• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.3
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• pp.304-309
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• 2011

A monitoring system for magnetic abrasive polishing process is necessary to ensure the polishing products the high quality and integrity. Acoustic emission (AE) signal is known to reflect the material removal phenomena in other machining processes. In a case of the magnetic abrasive polishing of non-magnetic materials, application of AE method is very difficult because of lower machining force on the surface of workpiece and the level of AE signal is extremely lower. In this study, AE sensor-based monitoring system is applied to the magnetic abrasive polishing. The relation between the level of the AE RMS and the surface roughness during the magnetic abrasive polishing of magnesium alloy steel is investigated.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1349-1352
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• 2005

This paper is to investigate a defect for thermal barrier coating layers by acoustic emission method in 4-point bending test. The two-layer thermal barrier coating is composed of $150\mu{m}\;CoNiCrAlY\;bond\;coating\;by\;vacuum\;plasma\;spray(VPS)\;process\;and\;250\mu{m}\;ZrO_2-8wt%Y_2O_3$ ceramic coating layer by air plasma spray(APS) process on Inconel-718. The specimen prepared by cyclic thermal test(500, 1000, 2000cycle) at $1050^{\circ}C$ The AE monitoring system is composed of PICO type sensor, a wide band pre-amplifier(40dB), PC and AE DSP(16/32 PAC) board. The AE event, amplitude, Cumulative energy and count of coating specimens is evaluated according to cyclic thermal test.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.315-318
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• 1997

In terms of productivity, the speed of machining process has been increasing in most of engineering part. But the tapping process does not reach at enough level compared with other machining processes because of its complicate cutting mechanism. In the high speed tapping process, the one of important elements is tool monitoring system to prevent tool breakage. This paper describes tool monitoring system by acoustic emission(AE) in the tapping process. We used 2 types of AE sensors in this test. The one is commercial sensor which is used in other machining monitoring system like polishing and the other is a self-fabricated sensor for this test. In this test we purpose to find out the frequency of AE signal in tapping process and verify the possibility of applying AE sensor in in-process tapping monitoring system. Also grasp of characteristic of tapping process by AE signal is handled.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.93-101
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• 1997

Ever since the nonlinearity of machine tool dynamics was established, researchers attempted to make use of this fact to devise better monitoring, diagnostics and control system, which were hitherto based on linear models. Theory of chaos which explains many nonlinear phenomena comes handy for furthering the analysis using nonlinear model. In this study, measuring system will be constructed using multi-sensor (Tool Dynamometer, Acoustic Emission) in end milling process. Then, it will be verified that cutting force is low-dimensional chaos by calculating Lyapunov exponents. Fractal dimension, embedding dimension. And it will be investigated that the relation between characteristic parameter calculated from sensor signal and tool wear.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.33-37
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• 2002

Insulation aging diagnosis system provides early warning regarding electrical equipment defects. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear. it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a electromagnetic wave and acoustic signal to diagnose an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System and acquires 2D patterns from analyzing it. For filtering the noise contained in sensor signals we used ICA algorithms. Using this data, we design of the neuro-fuzzy model that diagnoses an electrical equipment and is investigated in this paper. Validity of the new method is asserted by numerical simulation.

• Proceedings of the KIEE Conference
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• 2007.04b
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• pp.131-133
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• 2007

This paper was described about the calibration method and setup of calibration system. This method and system were based on ISO 12713 and 12714. This system consisted of a conical type reference sensor, test block, glass capillary source and measuring oscilloscope. The waves of reference sensor and tested sensor was shown and these wave was analyzed with ISO method.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.199-210
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• 2008

Acoustic emission(AE)/Microseimsic(MS) activities are low-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is not easy to determine the precursor and initiation stress level of failure in displacement detection method. To overcome this problem, AE/MS techniques for detection of structure failure and damage have recently adopt in civil engineering. In this study, AE/MS monitoring system, which consist of sensor, data acquisition and operation program, is constructed with domestic technology. To verify and optimize the developed system, we are now carrying out the field application at an underground research laboratory and the developed AE/MS monitoring will be used in detecting of seismic events with various scales.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.344-348
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• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he 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. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.