• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.832-836
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• 1997

An experimental study on the grinding temperature, surface roughness and Acoustic Emission(AE) signal was conducted with different shapes of wheel. The grinding characteristics by slotted shapes of wheel changed by width and helical angle,were compared with those by general one. Lower grinding temperature was obtained for 30 .deg. helical angel with 10mm width and Root Mean Square(RMS) values of AE signals were lower for slotted shapes rather than general one. Surface roughness characteristic of slotted shapes found to be rough,but the value of roughness for 45 .deg. helical angel with 6mm width, represented to similar tendency general one.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.773-781
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• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.125-130
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• 1994

Recently,manufacturing work been transformed to small acale production from with various items to act up to user's expectation from mass production with a little items required in the past. The FMS using NC type mother machinaries has been applied actively also in domestic manufacturing line to meet thus tendancy, but there are many machining troubles occured in work process not be settled yet. Nowdays high efficiency has been required no less than high precision in grinding work for the improvement of productivity. In this study, to represent more advanced FMS can be adapted to thus situation In-process type monitoring method using AE and Current sensors is suggested to investigatethe machining condition in grinding process. As results from this experimental study, is is recoqnized well that grinding conditions and dressing point of in time can be estimated effectively using monitoring method suggested. Furthermore, surface shape of grinding wheel on voluntary point of in time can be predicted indirectly through the observation and comparison of AE signal waveform obtained as performance of continuous dressing work.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.73-78
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• 1994

Wear and fracture mode of ceramic tool for hardened SKD11 steel was investigated by face milling in this study. The cutting force and Acoustic Emission(AE) signal were utilized to detect the wear and fracture of ceramic tool. The following conclusions were obtained : (1) The wear and fracture modes of ceramic tool are characterized by three types: \circled1wear which has normal wear and notch wear, \circled2 wear caused by scooping on the rake face, \circled3 large fracture caused by thermal crack in the rake face. (2) The wear behaviour of ceramic tool can be detected by the increase of mean cutting force and the variation of the AE RMS voltage. (3) The catastrophic fracture of ceramic tool can be detected by the cutting force(Fz-component). (4) As the hardness of work material increased, Acoustic Emission RMS value and mean cutting force(Fz) increased linearly, but the tool life decreased.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.131-137
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• 1994

Detection of grinding trouble occuring during the grinding process is classified into two types, i.e, based on the quantitative and qualitative knowledge. But, since the grinding operation is especially related with a large amount of functional parameters, it is actually defficult to cope with the grinding troubles occuring during process. Therefore, grinding trouble-shooting has difficulty in satisfying the requirement from the user. To cope with the grinding troubles occuring during the process, the application of neural network is on effective way. In this study, we identify the four parameters derived from the AE(Acoustic Emission) signals and present the grinding trouble-shooting system utilizing a back-propagation model of the neural network.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.299-303
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• 2000

A brushless linear motor is suitable for operation with higher speed and precision. Since it does not involve mechanical coupling, linear driving force can be applied directly. Conventional PID and fuzzy controllers are implemented and performance results using those controllers are compared. Along with better simulated performance observed using fuzzy controller, further fabrication is to be included with various empirical results. Several system operational characteristics have been observed. Typical nonlinearities as friction, cogging and torque or thrust ripple that might deteriorate system performance would be tackled using presumably effective method such as neural network based learning controller.

• Tribology and Lubricants
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• v.15 no.4
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• pp.328-334
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• 1999

Acoustic emission (AE) sensor was used to evaluate the wear-life of CrN-coated steel disks with 1 $\mu\textrm{m}$ and 4 $\mu\textrm{m}$ coating thickness. The relationship between Af and friction signal from scratch test and sliding test was investigated. The first spatting of CrN film was detected by AR signals in the early stage of coating failures, and overall failures by friction signals. Therefore, the conservative design for coating-life should be done using the results of AE signals. Using the percent contact load, the ratio of sliding normal load to the critical scratch load and the number of cycles to failure was measured to predict the wear-life of CrN film. On the wear-life dia-gram the percent contact loads and the number of cycles to failure showed a good linear relationship on the log coordinate. As the load percentage was decreased, the diagram showed that the wear-limits, at which the coated steels survived more than 35,000 cycles, were about 4∼5% of the critical scratch loads.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05c
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• pp.79-83
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• 2001

This is an investigation on the propagation characteristics of AE signal in GIS. The selection of measuring position and resonant frequency of AE sensor is one of the most important factor to optimize a diagnostic system. And natural frequency and harmonic response characteristics are indispensable to optimize those factors. So, in this investigation, we make a 3D model of 362kV GIS and make a modal and harmonic analysis to survey the vibro-acoustic property. Through the result of the analysis, we can make a further understanding on the vibro-acoustic characteristics of GIS.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.245-248
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• 2006

The objective of this study is to take the first step in creating a user-friendly health monitoring system for recycled aggregate concrete structure using acoustic emission(AE). Each specimen was a cube, the edge of which was 150mm. For pull-out tests, a steel rebar, 13mm in diameter, was embedded in the center of each specimen and casting directions(i.e., vertical and horizontal) were considered in this paper. The AE parameters were analyzed for damage levels(i.e. internal cracking stage, pull-out stage) of all specimens. Results from this study show that event, duration versus amplitude of a signal, showed a clear difference for different loading stages depending upon the concrete casting directions.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.381-384
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• 1997

This paper describes the method of diagnosing the degradation by void defects of insulator inside in operation. Needle-shape void specimens, made from LDPE, were used to generate an electrical tree under ac voltage. The method uses a neural network system with input signal of AE patterns. AE pattern consists of the pulse count and average amplitude according to the phase angle. After the learning process was over, unknown emission patterns were put into the network. It was shown that the network discriminates the void deflects well. The effectiveness of the neural network system for partial discharge recognition was shown.