• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.975-979
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• 2002

This paper studies the indirect parameters when the new and worn end mill working in the machining center. The parameter output methods are cutting force, current values and AE signals. In the result, when the worn end mill operating, cutting forces increase the 14.71〔N〕, current values increase the 2.917〔A〕 and 1.168〔A〕 according to the spindle mote. and feed motor, and AE signals increase the 0.588$\times$10$^{-5}$ 〔A〕. We can use these parameters in the detection of end mill wear.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.11
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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.

• Smart Structures and Systems
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• v.23 no.2
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• pp.173-184
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• 2019

The main objective of this study is to evaluate the true fracture energy and monitor the damage progression in steel fibre reinforced concrete (SFRC) specimens using acoustic emission (AE) features. Four point bending test is carried out using pre-notched plain and fibre reinforced (0.5% and 1% volume fraction) - concrete under monotonic loading. AE sensors are affixed at different locations of the specimens and AE parameters such as rise time, AE energy, hits, counts, amplitude and duration etc. are obtained. Using the captured and processed AE event data, fracture process zone is identified and the true fracture energy is evaluated. The AE data is also employed for tracing the damage progression in plain and fibre reinforced concrete, using both parametric- and signal- based techniques. Hilbert - Huang transform (HHT) is used in signal based processing for evaluating instantaneous frequency of the acoustic events. It is found that the appropriately processed and carefully analyzed acoustic data is capable of providing vital information on progression of damage on different types of concrete.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.425-433
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• 2001

A correlation between fatigue damage and acousto-ultrasonic (AU) parameters has been obtained from signals acquired during fatigue loading of the single-lap joints of a carbon-fiber reinforced plastic (CFRP) laminates and A16061 plate. The correlation showed an analogy to those representing the stiffness reduction $(E/E_0)$ of polymer matrix composites by the accumulation of fatigue damage. This has been attributed to the transmission characteristics of acoustic wave energy through bonded joints with delamination-type defects and their influence on the change of spectral content of AU signals. Another correlation between fatigue cycles and the spectral magnitude of acoustic emission (AE) signals has also been found during the final stage of fatigue loading. Both AU and AE can be applied almost in real-time to monitor the evolution of damage during fatigue loading.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1131-1136
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• 2011

This study is performed on the acoustic emission parameters involved in a burst test for a CNG-vehicle fuel tank. A resonant AE sensor with a central frequency of 150 kHz was attached on the composite materials in the center of the fuel tank. The analysis of AE parameters such as hit, amplitude, count, duration, risetime, and signal strength during load holding was performed. The total count and total signal strength are effective tools for the damage evaluation of a CNG fuel tank.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.532-538
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• 2002

This study was performed to classify the fatigue crack opening and closure for three kinds of aluminum alloy using principal component analysis (PCA). Fatigue cycle loading test was conducted to acquire AE signals which come from different source mechanisms such as crack opening and closure, rubbing, fretting etc. To extract the significant feature from AE signal, correlation analysis was performed. Over 94% of the variance of AE parameters could accounted for the first two principal components. The results of the PCA on AE parameters showed that the first principal component was associated with the size of AE signals and the second principal component was associated with the shape of AE signals. An artificial neural network (ANN) an analysis was successfully used to classify AE signals into six classes. The ANN classifier based on PCA appeared to be a promising tool to classify AE signals for fatigue crack opening and closure.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.297-306
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• 2022

The acoustic emission (AE) is proposed as a feasible method for the real-time monitoring of the structural damage evolution in concrete materials that are typically used in the storage of nuclear wastes. However, the characteristics of AE signals emitted from concrete structures subjected to various environmental conditions are poorly identified. Therefore, this study examines the AE characteristics of the concrete structures during uniaxial compression, where the storage temperature and immersion conditions of the concrete specimens varied from 15℃ to 75℃ and from completely dry to water-immersion, respectively. Compared with the dry specimens, the water-immersed specimens exhibited significantly reduced uniaxial compressive strengths by approximately 26%, total AE energy by approximately 90%, and max RA value by approximately 70%. As the treatment temperature increased, the strength and AE parameters, such as AE count, AE energy, and RA value, of the dry specimens increased; however, the temperature effect was only minimal for the immersed specimens. This study suggests that the AE technique can capture the mechanical damage evolution of concrete materials, but their AE characteristics can vary with respect to the storage conditions.

• Composites Research
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• v.15 no.5
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• pp.1-6
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• 2002

When compared to other composite materials such as FRP and MMC, hybrid composite material is more attractive one due to the high specific strength and the resistance to fatigue. However, the fracture mechanism of hybrid composite material is extremely complicated because of the bonding structure of metals and FRP. Recently, nondestructive technique has been used to evaluate the fracture mechanism of these composite materials. In this study. AE technique has been used to clarify the fracture mechanism and the degree of damage for Al 7075/CFRP hybrid composite material. It was found that AE event, energy and amplitude among AE parameters were effective to evaluate fracture process of Al 7075/CFRP composite material. In addition, the relationship between the AE signal and the characteristics of failure surface using optical microscope was discussed.

• Structural Engineering and Mechanics
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• v.77 no.5
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• pp.637-650
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• 2021

The paper presents an investigation of the widely varying mechanical performance and behaviour of steel and Glass Fibre Reinforced Polymer (GFRP) reinforced concrete beams using non-destructive techniques of Acoustic Emission (AE) and Digital Image Correlation (DIC) under four-point bending. Laboratory experiments are performed on both differently reinforced concrete beams with 0.33%, 0.52% and 1.11% of tension reinforcement against balanced section. The results show that the ultimate load-carrying capacity increases with an increase in tensile reinforcement in both cases. In addition to that, AE waveform parameters of amplitude and number of AE hits successfully correlates and picks up the divergent mechanism of cracking initiation and progression of failure in steel reinforced and GFRP reinforced concrete beams. AE activity is about 20-30% more in GFRP-RC beams as compared to steel-RC beams. It was primarily due to the lower modulus of elasticity of GFRP bars leading to much larger ductility and deflections as compared to steel-RC beams. Furthermore, AE XY event plots and longitudinal strain profiles using DIC gives an online and real-time visual display of progressive AE activity and strains respectively to efficaciously depict the crack evolution and their advancement in steel-RC and GFRP-RC beams which show a close matching with the micro-and macro-cracks visually observed in the actual beams at various stages of loading.