• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.230-235
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• 1997

During laser welding, a laser induced matal vapour and plasuma is formed. The plasma shows strong fluctuation combined with acoustic sound emission. On-line monitoring of the process is possible by measuring and analysing the plasma and acoustic sound emission. This paper introduce the method of on line process monitoring in the laser beam welding and analysis being monitoring signal. The results show the complementary information on the process.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.3
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• pp.39-44
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• 2002

As laser cutting process is widely used in industry, an automated on-line process control system has become very important. In this paper, development of a laser cutting monitoring system, which is regarded as the fundamental step toward automation of the process, is presented. Acoustic emission and an artificial neural network were used for the monitoring system. With given process Parameters including laser power and scanning speed the system can predict the suitability of laser beam for the cutting or a stainless steel (STS304) plate.

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

• Computers and Concrete
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• v.20 no.5
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• pp.595-603
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• 2017

Piezoceramic transducers have been widely used in the health monitoring of civil structures. However, in most cases, they are used as sensors either to measure strain or receive stress waves. This paper proposes a method of using piezoelectric transducers as strain gauges and acoustic emission (AE) sensors simultaneously. The signals received by piezoceramic transducers are decomposed into different frequency components for various analysis purposes. The low-frequency signals are used to measure strain, whereas the high-frequency signals are used as acoustic emission signal associated with local damage. The b-value theory is used to process the AE signal in piezoceramic transducers. The proposed method was applied in the bending failure experiments of two reinforced concrete beams to verify its feasibility. The results showed that the extracted low-frequency signals from the piezoceramic transducers had good agreement with that from the strain gauge, and the processed high-frequency signal from piezoceramic transducers as AE could indicate the local damage to concrete. The experimental results verified the feasibly of structural health monitoring using piezoceramic transducers as strain gauges and AE sensors simultaneously, which can advance their application in civil engineering.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.387-390
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• 1997

Grinding process takes a long time that grinding machine is setted properly. It is difficult for user to judge correctly the abnormal states generated in grinding process. Air grinding has to be reduced for the improvement of productivity. In addition, it is important to monitor the dressing and the grinding process so that the grinding working maintains optimal grinding conditions. In this study, the monitoring system using the acoustic emission is developed to monitor these processes continuously. This system was able to reduce the preparation as well as the machine setting time in grinding operation.

• Structural Engineering and Mechanics
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• v.54 no.6
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• pp.1075-1095
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• 2015

The use of acoustic emission (AE) technique for detecting and monitoring damages and the progress on damages in different structures is widely used and has earned a reputation as one of the most reliable and well-established technique in non-destructive testing (NDT). Acoustic Emission is a very efficient and effective technology used for fracture behavior and fatigue detection in metals, fiberglass, wood, composites, ceramics, concrete and plastics. It can also be used for detecting faults and pressure leaks in vessels, tanks, pipes, as well as for monitoring the progression of corrosion in welding. This paper reviews major research developments over the past few years in application of acoustic emission in numerous engineering fields, including manufacturing, civil, aerospace and material engineering.

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.60-67
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• 2005

Compared with conventional welding, laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme fur a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.