• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.252-259
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• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1993.12a
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• pp.1-31
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• 1993

General state of research in fundamentals and applied areas of tribology is analysed with taldng account of changes in structure of funding and other aspects of transfer from centmllzed state economy to market one under simultaneous effect of USSR disintegration to independent states. Belarus Tribology Society as a coordinating body is presented. Activities of the major centers of research in friction, wear and lubrication are shown. Recent advances in development of analytical and calculation methods, surface characterization, wear testing and standardization, condition monitoring and tribomaterials are considered.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.817-823
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• 2020

In recent, the machining of difficult-to-cut materials such as titanium alloys, stainless steel, Inconel, ceramic, glass, and carbon fiber reinforced plastics (CFRP) used in aerospace, automobile, medical industry is actively researched. Abrasive waterjet is a non-traditional processing method in which ultra-high pressure water and abrasive particles are mixed in a mixing chamber and shoot out jet through a nozzle, and removed by erosion due to collision with a material. In particular, the nozzle of the abrasive waterjet is one of the most important parts that affect the machining quality as with a cutting tool in general machining. It is very important to monitor the condition of the nozzle because the workpiece is uncut or the surface quality deteriorates due to wear, expanding of the bore, damage of the nozzle and clogging of the abrasive, etc. Therefore, in this paper, we propose a monitoring system based on Acoustic Emission(AE) sensor that can detect nozzle condition in real time during AWJ processing.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.6
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• pp.829-834
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• 2011

This paper introduces a new system which measures abrasion quantity of cylinder liners of large-scale marine diesel engines. The proposed system consists of three parts; measurement part where an LVDT, a temperature sensor, a camera and LED for lighting control are installed, monitoring part which accomplishes measurement command transmission and monitoring based on PC or notebook, and master control part which controls the measurement part and transfers measurement data to the monitoring part. The accuracy of the developed system is compared with that of an internal micrometer with 1/100 mm accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.64-71
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• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• Transactions of Materials Processing
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• v.18 no.2
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• pp.144-149
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• 2009

Recently, the monitoring technology of machining process is very important to improve productivity and quality in manufacturing filed. Such monitoring technology has been performed to measurement using vibration signal, acoustic emission signal and tool dynamometer. However, micro machining is limited small-scale parts machining because micro tool is very small and weakness to generate signal in micro machining process. Therefore, this study has efficient sensing technology for real monitoring system in micro machine that is proposed to supplement a disadvantage of single-sensor by multi sensor. From experimental result, it was evaluated tool wear and cutting situation according to repetitive slot cutting condition and changing cutting condition, and it was performed monitoring spindle rpm and condition according to compare acceleration signal with current signal.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1225-1235
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• 2018

In this study, as a part of practical and customized smart contents development planning research related to smart fitness contents associated with smart wear that can monitor physical activity, we investigated the potential needs for smart fitness contents through research. As a result, the potential needs for smart fitness contents is 'accessibility to use', 'inducement of interest', 'diverse story line' were derived at the stage of 'before exercise', 'Real - time voice coaching', 'accurate exercise posture monitoring', and 'personalized exercise prescription' were derived at the stage of 'during exercise'. At the stage of 'after exercise', 'substantial reward system', 'grading system', 'body figure change monitoring' and 'everyday life monitoring' were derived. At the stage of 'connection to the next exercise', 'triggering exercise motivation', 'high sustainability' wear derived.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.33-43
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• 1992

Items are assumed to fail by degradation. An appropriate stochastic model of such item is a cumulative process in which an item can fail only when the total amount of wear exceeds a prespecified failure level. This paper presents replacement policy in which an item is replaced at a certain level of wear before failure or at failure, whichever occurs first. Yet, when measuring the item wear level is very expensive, destructive or time-consuming, it may be economical to use substitutive characteristics that are correlated with the item wear level and relatively inexpensive to measure. The item's wear level could usually be estimated by monitoring such substitutive characteristics only except for a breakdown, which may be observed immediately at its occurrence. The purpose of this paper is to find an optimal periodic replacement policy based on such substitutive characteristics that balance the cost of replacement with the cost of failure and result in a minimum total long-run average cost per unit time. The optimal level of substitutive characteristics to replace the item is obtained. Numerical example illustrate how the model can be used to determine the optimal replacement policy.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1570-1575
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• 1991

The feasibility of using an adaptive resonance network (ART2) with unsupervised learning capability for too] wear detection in turning operations is investigated. Specifically, acoustic emission (AE) and cutting force signals were measured during machining, the multichannel AR coefficients of the two signals were calculated and then presented to the network to make a decision on tool wear. If the presented features are significantly different from previously learned patterns associated with a fresh tool, the network will recognize the difference and form a new category m worn tool. The experimental results show that tool wear can be effectively detected with or without minimum prior training using the self-organization property of the ART2 network.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.64-71
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• 1998

This paper investigates the relationship between cutting conditions and Acoustic Emission(AE) signals; $AE_{avg}$, $AE_{rms}$, $AE_{mode}$$AE_{avg}$ and $AE_{rms}$ are increased as the increasing of cutting velocity and depth of cut respectively. The new parameters, derived from $AE_{avg}$ and $AE_{rms}$, which may be used for the in-process detection of tool wear is discussed. It is also known that $AE_{mode}$