• Composites Research
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• v.13 no.1
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• pp.40-49
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• 2000

The objective of this research is to develop the impact monitoring techniques providing impact identification and damage diagnostics of smart composite laminates susceptible to impacts. This can be implemented simultaneously by using the acoustic waves by the impact loads and the acoustic emission waves from damage. In the previous research, we have discussed the impact location detection process in which impact generated acoustic waves are detected by PZT using the improved neural network paradigm. This paper describes the implementation of time-frequency analysis such as the Short-Time Fourier Transform (STFT) and the Wavelet Transform (WT) on the determination of the occurrence and the estimation of damage.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.388-393
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• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1491-1498
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• 2000

In recent years, CFRP composite materials have been increasingly used in various fields of engineering because of a high specific strength and stiffness properties. Drilling is one of the most impo rtant cutting processes that are generally carried out on CFRP materials owing to the need for the structural integration. However, delamination are often occurred as one of the drilling damages. Therefore, there are needs studying for the relationships between CFRP drilling and delamination in order to avoid low strength of the structures and inaccuracies of the integration. In this study, AE signals and thrust forces were used for the evaluations of the delamination from a drilling process in [0/900]s CFRP materials. And the drilling damage processes were observed and measured by a real time monitoring technique with a video camera. From the results, we found that the relationships between the delamination from drilling and AE characteristics and drill thrust forces for [0/900]s CFRP composites. Also, we proposed the monitoring method for a visual analysis of drilling damages.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.395-399
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• 2007

Renewable energy sources such as wind energy is copiously available without any limitation. Wind turbines are used to tap the potential of wind energy which is available in millions of megawatt. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. We reviewed different techniques, methodologies, and algorithms developed to monitor the performance of wind turbine as well as for an early fault detection to keep away the wind turbines from catastrophic conditions due to sudden breakdowns. To keep the wind turbine in operation, implementation of Condition Monitoring System (CMS) is paramount, and for this purpose ample knowledge of these types of system is mandatory. So, an attempt has been made in this direction to review maximum approaches related to CMS in this piece of writing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.888-891
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• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.5
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• pp.384-390
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• 2016

It is important to have a pipeline leak-detection system that determines the presence of a leak and quickly identifies its location. Current leak detection methods use a acoustic emission sensors, microphone arrays, and camera images. Recently, many researchers have been focusing on using cameras for detecting leaks. The advantage of this method is that it can survey a wide area and monitor a pipeline over a long distance. However, conventional methods using camera monitoring are unable to target an exact leak location. In this paper, we propose a method of detecting leak locations using leak-detection results combined with multi-frame analysis. The proposed method is verified by experiment.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.1
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• pp.47-55
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• 2012

A structural health monitoring technique for locating impact position in a plate structure is presented in this paper. The method employs a single sensor and spatial focusing of time reversal (TR) acoustics. We first examine the TR focusing effect at the impact position and its surroundings through simulation and experiment. The imaging results of impact points show that the impact source location can be accurately estimated in any position of the plate. Compared to existing techniques for locating impact or acoustic emission source, the proposed method has the benefits of using a single sensor and not requiring material properties and geometry of structures. Furthermore, it does not depend on a particular mode of dispersive Lamb waves that is frequently used in other ultrasonic testings of plate-like structures.