• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.3
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• pp.394-400
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• 2012

Due to the increasing demand for carious methodologies, the quality improvement of products were introduced. A brush, the most frequently used type of grinding process, is one of the deburring. In order to produce consistent burr shape, various machining conditions have been combined and applied to disk grinding process. By tool dynamometer, acoustic emission sensor and acceleration sensor depend on changes in processing conditions(depth of engagement, cutting speed, workpiece position, workpiece orientation, cutting time) signals were obtained for brush deburring grinding system. Root mean square obtained by processing the signal processing conditions by analyzing the characteristics of deburring is to derive the optimum conditions.

• Proceedings of the KIEE Conference
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• 1999.07e
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• pp.2335-2337
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• 1999

In this paper, using AE (Acoustic Emission) sensor the electrical and emitted-sound signals are measured by treeing breakdown in the epoxy resin, and the corresponding frequency spectrum of the AE signals are analyzed. We also examined the relationship between partial discharge magnitude and pulse number of AE signals to diagnose the deterioration of the electrical insulation due to treeing breakdown. From these results, a frequency band of AE signals through treeing breakdown was set to about 230 [kHz], and it appeared that pulse number of AE signal was proportional to partial discharge magnitude.

• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.1
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• pp.17-26
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• 1998

The specimen with the wear between a roller and a vane and a normal specimen are classified by AE signal pattern recognition method with a neural network classifier in airconditioning operation test. Also the specimen with the scoring between a shaft and a bearing and a normal specimen are classified by the same method. As the internal pressure increases, the wear between the roller and the vane increases. The different pairs of oils and refrigerants five the effect on the wear.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.891-894
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• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• 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 of Precision Engineering Conference
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• 1996.04a
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• pp.526-530
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• 1996

In this paper, three different types of commercially tools-P20, NC123K and ceramic-have been used to working austempered ductile iron(ADI). In the austempered condition the materials are hard, strong and difficult to machine. Thus, we selected a optimum tool material among three different types of used tools in machining of austempered ductile iron. It was used acoustic emission(AE) to know cutting characteristic for selected tool and flank wear land of the ceramic too. The obtained results are as follows; (1) The ceramic tool among three different types of tools is the best in machining austempered ductile iron. (2) In case of ceramic tool, the amplitude level of AE signal(AErms) is mainly affected bycutting speed in cutting speed in cutting condition and it is proportioned to cutting speed. (3) There have the relationship of direct proportion between the amplitude level of AE signal and flank wear land of the tool. (4) If it find the value of AErms at each cutting speed, the in-process detection to ceramic tool's wear is possible

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.3
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• pp.211-216
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• 2016

Al alloys are useful materials having high specific strength and are used in machining of parts having thin-walled structures for weight reduction in aircraft, automobiles, and portable devices. In machining of thin-walled structures, it is difficult to maintain dimensional accuracy because machining deformation occurs because of cutting forces and heat in the cutting zone. Thus, cutting conditions and methods need to be investigated and cutting signals need to be analyzed to diagnose and minimize machining deformation and thereby enhance machining quality. In this study, an investigation on cutting conditions to minimize machining deformation and an analysis on characteristics of cutting signals when machining deformation occurs are conducted. Cutting signals for the process are acquired by using an accelerometer and acoustic emission (AE) sensor. Signal characteristics according to the cutting conditions and the relation between machining deformation and cutting signals are analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.4 s.97
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• pp.148-154
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• 1999

This study was carried out to verify grinding performance of dental diamond bur and investigate the possibility of AE application in dentistry field. Workpieces were made of acryl and bovine respectively for the experiments in this study. Grinding test was conducted to get the data of grinding resistance and specific grinding energy of four different types of diamond bur by using tool dynamometer. AE signal was acquired to verify grinding process in the AE measuring system. Tool wear was observed to find parameters about grinding characteristics of diamond bur by means of SEM picture. It was found that the wear of dental diamond bur could be detected with polishing of grinding material, removal of adhesive parts, wear of particles neighboring cutting nose, loss of material and elevation of temperature. The wear of B, C, D type diamond bur is due to wear and fracture of grain size. Abnormal state can be found through the behavior of AE signal in the grinding working. As a result, it is expected that forecast of abnormal state is possible using AE equipments under real time process.

• Korean Journal of Materials Research
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• v.3 no.4
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• pp.361-371
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• 1993

Among the various test methods for stress corrusiun cracking(SCC) susceptibility evaluatiun, the slow stram rate test(SSHT) method is a rapid and effective nwthod to evaluate the SCC susceptibility of metal in relatively short time. But it is very difficult to analyze the microfracture behaviors in SCC process by using the test(SSRT) method only. Up to now, it has been well known that the acoustic emission(AE) test is the effective technique to monitor the microcrack initiation and propagation in material fracture pmcess. Therefore. in this paper, we analyzed the correlation between the see process and the characteristics of AE signal by using the SSHT and the AE test. According to the test results. the AE signals produced from the material microfracture were clearly depended on the test environment. The AE signal characteristics generated during see process in synthetic sea water were comparatively greater than those. in air. In addition, the SCC behaviors could be definitely evaluated by the amplitude parameter of AE signals.