• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1525-1530
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• 2014

In this research, we describe teaching based sweeping control for grinder robot has been proposed as a system which is suitable to work utilizing pressure sensitive alternative to human. Teaching method is used for grinder robots operations because of their position accuracy, path accuracy, and machining reaction force. A grinder robot for two-dimensional iron plate was developed on the basis of an force sensor based teaching method. An automatic-path-generation method and experimental results using specific points was adopted to reduce the number of teaching points and time. And also, in order to determine the proper machining conditions, various machining conditions such as grinder-wheel rotation speed and robot moving speed, were evaluated.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.83-88
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• 1998

This paper presents an efficient control algorithm that removes irregular burrs using vision and force sensors. In automated robotic deburring, the reference force should be accommodated to the profile of burrs in order to prevent the tool breakage. In this paper, (1) The profile of burrs is recognized by vision sensor and followed by the calculation of reference force, (2) Deburring expert's skill is transferred to robot. Finally, the performance of robot is evaluated through simulation and experiment.

• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.110-115
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• 2016

This paper describes the design and fabrication of a three-axis force sensor with three parallel plate structures(PPSs) for measuring force in a finger force measuring system for a spherical object catch. The three-axis force sensor is composed of a Fx force sensor, Fy force sensor and a Fz force sensor, and the elements of Fx force sensor and Fy force sensor are a parallel plate structure(PPS) respectively and Fz force sensor is two PPS. The three-axis force sensor was designed using FEM(Finite Element Method), and manufactured using strain-gages. The characteristics test of the three-axis force sensor was carried out. As a test results, the interference error of the three-axis force sensor was less than 1.32%, the repeatability error of each sensor was less than 0.04%, and the non-linearity was less than 0.04%.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.865-870
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• 2001

A sensing element for tri-axial force measurement, unit sensor of tactile sensor, was designed and evaluated by using finite element method (ANSYS). The sensor has a maximum force range of ${\pm}10$ N in the x, y, and z direction. Optimal cell structures and piezoresistor positions were determined by the strain distribution obtained from finite element analysis. Finally three Wheatstone birdge circuits were arranged and verified by $F_x$, $F_y$, and $F_z$ loading conditions. In addition, in case of sensing element subjected to thermal loading, the outputs of three bridge circuits were also evaluated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1160-1165
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• 2006

In this paper, the 2-dimensional uniaxial force sensors array is introduced to detect the distributed force using fiber Bragg gratings. Uniaxial force transducer was designed to avoid the chirping and micro bending which degrade the performance of the sensor. The Brags wavelength shift of the sensor was estimated using the finite element analysis. Using this uniaxial force sensor, the uniaxial force sensors array $(3{\times}3)$ was fabricated, and the Performance of this sensors array was evaluated. The Presented sensors may has very simple configuration and its wiring is very simple compared with any other force sensors arrays.

• Proceedings of the Korean Magnestics Society Conference
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• 1991.05a
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• pp.39-40
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• 1991

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.187.2-187.2
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1061-1062
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• 2011

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.910-917
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• 2007

Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper. a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process and recognition Phases. In the Pre-Process Phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. Experimental data was generated by six men employing three types of human touching behaviors including 'hitting', 'stroking' and 'tickling'. As the experimental result of a recognizer being generated for each user and being evaluated as cross-validation, the average recognition rate was 82.9% while the result of a single recognizer for all users showed a 74.5% average recognition rate.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.64-70
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• 1999

When manipulator moves the objects, the object position error can be occurred because of acceleration or negative acceleration according to the direction. So we make manipulator working path for establishing optimal gripper force control preventing occurrence of object position error. And we attached the tactile sensor on the gripper of manipulator which gives us very specific information between manipulator and object. Reasoning of continuous tactile image data, manipulator can sense rotation and slippage and change the grasping force that corrects calculated grasping force and compensation can be possible of the object position error. We use the FSR(Force Sensing Resistor)sensor which consists of 22 by 22 taxels and continuous taxel number is used for filtering and using the moment method for sensing algorithm in our experiment.