• Transactions of Materials Processing
• /
• v.29 no.1
• /
• pp.20-26
• /
• 2020

In the design of the metal forming processes, various types of ductile fracture criteria are used to predict crack initiation and to fabricate metallic products without any defects. However, the quantitative measurement method for determination of crack initiation is insufficient. It is very difficult to detect crack initiation in ductile metals with excellent deformability because no significant load drop is observed due to crack generation. In this study, the applicability of acoustic emission sensors, which are commonly used in facility diagnostics, to measure crack initiation during the metal forming process was analyzed. Cylindrical notch specimens were designed using the finite element method to induce a premature crack on the surface of pre heat-treated steel (ESW90) material. In addition, specimens with various notch angles and heights were prepared and compression tests were carried out. During the compression tests, acoustic emission signal on the dies and images of the surface of the notch specimen were recorded using an optical camera in real time. The experimental results revealed that the acoustic emission sensor can be used to detect crack initiation in ductile metals due to severe plastic deformation.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.07a
• /
• pp.585-591
• /
• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.6 no.4
• /
• pp.124-129
• /
• 1997

The experimental investigation is mainly focused to detect tool wear by cutting signal measurements in multi-insert face milling SS 41 and STS 304. This research have investigated the effects on the insert number, which has relationship with mean-cutting force. AE(acoustic emission) signal, tool life and surface roughness in machining SS41 and STS 304. The cutting force and AE signal are monitored to analyse the cutting process, The surface roughness of the specimens machined by TiN coated tool with the various insert numbers measured at various cutting speeds, feed rates and depths of cut, The width of flank wear is also observed.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.7 s.94
• /
• pp.1783-1793
• /
• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2011.04a
• /
• pp.582-587
• /
• 2011

Acoustic Emission(AE) is widely used for early detection of faults for rotating machinery in these days because of its high sensitivity. AE signal has to need for transferring to low frequency range for the spectrum analysis included the fault mechanism. In transferring process, we lose a lot of fault information caused by unusable signal processing method. Discrete Wavelet Transform(DWT) is a method of signal processing for AE signatures, but the pattern of its mother function is not optimized with AE signals. So, we can lose the fault information when we want to use the DWT for AE signal. Therefore, in this paper, we will propose a novel pattern for DWT mother function, which is optimized with AE signals. And it will be applied to compare the results of DWT by daubechie and novel pattern.

• KSTLE International Journal
• /
• v.1 no.1
• /
• pp.29-33
• /
• 2000

In this study, a methodology is developed and confirmed to find the physical contact between the slider and disc due to the defects of disk during head seeking operation using acoustic emission (AE) signal. The head/disk contact was detected during random and standard seeks, whereas no contact was detected during track fellowing. During standard and random seeks, the torsion mode of slider excitation was observed at 680KHz. Therefore, it is thought that AE technique can be used as an alternative method of the glide test by monitoring existence of the torsional mode of the slider during seek operation or can be used to detect the spacing loss during seeking operation. By appropriately choosing the location of the sensor an order of magnitude increase in the sensitivity for RMS AE signal is observed. Therefore we can find take-off velocity clearly with high signal to noise ratio of AE signal.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.20 no.3
• /
• pp.304-309
• /
• 2011

A monitoring system for magnetic abrasive polishing process is necessary to ensure the polishing products the high quality and integrity. Acoustic emission (AE) signal is known to reflect the material removal phenomena in other machining processes. In a case of the magnetic abrasive polishing of non-magnetic materials, application of AE method is very difficult because of lower machining force on the surface of workpiece and the level of AE signal is extremely lower. In this study, AE sensor-based monitoring system is applied to the magnetic abrasive polishing. The relation between the level of the AE RMS and the surface roughness during the magnetic abrasive polishing of magnesium alloy steel is investigated.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.32 no.3
• /
• pp.30-36
• /
• 2004

As fiber reinforced composite materials are widely used in aircraft, space structures and robot arms, the study on the non-destructive testing methods of the composite materials has become an important research area for improving their reliability and safety. In this paper, the AE signal analyzer with the resonance circuit to extract the specified frequency of the acoustic emission signal were designed and fabricated. The noise levels of the fabricated AE signal analyzer by the disturbance such as impact or mechanical vibration had a very small value comparable to those of the conventional AE signal analyzer. Also, the fabricated AE signal analyzer was proved to have about the same crack detection capabilities with the conventional AE signal analyzer under the static and dynamic tensile tests of the composite materials.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.23 no.1
• /
• pp.53-60
• /
• 2010

This paper dealt with the acoustic detection of partial discharge (PD) in insulation oil for insulation diagnostics of oil immersed transformers. Electrode systems such as needle to plane, plane to plane, and floating were fabricated to simulate some defects in transformers. A wide band acoustic emission(AE) sensor with the frequency ranges of 100 kHz~1 MHz and a narrow band AE sensor with the resonant frequency of 140 kHz were used in the experiment. Also, a decoupler and an amplifier were designed to detect and amplify the acoustic signal only. The decoupler separates acoustic signal from DC source without any distortion, and the amplifier has the gain of 40 dB in frequency ranges of 11 kHz~4 MHz. In the experiment, frequency components and propagation characteristics of acoustic signal were analyzed, and an algorithm of positioning of PD occurrence by the time difference of arrival was proposed. From the results, the frequency components of the acoustic signal exist from 50 kHz to 200 kHz and the positioning error of PD calculated by three AE sensors was within 1%.

• Composites Research
• /
• v.35 no.2
• /
• pp.61-68
• /
• 2022

Composite materials have been used in aerospace industry and many applications because of many advantages such as specific strength and stiffness and corrosion resistance etc. However, it is vulnerable to impacts, these impact lead to formation of cracks in composite laminate and failure of structures. In this paper, we analyzed Mode I fracture toughness of Carbon/Epoxy laminates using acoustic emission signal. DCB test was carried out to analyze Mode I failure characterization of Carbon/Epoxy laminates, and AE sensor was attached to measure AE signal induced by failure of specimen. Fracture toughness was calculated using cumulative AE energy and measured crack length using camera. The calculated fracture toughness was applied in FE model and the result of FE analysis compared with DCB test results. The results show good agreement with between FEM and DCB test results.