and experimental result from orthogonal machining in milling machine the model is verified.