• The Journal of Engineering Geology
• /
• v.6 no.2
• /
• pp.59-64
• /
• 1996

Acoustic Emission(AE) signals are emitted by a sudden release of strain energy associated with material damage. A multi-channels of LeCroy system and piezoelectric pressure transducers are employed for AE measurement to investigate the roles of AE in the propagation of macro cracks as well as the characteris-tics of AE wave in occurrence, amplitude and dominant frequency with changes in macro loading modes. Deduced crack opening volume of micro cracks varied widely and implies that AE events could be caused by crystal dislocations on a small scale and grain boundary movements on a large scale. Amplitude of first arrival AE wave emitted during mode I test was approximately 3 times higher than those from mixed mode test, while the number of AE count in mode I test was only 25% of mixed mode. It may imply that the total energy required for generation of a given fracture surface is similar regardless in change of macroloading modes.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.6
• /
• pp.48-57
• /
• 1998

Abrasive waterjet(AWJ)은 가공시 열에 의한 가공경화가 없기 때문에 유리, 세라믹, 타이타늄및 금속복합재료와 같은 난삭재의 가공기술로 사용이 증가되었다. Acoustic emission(AE)신호에 의한 AWJ 세라믹 drilling가공시 On-Line Monitoring의 가능성이 고찰되었다. 기계 적인 물성이 서로 상이한 3종류의 세라믹이 본 연구에서 사용되었으며, AE신호는 AWJ drilling의 깊이를 monitoring하는데 유용함을 알 수 있었고 또한 세라믹의 material removal mechanisms을 규명하였다.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.1
• /
• pp.54-61
• /
• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.6
• /
• pp.64-71
• /
• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$

• Composites Research
• /
• v.14 no.1
• /
• pp.1-7
• /
• 2001

In this study, damage evaluation of glass fiber reinforced thermoplastic composites was investigated with acoustic emission method. Specimens of 1.7mm thickness laminate were made from PET and 7 layers o171ass fabrics. Notch and impact loading were added to the specimen and normal tensile test and tensile test with the dead load were carried out. AE signal was measured as the functions of notch ratio to the width0 and impact energy in order to find out the correlation between fracture mode and AE parameters. The result has shown that low amplitude of AE signal was due to the microcrack of matrix and its growth, whereas the amplitude in the mid range was the response to the delamination and interfacial separation. In the range of high amplitude above 90dB. the fracture of glass fabric was found. Tensile strength decreased with increasing notch ratio to the width and impact energy because of tile effect or delamination, the cracking of matrix and stress concentration. In proportion to the size of damaged area. AE signal showed its wider range of frequency and energy as well as increased number of hits.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.31 no.5
• /
• pp.500-507
• /
• 2011

Acoustic emission(AE) signals during the polymerization shrinkage of composite resin subjected to the LED light exposure were detected through a wave guide method and a direct sensor attachment method. For PMMA, human tooth, stainless steel substrate, data of AE hits and amplitudes were compared. For the test using the wave guide, AE amplitudes decreased because of the attenuant wave. However, AE hits and 1st peak frequency distribution were not different according to the sensor attachments. Through the experiments, wave guide could be used for a nondestructive evaluation of the marginal disintegrative fracture of dental restoration.

• Composites Research
• /
• v.18 no.2
• /
• pp.1-12
• /
• 2005

Fracture behaviors of single-edge-notched monolithic aluminum plates and glass fiber/aluminum laminates under tensile loadings have been studied using acoustic emission(AE) monitoring. AE signals from monolithic aluminum could beclassified into two different types. For glass fiber/aluminum laminates, AE signals with high amplitude and long duration were additionally confirmed on FFT frequency analysis, which corresponded to macrocrack propagation and/or delamination. AE source location determined by signal arrival time showed the zone of fracture. On the basis of the above AE analysis and fracture observation, characteristic features of fracture processes of single-edge-notched glass fiber/aluminum laminates were elucidated according to different fiber ply orientations and fiber/aluminum lay-up ratios.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.16 no.3
• /
• pp.155-161
• /
• 1996

For the condition monitoring of the journal bearing in rotating machinery, a system for their diagnosis by acoustic emission(AE) was developed. AE has been used to detect abnormal conditions in the bearing system. It was found from the field application study as well as the laboratory experiment using a simulated journal bearing system that AE RMS voltage was the most efficient parameter for the purpose of current study. Based on the above results, algorithms and judgement criteria for the diagnosis system was established. The system is composed of four parts as follows: the sensing part including AE sensor and preamplifier, the signal processing part for RMS-to-DC conversion to measure AE ms voltage, the interface part for transferring RMS voltage data into PC using A/D converter, and the software part including the graphic display of bearing conditions and the diagnosis program.

• Composites Research
• /
• v.23 no.6
• /
• pp.39-46
• /
• 2010

Acoustic emission (AE) signals are detected during the polymerization shrinkage of composite resin restoration in artificial dental ring according to various interfacial treatment conditions. AE amplitudes and the number of AE hit events were compared through the non-parametric statistics of Mann-Whitney method and Kruskal-Wallis method. The AE amplitudes detected from the PMMA and human tooth ring specimens were not significantly different according to adhesive conditions. The stainless steel ring specimen, meanwhile, had a difference in AE amplitude (p<0.05). The quantity of hit events for the human molar dentin specimens of the good bonding state was much less than that for the steel ring specimen but more than that for the PMMA ring specimen. For the same substrate, the better the bonding state, the less the AE hit events (p<0.05). The degree of marginal disintegration measured by SEM was proportional to the amount of AE hit events detected.

• Journal of Power System Engineering
• /
• v.13 no.4
• /
• pp.43-48
• /
• 2009

The purpose of this study is to show how to eliminate the noises and to obtain the data related with the relationship between AE signal characteristics and mechanical behaviors for the pressure vessel steel. Various kinds of noises are introduced into the AE data in the course of experiments. Accordingly, real AE data have to be obtained after tests. AE test was carried out under four point bending load. Among AE signals, counts and signal strength are used to find out the differences of AE characteristics between the basemetal and weldment. After tests, this paper shows the procedures of filtering the noises against basemetal of the pressure vessel steel to obtain the real data around crack tip. Relationships between plastic zone size and cumulative AE counts are shown also. AE signals were absent within an elastic region, regardless of the specimens. Most of AE signals are produced in the process of plastic deformation. The deformation and fracture modes of basemetal and weldment are quiet different.