• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.1
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• pp.33-39
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• 2012

The tool wear and failure in automatic production system directly influences the quality and productivity of a product, thus it is essential to monitor the tool state in real time. For such purpose, an ART2-based remote monitoring system has been developed to predict the appropriate tool change time in accordance with the tool wear, and this study aims to experimently find the relationship between the tool wear and the monitoring signals in cutting processes. Also, the roughness of workpiece according to the wool wear is examined. Here, the tool wear is indirectly monitored by signals from a vibration senor attached to a machining center. and the wear dimension is measured by a microscope at the start, midways and the end of a cutting process. A series of experiments are carried out with various feedrates and spindle speeds, and the results show that the sensor signal properly represents the degree of wear of a tool being used, and the roughnesses measured has direct relation with the tool wear dimension. Thus, it is concluded that the monitoring signals from the vibration sensor can be used as a useful measure for the tool wear monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.94-100
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• 2000

This paper presents a methodology for In-Process monitoring of tool wear by using ultrasonic sensor in turning operation. An integrated single ultrasonic transducer operation at a frequency of 10MHz is placed in contact with the insert tip. The change in amount of the reflected energy from the nose and flank of the tool can be related to the level of tool wear and the mechanical integrity of the tool. As the results, the tool wear monitoring system based on the ultrasonic pulse-echo method was proposed, it is useful to determine a tool life and tool change time.

• Transactions of Materials Processing
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• v.31 no.2
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• pp.103-108
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• 2022

Systematic quality control based on real time data is required for modern factories. This study introduced a method of predicting punch wear in the trimming process of automobile parts. Based on monitoring data of the mass production process using a bolt-type piezo sensor, it was shown that precursor symptoms of die wear could be predicted from the change in load pattern with respect to production volume. The load pattern that changed according to the wear of the die was verified by numerical analysis.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.172-177
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• 1998

An experimental study on the prediction of grinding wheel wear by dual detection methods was conducted by the laser displacement and acoustic emission(AE) system. The laser displacement sensor was located above the head of the grinding wheel and the AE sensor was set under the workpiece, where the wheel were condition can be detected. It was found that the dual detection methods by laser displacement system and AE system made it possible to predict the wheel wear. From the experiments, the root mean square(RMS) values both methods was found to be proportional to the grinding wheel wear.

• Journal of Fashion Business
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• v.22 no.5
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• pp.125-136
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• 2018

This study intended to develop a motorcycle safe vest that can be prepared against accidents by mounting a smart module (with built-in sensor) on the safe vest in order to emphasize safety among functional aspects of the motorcycle clothing. The research method investigated professional books, prior research, and Internet data to examine the characteristics of motorcycle wear and the theoretical examination of smart wear, and analyzed the functional characteristics of the design by reviewing smart jacket and vest design cases for motorcycles currently on the market. As a results of study an interface device sensor, which contains a sensor with IMU(Intertial Measurement Unit) and CPU(Central Processing Unit), was inserted into a motorcycle top in order to draw attention to the safety of motorcycle riders. The IMU sensor attached to the vest detected the tilting motion of the rider to either left or right side to obtain data on left or right direction, sudden stop, and so forth and displayed left or right turn signal and sudden stop sign on the backplate (back) through the LED module. As for charging the device to operate LEDs, a generator, which is designed to convert the heat energy in the exhaust into electric energy, was used to efficiently self-produce the power required to operate LEDs of the top while riding.

• Tribology and Lubricants
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• v.15 no.4
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• pp.328-334
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• 1999

Acoustic emission (AE) sensor was used to evaluate the wear-life of CrN-coated steel disks with 1 $\mu\textrm{m}$ and 4 $\mu\textrm{m}$ coating thickness. The relationship between Af and friction signal from scratch test and sliding test was investigated. The first spatting of CrN film was detected by AR signals in the early stage of coating failures, and overall failures by friction signals. Therefore, the conservative design for coating-life should be done using the results of AE signals. Using the percent contact load, the ratio of sliding normal load to the critical scratch load and the number of cycles to failure was measured to predict the wear-life of CrN film. On the wear-life dia-gram the percent contact loads and the number of cycles to failure showed a good linear relationship on the log coordinate. As the load percentage was decreased, the diagram showed that the wear-limits, at which the coated steels survived more than 35,000 cycles, were about 4∼5% of the critical scratch loads.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.93-101
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• 1997

Ever since the nonlinearity of machine tool dynamics was established, researchers attempted to make use of this fact to devise better monitoring, diagnostics and control system, which were hitherto based on linear models. Theory of chaos which explains many nonlinear phenomena comes handy for furthering the analysis using nonlinear model. In this study, measuring system will be constructed using multi-sensor (Tool Dynamometer, Acoustic Emission) in end milling process. Then, it will be verified that cutting force is low-dimensional chaos by calculating Lyapunov exponents. Fractal dimension, embedding dimension. And it will be investigated that the relation between characteristic parameter calculated from sensor signal and tool wear.

• Design & Manufacturing
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• v.16 no.4
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• pp.46-51
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• 2022

The piercing process of creating holes in sheet metals for mechanical fastening generates high shear force. Real-time monitoring technology could predict tool damage and product defects due to this severe condition, but there are few applications for piercing high-strength aluminum. In this study, we analyzed the load signal to predict the punch's wear level during the process with a piezoelectric sensor installed piercing tool. Experiments were conducted on Al6061 T6 with a thickness of 3.0 mm using piercing punches whose edge angle was controlled by reflecting the wear level. The piercing load increases proportionally with the level of tool wear. For example, the maximum piercing load of the wear-shaped punch with the tip angle controlled at 6 degrees increased by 14% compared to the normal-shaped punch under the typical clearance of 6.7% of the aluminum piercing tool. In addition, the tool wear level increased compression during the down-stroke, which is caused by lateral force due to the decrease in the diameter of pierced holes. Our study showed the predictability of the wear level of punches through the recognition of changes in characteristic elements of the load signal during the piercing process.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.