• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.414-418
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• 1996

This paper aims at reviewing the possibility application over normal or abnormal, detection used by AE and the characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and AE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece: arcyl and cowteeth. According to the experiment results, the following can be expected; AE has the possibility to detect the state normality and abnormality. However, the grinding resistance measuring can find it difficult to detect it. It can be accurately excepted from AE occurrence pattern in contact start point of diamond but and cowteeth, grinding condition and derailment point. It is known that AErms is well compatible with grinding resistance.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.6
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• pp.71-77
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• 1999

Monitoring of Cutting tool wear is a critical issue in automated machining system and has been extensively studied for many years. An austempered ductile iron(ADI) exhibits the excellent mechanical properties and the wear resistance. ADI has generally the poor machinability due to the characteristic. This paper presents the in-process detection of flank wear of cutting tools using the acoustic emission sensor and the digital oscilloscope. The amplitude level of AE signal(AErms) is mainly affected by cutting speed and it is proportional to cutting speed. There have been the relationship of direct proportion between the amplitude level of AE signals and the flank wear width of cutting tool. The flank wear with corresponding to the tool life is successfully detected with the monitor-ing system used in this study.

• 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 for Precision Engineering
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• v.16 no.5 s.98
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• pp.186-193
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• 1999

Sensor feedback for process control is one of the essential elements is an automated deburring procedure. This paper presents the implementation of acoustic emission (AE), which has been developed as a feedback sensing technique for precision (mechanical) deburring, in a precision laser deburring process. AE signals were sampled for laser machining/deburring under various experimental conditions and analyzed using several signal-processing methods including AErms and spectral analysis. The results, such as the sensitivity of AE signals for different laser cutting depths, edge detection capability and the frequency analysis show a clear correlation between physical process parameters and the AE signals. A subsequent control strategy for deburring automation is also briefly discussed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.15-20
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• 1999

In this study, the effects of the maximum grit depth of cut on the grinding characteristics were investigated. And AE signals produced during grinding processes have been studied to find out the appropriate AE parameters for assessing grinding processes. S45C steel has been ground under the conditions yielding removal rate of workpiece, 100, 200, 300 and 400rnm$^3$/min which was achived by altering workpiece velocity($\upsilon$) and apparent depth of cut(Z). According to the experimental results, the value of surface roughness increases but grinding power, energy rate of AE signal(AErms$^2$) and specific grinding energy consumed decrease with increase of the maximum grit depth of cut.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.1011-1017
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• 1999

Electrolytic in-process dressing grinding technique which enables application of metal bond wheels with fine superabrasives in mirror surface grinding operations has developed. It is possible to make efficient precision machining of hard and brittle material such as ceramic and hard metal by the employment of this technique. However, in order to ensure the success of performances such as efficient machining, surface finish, and surface quality, it is important to sustain the insulating layer that has sharply exposed abrasives in wheel surface. Using AE(Acoustic Emission) sensor, this paper will show whether the insulating layer sustains stably or not in real grinding time. And by comparing AErms value and surface roughness their thresholds for stable electrolytic in-process dressing grinding will be determined.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.601-607
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• 2012

The magnetic abrasive polishing (MAP), for online monitoring with AE sensor attachment, was performed in this study. To predict the surface roughness after the magnetic abrasive polishing of NAK80, the signal data acquired from the AE sensor were analyzed. A dimensionless coefficient, which consisted of average of AErms and standard deviation of AE signal, was defined as a characteristic of the MAP and a prediction model was obtained using least square method. A neural network, which had multiple input parameters from AE signals and polishing conditions, was applied for predicting the surface roughness. As a result of this study, it was seen that there was very close correlation between the AE signal and the surface roughness in the MAP. And then on-line prediction of the surface roughness after the MAP of the NAK80 was possible by the developed prediction model.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.108-115
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• 1999

Since grinding is a more complicated process than any other machining process, it is hard for operators to setup a grinding machine properly and to find out correctly abnormal grinding states resulting in damages to products. Abnormalities would be caused by improper setup, improper dressing/grinding conditions which are likely to be occurred without skilled operators' attention. In this study, an AE monitoring unit is developed to help operators conduct with ease setup, and set properly dressing/grinding conditions. AErms(root-mean-square) signal being monitored, on-going process states during grinding and dressing is visualized for machine operators to judge whether the processes are in good condition. Evaluation tests are carried out on centerless grinding machines-both cylindrical and internal. The developed AE monitoring system is verified to be useful to check grinding/dressing states in process even in the centerless grinding of which process is most unknown among various grinding methods because of the complex structure.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.80-84
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• 1996

This paper aims at reviewing the Possibility application over normal or abnormal, detection used by AE and the wear characteristics of grinding process. In this study, when diamond bur in dentistry with chosen grinding conditions were tuned at grinding. The variation of grinding resistance and hE signal is detected by the use of AE measuring system. The tests are carried out in accordance with diamond burs and workpiece; arcyl and bovine. According to the experiment results, the following can be expected; AE has the possibility to detect the state normality and abnormality. However, the grinding resistance measuring can find it difficult to detect it. It can be accurately excerpted from AE occurrence pattern in contact start point of diamond bur and bovine, grinding condition and derailment point. It is known that AE$\_$rms/ is well compatible with grinding resistance. According to the increase of the material removal rate, the specific energy of the diamond bur is inclined to decrease and the grinding resistance has a tendency to increase.