• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• 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 Information Processing Systems
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• v.1 no.1 s.1
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• pp.1-8
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• 2005

Embedded systems are used in many fields such as home appliances, terminals, controls, communications, etc. So, to manage, control, and test these embedded systems, monitoring programs have been developed variously. In this paper, to overcome the characteristic faults of embedded systems which have resource restrictions, we implemented a development environment based on NFS and designed a monitoring tool that can evaluate and analyze kernel performance in embedded equipment by using LTT(Linux Trace Toolkit). Also, we designed a method to show monitoring data collected by using a monitoring tool, called MONETA 2.0, through the web-page.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.564-567
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• 2005

For the application of monitoring system of the machine tool to industry, the requirements such as high reliability and low cost need to be satisfied. In this study, a reliable but inexpensive monitoring method for machine tool is introduced. To improve the monitoring reliability, several kinds of information related to machining and operation are selected; real-time video clip from USB camera, operation data and signal from CNC and feed motor torque. Especially, to improve the quality of real-time video clip, a camera housing is developed, it can significantly reduce the vibration effect and prevent from coolant and chip. The collected information are transferred to the monitoring terminals in remote sites using OPC and TCP/IP protocol over Ethernet, which give us convenience of development and interoperability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.332-335
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• 1997

With the advance of precision technique, the optical system is more precise and complex and the machining method of optical element which is composed of micro-grooves is developed. Especially, the method of micro-grooving using diamond tool is used widely owing to many merit, but has problems of damage of surface roughness due to tool wear and tool fracture. This paper deals with state monitoring using AE RMS in the micro-grooving. The change of AE RMS is very small with increment of cutting velocity and depth of cut. In spite of constance magnitude of principal force in machining using diamond tool of tool wear and tool fracture, AE RMS is highly fluctuated. Because changing of cutting state has relevance to surface roughness profile, surface toughness profile is expected using AE RMS.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.189-198
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• 1998

Recently, monitoring of tool life is a matter of common interesting because tool life affects precision, productivity and cost in machining process. Especially flank wear has a direct effect on cutting mechanism, so the various pattern of cutting force is obtained experimentally according to variation of wear condition. By investigating cutting force signal, AR(Autoregressive) modeling and correlation dimension analysis is conducted in turning operation. In this modeling and analysis, we extract features through 6th AR model, correlation integral and normalized correlation integral. After the back-propagation model of the neural network is utilized to monitor tool life according to flank wear. As a result. a very reliable classification of tool life was obtained.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.82-87
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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 workstation to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and fracture 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.39-45
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• 2000

This paper describes the fundamental investigations on the in-process monitoring techniques focused on Acoustic Emission(AE) based on analytical method. Experiments were conducted on a CNC lathe using conventional carbide insert tools under various cutting conditions. As the result of this study a suggestion is given about the multi-purpose use of AE-signals detected with a single sensor for the monitoring of tool wear, built-up edge and chatter vibration in turning process.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.923-924
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.47-51
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• 1997

The high speed machining which can improve the production and quality in machining has been adopted remarkably in dietmold industry. As the speed of machine tool spindle increases, the machinability evaluation and monitoring of high speed machining is necessary. In this study, the machinability of 30, 000rpm class spindle was evaluated by using the developed tool dynamometer and the machining properties of high hardened and toughness materials in high speed were examined. Finally, the in-process monitoring technologies of tool wear were presented through the prediction by the experimental formula and pattern recognition by the neural network.