• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.137-142
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• 2001

Since recent production system becomes that of the small quantity, large volume with high quality production, accurate and high speed inspection system is required. In such situation, noncontact 3D measurement system which utilized CCD cameras is useful technique in terms of system cost, speed of data acquisition, measuring accuracy and application. However, it has low accuracy compared with contact 3D measurement system because of the camera distortion, non uniformity of laser distribution and so on. For those reasons, in this paper precision enhancement method is studied considering radial camera distortion, and laser distribution. A distortion correction method is applied even to the standard lens. The laser slit beam trajectory is determined by 3 method: based of the Gaussian function signal approximation, the median method, the center of gravity method and the peak point of the Gaussian function method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.6
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• pp.56-63
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• 2004

The real-time NURBS interpolation method using 2-stage interpolation is studied. The 2-stage interpolation method that compensates for interpolation errors within machine BLU is proposed. The interpolation result was filtered by an Acceleration/Jerk limitation equation. Through this 2-stage interpolation, both the interpolation error condition and the motion kinematics could be satisfied. Using computer simulation in which interpolation results are evaluated by a numerical iteration method, it is shown that the 2-stage interpolation algerian could interpolate target curves precisely with geometric and dynamic contentment. The proposed algorithm was implemented in the CNC simulator system and an experimental un was conducted to identify the real-time adaptation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.96-100
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• 2007

Condenser lens and objective lens are used to demagnify the image of the crossover to the final spot size. In lens, electrons are focused by magnetic fields. This fields is fringing field. It is important in electron focusing. Electron focusing occurs the radial component field and axial component field. Radial component produces rotational force and axial component produces radial force. Radial force causes the electron's trajectory to curve toward the optic axis and corss it. Focal length decreases as the current of lens increases. In this paper, we use the focal length for desiging the hardware of lens current control and present the results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.154-161
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• 1999

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.679-684
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• 2000

This paper presents the development of system and program for a Parallel-Typed CNC Machine. The system consists of parallel manipulator, PC (Personal Computer), DMC (DSP Motion Controller), and machining tools. In order to control the manipulator, the program, which is implemented in "c/c++" language, involves inverse/direct kinematics, velocity mapping, Jacobian and etc. A controller computes the kinematic formulation in real-time and generates and motion by the DMC. A monitor, which has access to program and sensory information, displays the status of manipulator.nipulator.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.199-203
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• 2000

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.309-314
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• 2004

A flexible disk grinding system process has been introduced that utilized varying disk orientation with respect to workpiece along with the applied feed speed. Various process conditions including cutting speed, maximum feed speed and orientation angles could applied to observe process outcomes. Even though continuous and constant feed speed has been applied to the process, the results from the trapezoidal input velocity profiles would be observed and compared. Based on the control strategies including neural network methodologies, several output results were compared to find the optimum process condition. Two axis control results were displayed showing better performance with higher trajectory error for larger training epoch.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.