• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• Journal of the Korean GEO-environmental Society
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• v.20 no.4
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• pp.5-9
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• 2019

This paper is to provide the results of a pilot study of the usability and possibility of impact force response signal induced from impacting an object for the assessment of compressive strength of various materials (rock, concrete, wood, etc.) nondestructively. For this study, a device was devised for impacting an object and measuring the impact force. The impact was carried out by an initial rotating free falling impact and following repetitive impacts from the rebound action which eventually disappears. Wood and rock test specimens for different strengths were tested and an impact force response signal was measured for each test specimen. The total impact force signal energy which is assessed from integrating the impact force response signal was compared with the directly measured compressive strength for each specimen. The comparison showed that the total impact force signal energy has a direct relationship with the directly measured compressive strength and the results clearly indicated that the compressive strength of construction materials can be assessed nondestructively using total impact force signal energy which is assessed from integrating the impact force response signal induced from impacting an object.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.1-8
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• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why we estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation which is used in the force estimation process, the transformed signal Is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noise ratio) function is introduced.

• Journal of the Korean GEO-environmental Society
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• v.23 no.10
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• pp.13-19
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• 2022

This paper is to provide the results of usability of the impact force response signal induced from initial and successive rebound impacting a rock specimen for assessing the compressive strength of rock non-destructively. For this study, a device was devised for impacting a rock specimen and a system for measuring the impact force was set up. The impact was carried out by an initial rotating free falling impact and following repetitive impacts from the rebound action which eventually disappears. Three different kinds of rock specimen were tested and an impact force response signal was measured for each test specimen. The total impact force signal energy which is assessed from integrating the impact force response signal induced from initial and rebound impacts was compared with the directly measured compressive strength for each rock specimen. The comparison showed that the total impact force signal energy has a direct relationship with the directly measured compressive strength and the results clearly indicated that the compressive strength of rock can be assessed non-destructively using total impact force signal energy.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.146-151
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• 2001

The importance of sensing the force and torque with arbitrary direction and magnitude is becoming more crucial for robotic applications and manufacturing automations. Recently, several designs of a multi-axis force-torque sensor have been tried to sense this force and torque. This paper deals mainly with the signal processing of a six-axis force-torque sensor using cross-shaped elastic structures with circular holes. In this paper, we show principle of sensing force and torque, the signal processing methodology, and efficient methods of seeking strain gage positions in the sensor structure. The validity of the proposed method is shown via experiments.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.469-476
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• 2005

Performance of a force-torque sensor is affected significantly by an error signal that is included in the sensor signal. The error sources may be classified mainly into two categories: one is a structural error due to inaccuracy of sensor body, and the other is a noise signal existing in sensed information. This paper presents a principle of 6-axis force-torque sensor briefly, a double-hole structure to be able to improve a structural error, and then a signal conditioning to reduce the effect of a noise signal. The validity of the proposed method is investigated through experimental study, which shows that SIN ratio is improved significantly in our experimental setup, and the sensor can be implemented cheaply with reasonable performance.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.25-29
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• 1991

In the field of prosthesis arm control, tile pattern classification of the EMG signal is a required basis process and also the estimation of force from col looted EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why he estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation, which is used in the force estimation process the transformed signal is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noire ratio) function is introduced.

• Current Optics and Photonics
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• v.2 no.2
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• pp.134-139
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• 2018

A novel approach for frequency-tripling vector signal generation via balanced detection without precoding and optical filter is proposed. The scheme is mainly utilizing an integrated dual-polarization quadrature phase shift keying (DPQPSK) modulator. In the DPQPSK modulator, one QPSK modulator is driven by an RF signal to generate high-order optical sidebands, while the other QPSK modulator is modulated by I/Q data streams to produce baseband vector signal as an optical carrier. After that, a frequency-tripling 16-quadrature-amplitude-modulation (16QAM) vector millimeter-wave (mm-wave) signal can be obtained by balanced detection. The proposed scheme can reduce the complexity of transmitter digital signal processing. The results show that, a 4 Gbaud baseband 16QAM vector signal can be generated at 30 GHz by frequency-tripling. After 10 km single-mode fiber (SMF) transmission, the constellation and eye diagrams of the generated vector signal perform well and a bit-error-rate (BER) below than 1e-3 can be achieved.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.30-34
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• 1998

In-process monitoring of cutting conditions and tool wear is important for improving productivity. This paper is concerned with on-line monitoring of tool wear and cutting force in end milling operation. The experimental study deals with the relations between flank wear and cutting force signal. Tool wear is detected by monitoring of cutting signal. A monitoring procedure is shown in this paper. The influence of flank wear on cutting signal activity was examined. The results are presented in the form of graphs. The analysis of the cutting signal and flank wear curves provides useful indicators of unacceptable wear development in the tool.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.3
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• pp.130-143
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• 1995

This paper presents a new methodology for on-line tool breadage detection by sensor fusion of an acoustic emission (AE) sensor and a built-in force sensor. A built-in piezoelectric force sensor, instead of a tool dynamometer, was used to measure the cutting force without altering the machine tool dynamics. The sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A sighificant drop of cutting force was utilized to detect tool breakage. The algorithm was implemented on a DSP board for in-process tool breakage detection. Experiental works showed an excellent monitoring capability of the proposed tool breakage detection system.