• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1995.10a
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• pp.68-75
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• 1995

The milling process is one of the most important metal removal processes in industry. Due to the complexities inherent to the cutter insert geometry and the milling cutter kinematics, these processes leave an analytically difficult to predict texture on the machined surface's hills and valleys. The instantaneous uncut chip cross sectional area may be estimated by the relative position between the workpiece and the cutter inserts. furthermore, since the cutting forces are proportional to the instantaneous uncut chip cross sectional area, the cutting forces in face milling operations can not be estimated easily. A new simulation program which is based upon the numerical method has been proposed to estimate the cutting force components, with the ability to predict the machined surface texture left by the face milling operation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1816-1819
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• 2003

As a result of milling operation, we expect to have burr at the outward edge of workpiece. Also, it causes undesirable problems such as deburring cost, low quality of machined surface, and bottleneck in manufacturing process. Though it is impossible to totally remove burr in machining, it is necessary to plan a machining process that minimizes the occurrence of burr. In this paper, a scheme is proposed which identifies the tool path of the milling operation with minimum burr. In the previous research, a Burr Expert System was developed where the feature identification, the cutting condition identification, and the analysis on exit burr formation are the key steps in the program. The Burr Expert System predicts which portion of workpiece would have the exit burr in advance so that we can calculate the burr length of each milling operation. Here, the critical angle determines whether the burr analyzed is an exit burr or not. So the burr minimization scheme becomes to minimize the burr with critical angle. By iterating all the possible tool paths in certain milling operation, we can identify the tool path with minimum burr.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.8
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• pp.55-62
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• 2002

A burr has been defined as undesirable projection of material formed as the result of plastic flow from a cutting or shearing operation. It is unavoidable in all kinds of machining operation. As a result, burr makes troubles on manufacturing process due to deburring cost, quality of products and productivity. In face milling operation, burrs are formed along five edges on the workpiece. In this study, the primary interest is about exit burr The influence of the cutting parameters on the formation of exit burrs in face milling will be described experimentally. Using the results of experimental study, burr types are classified according to appearance and formation mechanism in exit burr. The burr formation mechanism in each type of burr is suggested. Data bases are developed to predict burr formation result.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.12
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• pp.2268-2278
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• 1992

The vibratory modal for the face milling operation is assumed as a multi degrees of freedom system. The parameters of the system are determined based on the cutting experiment. From the relative displacements of this system the dynamic cutting forces were derived and simulated by the double modulation principle. The simulated cutting forces and measured cutting forces have a good agreement in time and frequency domains.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.23-36
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• 2000

A probabilistic model of burr formation in face milling of gray cast iron is proposed. During a face milling operation, an irregular pattern of the edge profile consisting of burrs and edge breakouts is observed at the end of cut. Based on the metal cutting theory, we derive a probabilistic model. The operational bayesian modeling approach is adopted to include the relevant theory in the model.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.87-96
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• 1996

On face milling operation a newly optimal tool, which can minimize the resultant cutting forces resulted from the cutting force model, was designed and manufactrued. Cutting experiments using the new and conventional tools were carried out and the cutting forces resulted from those tools were analyzed in time and frequency domains. The performance of the optimized cutter was tested through the dynamic cutting forces resulted form the newly designed tool are much reduced in comparision with those from the conventional tool. By reducing the dynamic cutting force fluctuations, machine tool vibrations can be reduced, and stable cutting operation can be carried out.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.1
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• pp.116-129
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• 1991

A cutting force model for face milling operation using 3-directional specific cutting force coefficients is developed. The model is taken into consideration factors such as cutter geometry, machining conditions, spindle eccentricity, insert initial postion errors, etc. The simulated force in X, Y, Z directions from the model are subsequently compared with measured forces in the time and frequency domains. The simulated forces have a good agreement with measured forces.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.38-43
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• 1994

In milling process, monitoring and diagosis system is very importent to accomplish factory automation. In this study, to drvelope on-line tool breakage detection system in face milling operation, analysis and experiment were performed. The tool breakage detection experiment was performed in machining center and the effectiveness of the detection tool breakage detection alorithm and the usage of feed drive current as a detection signal were verified.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.1-9
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• 2001

The promise of improved productivity and quality has lead to numerous research investigations in machining process monitoring and control. Recent studies have demonstrated that careful attention must be paid to the regulation of multiple process modules within a single operation such that each module performs its function properly and adverse interactions between modules do not occur. This had lead to the development of supervisory control; particularly to the development of methodologies to systemati-cally construct and implement these controllers. However, no research study has investigated the effect of the production environ-ment on the design of supervisor controllers. In this paper, the design of supervisory controllers for various production environ-ment is studied. The design approach given in Landers and Ulsoy(1998) is applied to construct two supervisory machining control-lers that are experimentally implemented in a face milling operation. Comparisons with an experimental implementation without process control illustrate the benefits of utilizing process controllers that are coordinated properly. The results also show that the given design approach may be used to construct supervisory controllers for different types of production environments.