• 대한안전경영과학회지
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• 제10권4호
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• pp.127-134
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• 2008

The objective of this study is to evaluate the mechanical behaviors and structural integrity of the weldment of high strength steel by using an acoustic emission (AE) techniques. Monotonic simple tension and AE tests were conducted against the 3 kinds of welded specimen. In order to analysis the effectiveness of weldability, joinability and structural integrity, we used K-means clustering method as a unsupervised learning pattern recognition algorithm for obtained multi-variate AE main data sets, such as AE counts, energy, amplitude, hits, risetime, duration, counts to peak and rms signals. Through the experimental results, the effectiveness of the proposed method is discussed.

• 한국정밀공학회지
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• 제11권4호
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• pp.69-76
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• 1994

AE(Acoustic Emission) signal is correlated to workpiece material, cutting conditions and tool geometry during metal cutting. The relationship between AE signal and cutting parameters can be obtained by theoretical model and experiments. The value of CR(Count Rate) is nearly constant in stable cutting, but when the chatter vibration occours, the value of CR is rapidly increased due to the vibration deformation zone. By experimental signal processing of AE, it is more effective than by RMS(Root Mean Square) measurement to detect the threshold of chatter vibration by CR measurement.

• Tribology and Lubricants
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• 제18권5호
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• pp.351-356
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers on the contacting surfaces in real time. The FFT(Fast Fourier Transform) analyses of the AE signal are sued to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.3383-3388
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• 2007

To compare the sensor performance of AE leak diagnosis system which can measure valve leak conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured on valve of the simulated test system for power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, pressure difference, valve size and fluid using both piezoelectric acoustic emission sensor and Pb-Free acoustic emission sensor. The results of this 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 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.

• 한국생산제조학회지
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• 제23권4호
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• pp.330-336
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• 2014

The continuous chip generated in cutting process deteriorates workpiece, tool, and machine tool system. It is necessary to treat this continuous chip in ductile material machining condition for stable cutting. This paper deals with the chip control method using acoustic emission(AE) signal in pure copper turning operation. AE raw signals, root mean square(RMS) signals and wavelet transformed signals measured in turning process are introduced to analysis for chip patterns. With analysis of AE signals, it is obtained that the produced chip patterns are correlated with the specified AE signals which are transformed by fuzzy pattern algorithm. By this experimental investigation, the chip patterns can be classified at significant level in pure copper machining process and controlled from continuous chips to reduced-length stable chips.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.80-84
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• 1996

This paper aims at reviewing the Possibility application over normal or abnormal, detection used by AE and the wear characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and hE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece; arcyl and bovine. According to the experiment results, the following can be expected; AE has the possibility to detect the state normality and abnormality. However, the grinding resistance measuring can find it difficult to detect it. It can be accurately excerpted from AE occurrence pattern in contact start point of diamond bur and bovine, grinding condition and derailment point. It is known that AE$\_$rms/ is well compatible with grinding resistance. According to the increase of the material removal rate, the specific energy of the diamond bur is inclined to decrease and the grinding resistance has a tendency to increase.

• 한국정밀공학회지
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• 제12권4호
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• pp.19-27
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• 1995

Face milling is required to study cutting process with a view of multipoint cutter. This experimental study mainly deals 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 molling 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. The cutting force and AE signal are monitored to make an analysis of cutting process. The surface roughness of the specimens machined by inserts of different numbers is measured at different speeds, feeds and depth of cut. The width of flank wear is also observed.

• 한국기계가공학회지
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• 제11권2호
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• 대한기계학회논문집A
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• 제33권5호
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 추계학술대회 논문집
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• pp.51-52
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• 2007

This paper compared wired Acoustic Emission (AE) signals with wireless AE signals. According to the material and process condition, each process signal has distinguishable characteristic to show each removal phenomenon. Therefore, wired and wireless AE sensors having different bandwidth are complementary for CMP process monitoring. Especially, the AE sensor was used to investigate abrasive and molecular-scale phenomena during CMP process, which was compatible to acquire high level frequency. In experiment, wireless AE system was used to get signals in rotary system, using bluetooth. But, it is possible to acquire only RMS signals, which can not analyze abrasive and molecular-sale phenomena. Second, wired AE system was installed using mercury slip-ring, which is suitable not only for rotation equipment but also for acquiring original signals. The acquired signals were analyzed by FFT for understanding of abrasive and molecular revel phenomena in CMP process, finally, we verified that two types of AE sensor with different bandwidth were complementary for CMP process monitoring.