• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.51-58
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• 2010

Recently, aerospace structures have lightweight trend in order to reduce the cost of fuel and system, Carbon Fiber Reinforced Plastic (CFRP) can give the ability to reduce weight at 20~50% as the substitution of metal alloy, and there are advantages such as high Non-rigid, specific strength and anti-corrosion, but it is difficult to prove its destruction properties due to heterogeneous structure and anisotropy. In this study we designed specimen, inducing distinguishing destructions of material (for example, matrix crack, fiber breakage, and delamination) by using the Carbon Fiber Reinforced Plastic (CFRP) which is used in a real aircraft, to apply acoustic emission technique to aerospace structures. And we gained data via tensile testing and acoustic emission technique, from which each fault signal was classified respectively by using AE parameters and waveform.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.13-25
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• 2020

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.27-33
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• 2009

A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.11a
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• pp.79-83
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• 2000

Interfacial and microfailure properties of carbon liber/bismaleimide (BMI) composites were evaluated using both tensile fragmentation and compressive Broutman tests with acoustic emission (AE). Since BMI is rather difficult matrix to apply for the conventional fragmentation test because of its too low elongation and too brittle and high modulus properties, dual matrix composite system was applied. After carbon fiber/BMI composite was prepared for rod shape by controlling differing curing stage, composites rod was embedded in toughened epoxy as outer matrix. The typical microfailure modes including fiber break, matrix cracking, and interlayer failure were observed during tensile testing, whereas the diagonal slippage in fiber ends was observed during compressive test. On the other hand, AE amplitudes of BMI matrix fracture were higher than carbon fiber tincture under tensile test because BMI matrix has very brittle and high modulus. The waveform of signals coming from BMI matrix fractures was consistent with AE amplitude result under tensile tests.

• Smart Structures and Systems
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• v.16 no.1
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• pp.67-79
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• 2015

Acoustic emission analysis is an effective technique for monitoring the evolution of damage in a structure. An experimental analysis on a set of reinforced concrete beams under flexural loading was carried out. A mixed AE analysis method which used both parameter-based and signal-based techniques was presented to characterize and identify different failure mechanisms of damage, where the signal-based analysis was performed by using the Hilbert-Huang transform. The maximum instantaneous energy of typical damage events and the corresponding frequency characteristics were established, which provided a quantitative assessment of reinforced concrete beam using AE technique. In the bending tests, a "pitch-catch" system was mounted on a steel bar to assess bonding state of the steel bar in concrete. To better understand the AE behavior of bond-slip damage between steel bar and concrete, a special bond-slip test called pullout test was also performed. The results provided the basis of quantitative AE to identify both failure mechanisms and level of damages of civil engineering structures.

• Journal of the Korean Society of Safety
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• v.16 no.2
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• pp.22-26
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• 2001

The machining process on be considered as a planned interaction of the workpiece, the tool and the machine tool. In an unmanned situation, the results of this interaction are to be continuously monitored so that any changes in the machining environment on be sensed to corrective actions. In order to design the process monitoring system for unmanned manufacturing, the identification of chip formation is proposed. The system proposes the method of using acoustic emission(AE) signal analysis to identify the chip formation during cutting.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.1
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• pp.65-72
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• 1990

Comparison of acoustic emission, ultrasonic testing and crack gauge in 3-point bending testing have been studied. As the results, COD is indirectly assumed by strain gauge rate and grid pitch width when crack gauge grid is out. Acoustic emission is qualitatively able to measure crack growth by total count but ultrasonic testing has a difficulty in measuring it because of echo height fluctuation according to the change and pressure of UT. probe.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.4
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• pp.274-286
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• 2005

Fracture behaviors and acoustic emission (AE) characteristics of single-edge-notched monolithic aluminum plates and glass fiber/aluminum hybrid laminate plates have been investigated under tensile loads. AE signals from monolithic aluminum could be classified into two different types: signals with low frequency band and high frequency band. High frequency signals were detected in the post stage of loading beyond displacement of 0.45mm. For glass fiber/aluminum laminates, AE signals with high amplitude and long duration were additionally confirmed on FFT frequency analysis, which corresponded to macro-crack propagation and/or delamination between A1 and fiber layers. On the basis of the above AE analysis and fracture observation with optical microscopy and ultrasonic T scan, characteristic features of AE associated with fracture processes of single-edge-notched glass fiber/aluminum laminates were elucidated according to different fiber ply orientations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.8
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• pp.863-867
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• 2014

In this study, an acoustic emission (AE) sensor was used for measuring the abrasive and molecular-scale phenomena in chemical mechanical polishing (CMP). An AE sensor is a transducer that converts a mechanical wave into an electrical signal, and is capable of acquiring high-level frequencies from materials. Therefore, an AE sensor was installed in the CMP equipment and the signals were measured simultaneously during the polishing process. In this study, an AE monitoring system was developed for investigating the sensitivity of the AE signal to (a) the variations in the material properties of the pad, slurry, and wafer and (b) the change in conditions during the CMP process. This system was adapted to Oxide and Cu CMP processes. AE signal parameters including AE raw frequency, FFT, and amplitude were analyzed for understanding the abrasive and molecular-level phenomena in the CMP process. Finally, we verified that AE sensors with different bandwidths could function in complementary ways during CMP process monitoring.

• Composites Research
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• v.16 no.1
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• pp.26-33
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• 2003

Utilizing a thermo-acoustic emission (AE) technique, a study on detection and evaluation of microfractures in cross-ply laminate composites was performed. Fiber breakages and matrix fractures formed by a cryogenic cooling at $-191^{\circ}C$ were observed with ultrasonic C-scan, optical and scanning electron microscopy. Those microfractures were monitored in a non-destructive in-situ state as three different types of thermo-AE signals classified on the basis of Fast-Fourier Transform and Short-Time Fourier Transform. Thus, it was concluded that real-time estimation of microfracture processes being formed during cryogenic cooling could be accomplished by monitoring such different types of thermo-AEs in each time-stage and then by analyzing thermo-AE behaviors for the respective AE types on the basis of the AE signal analysis results obtained during thermal heating and cooling load cycles.