• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2003년도 학술대회지
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• pp.224-228
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• 2003

The basic Principles and methods of the on-line monitoring of tribology parameters (friction coefficient and wear allowance) and fault diagnosis for the hoist disc brake system were introduced, the method were based on the spring force and oil pressure of the brake system and the hoist kinematics parameters. The experiment on the monitoring and diagnosis of hoist brake system were carried out. The research results showed: the monitoring and diagnosis methods are feasible.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• 한국생산제조학회지
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• 제6권2호
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• pp.112-118
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• 1997

Characteristics of AE(Acoustic Emission) signal is related to cutting conditions, tool materials, and tool geometry in metal cutting. Relation between AE signal and tool wear was investigated experimentally. Experiment is carried out by interrupted cutting for SCM420 workpiece with TiN coating tool on HSS material. AE RMS voltage and count per event were increased according to tool wear. The major results are as follows : 1) AE RMS value is nearly constant as cutting speed changes, but is rapidly increase as feed rate increases. 2) AE RMS value and Count per Event increase as tool wear increases. 3) It is more effective to monitor tool wear by Incremental rate of AE RMS value than by Incremental rate of count per event.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.26-33
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• 1999

Automatic monitoring of cutting process is one of the most important technologies for increasing the stability and the reliability of unmanned manufacturing system. In this study, basic methods which use the acoustic emission (AE) signals and cutting forces were proposed to monitor flank wear (width of flank wear) quantiatively. First, in order to detect flank wear, it was investigated that the influence of cutting conditions, that is, cutting velocity, feed and depth of cut, on AE signals (${AE_rms}$) and cutting forces. Furthermore, the relation between flank wear and the measured signals (${AE_rms}$, cutting force) was discussed.

• 한국정밀공학회지
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• 제20권1호
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• pp.222-228
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• 2003

A reliable tool wear monitoring technique is the one of important aspects for achieving an integrated and self-adjusting manufacturing system. In this paper, a tool wear estimation approach for turning is proposed. This approach uses the model of cutting force, spindle displacement and their relation. A series of experiments were conducted by designing experimental techniques to determine the relationship between flank wear and cutting force coefficient as well as cutting parameters such as cutting speed, depth of cut and feed. The proposed model performance has shown that the spindle displacement model predicts tool wear with high accuracy and spindle displacement signal is possible to replace cutting force signal.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.95-100
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• 1995

Automatic monitoring of cutting process is one of the most important technology for increasing the stability and the reliability of unmanned manufacturing system. In this study, basic methods which use the acoustic emission (AE) signals and sutting forces proposed to monitor tool wear (flank wear) quantitatively. Fist, in order to detect flank wear, it was investigated influence of cutting conditions, that is, cutting velocity, feed and depth of cut, on AE signals (AErems) and cutting forces. Furthermore,the relationship flank wear between AErems and cutting forces were discussed.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.459-464
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• 2001

A reliable tool wear monitoring technique is the one of important aspects for achieving an integrated and self-adjusting manufacturing system. In this paper, a tool wear estimation approach for turning is proposed. This approach uses the model of cutting force, spindle displacement and their relation. A series of experiments were conducted by designing experimental techniques to determine the relationship between flank wear and cutting force coefficient as well as cutting parameters such as cutting speed, depth of cut and feed. The proposed model performance has shown that the spindle displacement model predicts tool wear with high accuracy and spindle displacement signal is possible to replace cutting force signal.

• Structural Engineering and Mechanics
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• 제31권1호
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.805-810
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• 1997

In this paper the technique to predict tool were theoretically in the sheet metal shearing process is suggested. The were in sheet metal tool affects the tolerances of final parts, metal flows and costs of processes. In order to predict the tool were the deformation of workpiece during the process is analyzed by using non-isothermal finite element program. The ductile fracture criterion and the element kill method are also used to estimate if and where a fracture will occur and to investigate the features of the sheared surface in shearing process. Results obtained form finite element simulation such as node velocities and node forces are transformed into sliding velocity and normal pressure on tool monitoring points respectively. The monitoring points are automatically generated and the were rates on these points are accumulated during a process. It is assumed that the wear depth on the tool surface are linear function of the lot sizes based upon the known experimental results. The influence of clearance between die and punch upon tool wear is were is also discussed during the process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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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.