• 한국음향학회:학술대회논문집
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• 한국음향학회 2002년도 하계학술발표대회 논문집 제21권 1호
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• pp.209-212
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• 2002

The wear process of end mill is a so complicated process that a more reliable technique is required for the monitoring and controling the tool life and its performance. This research presents a new tool wear monitoring method based on the sound signal generated on the machining. The experiment carried out continuous-side-milling for 4 cases using the high-speed steel slot drill under wet condition. The sound pressure was measured at 0.5m from the cutting zone by a dynamic microphone, and was analyzed at frequency domain. The tooth passing frequency appears as a harmonics form, and end mill wear is related with the first harmonic. It can be concluded from the result that the tool wear is correlate with the intensity of the measured sound at tooth passing frequency estimation of end mill wear using sound is possible through frequency analysis at tooth passing frequency under the given circumstances.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.454-459
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• 2000

The high hardened materials that are remarkable in aspects of durability have been used for die and mold industry. As the high hardened materials are hard to machine, the high-speed machining is essential to manufacture these materials. Currently, in the general turning and milling, experiments to the tool wear monitoring have studied, but those have not applied in high-speed machining. In this study, the cutting mechanism was analysed by the cutting force according to cutting conditions, and the parameters to monitor the tool wear were selected from the tendency of the cutting force and acceleration according to cutting length in the high-speed machining of the high hardened materials(STD11).

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.61-66
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• 1994

Tool condition monitoring is one of the most important aspects to improve productivity and quality and to achieve intelligent machining system. The tool state is classified into three groups as chipping, wear and fracture. In this study, wear of a ceramic cutting tool for hardened die material (SKD11) was investigated. Flank wear was occured more dominant than crarer wear. Therefore, to predict flank wear, the modeling of cutting force has been performed. The modeling of cutting force by an assumption that act the stress distribution on the tool face obtained through a numerical analysis. The relationships between the cutting force and the tool wear can be constructed by machining paraneters with cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions of the flank wear is approximately in good agreement with experimental result.

• 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.

• Steel and Composite Structures
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• 제21권3호
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• pp.573-588
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• 2016

Carbon Fiber Reinforced Plastic (CFRP) and Titanium Alloy (Ti6Al4V) stack, extensively used in aerospace structural components are assembled by fasteners and the holes are made using drilling process. Drilling of stack in one shot is a complicated process due to dissimilarity in the material properties. It is vital to have optimal machining condition and tool geometry for better hole quality and tool life. In this study the tool wear and hole quality were analysed by experimental analysis using three modified twist drills and online tool condition monitoring using Acoustics Emission (AE) sensor. Helix angle and point angle influence tool performance and cutting force. It was found that a tool geometry (TG1) with high helix angle of $35^{\circ}$ with low point angle $130^{\circ}$ results in reduction in thrust force of 150-500 N range but the TG2 also perform almost similar to TG1, but when compared with the AErms voltage generated during drilling it was found that progressive rise in voltage in TG1 is less with respect to TG2 which can be attributed to tool life. In process wear monitoring was done using crest factor as monitoring index. AErms voltage were measured and correlated with the performance of the drills.

• 한국정밀공학회지
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• 제12권5호
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.148-152
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• 2000

Detection of tool wear is very important in automated manufacturing. This paper presents tool condition monitoring system based on the wavelet analysis of the AC servo motro current in drilling and milling process. The current measurement system is relatively simple and its mounting will not affect machining operations. The discrete wavelet transform was used to decompose the current signal of a spindle AC servo motor in time - frequency domain. The feature vectors were extracted from the decomposed signals and compared for normal and wear condition. The results show the possibility for the effective application of wavelet analysis to tool condition monitoring.

• 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.