• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.311-315
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• 2000

In thls paper, a remote machine monitoring system for a vimal machine is proposed. The monltonng system is one of the core functmns of a vimd machne that provides a modeling and simulation environment for machining processes and management of the machine life cycle. The proposed system contains the modules for investigating tool wear using neural network and web-based real time process monitoring. An example implementation for tool wear and machining status monitoring is illustrated

• Tribology and Lubricants
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• v.14 no.2
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• pp.75-81
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• 1998

Condition monitoring technology was applied for an air compressor lubricating system to achieve a proactive maintenance, which could prevent a catastrophic failure and detect root causes of the conditional failure of the system. For this work, various types of wear monitoring technology were used and compared with the results of vibration and temperature measurements. The Results generally showed that every technology has a limitation to failure detection, and integrated-based condition monitoring should be performed for the best results. In this work, an idea for the implementing integrated wear monitoring was suggested and demonstrated.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.82-87
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• 2000

This paper deals with monitoring of grinding wheel wear in grinding process. A monitoring system is developed in which a laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to grinding machines. The experiment results show that the monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for measuring the apparent diameter of the grinding wheel.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.1
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• pp.81-86
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• 2001

This paper deals with the monitoring of grinding wheel wear in surface grinding process. A monitoring system, which makes use of a laser scanning micrometer, is developed to measure the circumferential shape as well as the axial profile of grinding wheel. The monitoring system is applied to surface grinding processes. The experimental results show that the developed monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for evaluation the quality of ground surface and determining proper derssing time for the grinding wheel.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.3
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• pp.36-43
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and breakage during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.613-616
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• 2000

This paper deals with monitoring of grinding wheel wear in surface grinding process. A laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to two kinds of grinding methods: plunge and traverse grinding. Through experiments, it is found that measurement of grinding wheel wear reveals information of roughness of ground surface and the adequate dressing time. In addition, monitoring of grinding wheel wear makes it possible to identify abnormal grinding conditions.

• 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.