• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.365-368
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• 1995

Rotary Compressor has many AE(Acoustic Emission) sources according to condition of parts because it is operated with combination of various parts. In this study, analysis of AE raw signal generated form Rotary compressor which artificially-made parts inflicted abnormal condition was carried out. AE raw signals were acquired form high-speed A/D board, and many burst type signals were observed. By analyzing burst type signals which is caused form internal AE source,efficient AE parameters for monitoring and diagnosis were presented.

• 한국정밀공학회지
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• 제21권11호
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• pp.125-129
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• 2004

For the diagnosis of compressor abnormal condition in air-conditioner, AE signal which is derived from wear condition, compressed air and assembly error is analyzed experimentally. The burst and continuous type AE signal occurred by metal contact and compressed air and AE raw signal of compressors were directly acquired in production line. After extracting samples according to waveforms, Early Life Test(ELT) is conducted and classified to normal and abnormal waveform. The efficient parameters of waveform pattern are investigated in time and frequency domain and the diagnosis algorithm of air-conditioner by Neural Network estimation is suggested.

• International Journal of Precision Engineering and Manufacturing
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• 제7권3호
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.572-577
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• 2011

The acoustic emission (AE) technique is a well-known non-destructive test technique, both in research and for industrial applications. It is mainly used to monitor the onset of cracking processes in materials and components. Predicting and preventing the crack phenomenon has attracted the attention of many researchers and has continued to provide a large incentive for the use of condition monitoring techniques to detect the earliest stages of cracks. In this research, goal is in grasping features of AE signal caused by crack growth. The envelope analysis with discrete wavelet transform (DWT) is used to find the characteristic of AE signal. To estimate feature of divided into three by crack length, the time waveform and the power spectrum were generated by the raw signals and the transferred signal processed by envelope analysis with DWT.

• 대한기계학회논문집A
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• 제38권8호
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• pp.863-867
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• 2014

본 연구에서는 화학 기계 연마(CMP) 공정 중 발생하는 다양한 영역대의 신호를 분석하기 위하여 음향 방출 센서(AE)를 이용하였다. 특히 음향 방출 센서는 공정 중 발생하는 기계적 소음을 전기적 신호로 변환하기 용이하며, 특히 고주파 영역대의 신호를 감지하기에 용이하다. 그래서 본 연구에서는 CMP 장비에 음향 방출 센서를 부착하여 CMP 공정 중 발생하는 신호를 동시에 획득하였다. 본 음향 방출 모니터링 시스템은 CMP 공정 조건 변화 및 패드, 슬러리, 웨이퍼와 같은 소모재의 변화에 따른 신호분석을 하기 위해 제작 되었다. 본 연구에서는 산화막 웨이퍼와 구리막 웨이퍼에 본 시스템을 적용하였다. 음향 방출 센서로 획득한 신호로 Raw 신호 분석, 주파수 분석, 진폭 분석을 통해서 CMP 공정중 발생하는 현상을 분석하였다. 최종적으로 다양한 대역폭의 신호를 음향 방출 센서로 획득하여 CMP 공정 모니터링이 가능함을 확인하고자 하였다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.334-339
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• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• 한국정밀공학회지
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• 제23권1호
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• pp.64-71
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• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• 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 1997년도 춘계학술대회 논문집
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• pp.56-60
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• 1997

The compressor is one of important elements in refrigeration cycle and play an important role of refrigeration efficiency and quality. This paper analyzes slides in rotary compressors for room air conditioners, monitoring using Acoustic Emission(AE) technique. Reliability of rotary compressors which are factory-tested has been evaluated through visual inspection on taking them apart after long term test, which is life test. This paper describes methods for acquisition and processing of Acoustic Emission(AE) raw signal to monitor state of rotary compressor in Life Test.

• 대한기계학회논문집A
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• 제25권5호
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• pp.765-771
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• 2001

Elastic-plastic fracture toughness JIC can be used a s an effective design criterion in elastic-plastic fracture mechanics. Among the JIC test methods approved by ASTM, unloading compliance method was used in this study. In order to examine the relationship between fracture behavior of JIC test and AE signals, the post processing of AE signals has been carried out by Short Time Fourier Transform(STFT), one of the time-frequency analysis methods. The objective of this study is to evaluate the application of characterization of AE signals for unloading compliance method of JIC test. As a result of time-frequency analysis, we could extract the AE from the raw signal and analyze the frequencies in AE signal at the same time. AE signal generated by elastic-plastic fracture of material has some different aspects at elastic and plastic ranges, or the first portion of crack growth by fracture. First of all, increased energy recorded and detected by using AE count method increase rapidly from the start of ductile fracture. The variation of main frequency range with time-frequency analysis method could be confirmed. We could know fracture behavior of interior material by examination AE characteristics generated in real-time when elastic-plastic fracture occurred in material under loading.