• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.7 s.94
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• pp.1783-1793
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• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.108-113
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• 2005

In this study, rotary type diamond dressing system for ultra-precision centerless grinding for ferrule was developed at the first time and experiments were conducted with AE sensor and hall sensor system to verify the optimum dressing condition for ultra-precision centerless grinding for ferrule. The correlations with the condition of dressing are evaluated by AE signal analysis with root mean square (RMS) and frequency analysis. And current signals from hall sensor are also studied as a factor of dressing optimum condition selection. Dressing process was conducted to investigate the effects of depth of cut, rotating speed, and the number of overlap to select the optimum condition of rotary dressing system of ultra-precision centerless grinding machine for ferrule fabrication. In order to verify the optimum condition of dressing, AE and current signals were compared with the surface quality of dressing wheel and grinding wheel for ultra-precision ferrule grinding. All of these experiments were completed by Taguchi Methodology to reduce experimental time. Hence, the optimum condition of rotary dressing system for ultra-precision centerless grinding for ferrule fabrication can be selected following to the experiment result from signals of AE and hall sensor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.650-655
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• 1993

Carbon/epoxy composite(CFRP) coupons previously damaged by low velocity impact were tested under static tensile loading and microscope progress of damage was characterized by ultrasonic C-scan, Scanning Acoustic Microscopy (SAM) and Acoustic Emission(AE) techniques which were based on the application of elastic waves. The degress of impact damage has been correlated with the AE activity during monotonic or loading/unloading tensile testing as well as the result of ultrasonic test. The coupons were subjected to impact velocities ranged from 0.71 to 2.17 m/sec, which introduced the amount of damage rated as 0%, 10%, 30%, and 50% with reference to the total absorbed energy at fracture. Special attention was paid to determine optimal AE parameters to characterize the microscopic fracture process and to predict the residual strength of composite laminates. AE RMS voltage during the early stage of tensile loading was found an effective parameter to quantify the degree of impact damage. It was also found that the Felicity ratio is closely related to the stacking sequence and the residual strength of the CFRP laminates.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.106-111
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• 1993

This experimental study mainly deal with the single and multi-insert cutting characteristics using coated tool. Because metal cutting of the single and multi-insert has a large relation to the improvement of productivity, the economic cutting process can be achieved by the analysis of proper metal cutting mechanism. Therefore, machining characteristics of face milling in this paper has been studied by investigating the role of different insert number which is concerned with mean cutting force, the RMS values of AE(acoustic emission) signal, tool life and surface roughness in milling SS 41 and SUS 304.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.52-59
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• 1999

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

• 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 the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.5
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• pp.672-678
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• 2009

Acoustic Emission (AE) technique is a non-destructive testing method and widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it's easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.

• Journal of the Korean earth science society
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• v.41 no.4
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• pp.356-366
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• 2020

The accuracy of satellite-observed sea surface salinity (SSS) was evaluated in comparison with in-situ salinity measurements from ARGO floats and buoys in the seas around the Korean Peninsula, the northwest Pacific, and the global ocean. Differences in satellite SSS and in-situ measurements (SSS errors) indicated characteristic dependences on geolocation, sea surface temperature (SST), and other oceanic and atmospheric conditions. Overall, the root-mean-square (rms) errors of non-averaged SMOS SSSs ranged from approximately 0.8-1.08 psu for each in-situ salinity dataset consisting of ARGO measurements and non-ARGO data from CTD and buoy measurements in both local seas and the ocean. All SMOS SSSs exhibited characteristic negative bias errors at a range of -0.50- -0.10 psu in the global ocean and the northwest Pacific, respectively. Both rms and bias errors increased to 1.07 psu and -0.17 psu, respectively, in the East Sea. An analysis of the SSS errors indicated dependence on the latitude, SST, and wind speed. The differences of SMOS-derived SSSs from in-situ salinity data tended to be amplified at high latitudes (40-60°N) and high sea water salinity. Wind speeds contributed to the underestimation of SMOS salinity with negative bias compared with in-situ salinity measurements. Continuous and extensive validation of satellite-observed salinity in the local seas around Korea should be further investigated for proper use.