• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.2
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• pp.223-231
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• 1986

During the past decade, the Quick Tool-Life Testing Method has been studied. However, a generalized theory and testing method for the quantitative measurement of tool wear have not been developed yet. Among many factors to affect the tool wear, the flank wear is regarded as a main factor. In this study, the behavior of the flank wear for carbide tool was studied as a preceding step to present a simple method for Quick Tool-Life Testing, and it was found that the flank wear varies in direct proportion to cutting time, and the following general equation is obtained for the flank wear curves with respect to cutting time and velociety.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.5
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• pp.1055-1062
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• 1988

The characteristics of tool wear and the machinability in cutting of GFRP have been studied. The wear behavior of carbide insert tools(P20, M10, K10) and Cermet in TiC grade was studied by turning of changing the cutting condition. Machinability could be estimated as the following empirical formula, CT$^{n}$ =W The main results obtained are as follows: (1) Dependence of rate of tool wear on cutting speed; with increases of cutting speed, the rate of tool wear initially increases gradually(1st range), then it increases proportionally to cutting speed(2nd range), and finally the rate is constant(3rd range). (2) When the contact length has a main, effect on tool wear, the cutting speed does nit affect the tool wear. On the contrary, the cutting speed has a main effect on tool wear, the contact length does not affect the tool wear. (3) The order of machinability is K10, M10, P20 and Cermet in TiC grade.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.65-70
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• 2024

Tool wear is considered an important issue in manufacturing and engineering, as worn tools can negatively impact productivity and product quality. Given that the wear status of tools plays a decisive role in the production process, measuring tool wear is a key task. Consequently, there is significant attention in manufacturing fields on the precise measurement of tool wear. Current domestic methods for measuring wear are limited in terms of speed and efficiency, with traditional methods being time-consuming and reliant on subjective evaluation. To address these issues, we developed a measurement module implementing the DeepContour algorithm, which uses image processing technology for rapid measurement and evaluation of tool wear. This algorithm accurately extracts the tool's outline, assesses its condition, determines the degree of wear, and proves more efficient than existing, subjective, and time-consuming methods. The main objective of this paper is to design and apply in practice an algorithm and measurement module that can measure and evaluate tool wear using image processing technology. It focuses on determining the degree of wear by extracting the tool's outline, assessing its condition, and presenting the measured value to the operator.

• Structural Engineering and Mechanics
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• v.31 no.1
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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.

• Journal of the korean Society of Automotive Engineers
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• v.9 no.5
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• pp.49-56
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• 1987

This paper is a study on the effect of the cutting speed on the tool wear in turning of the glass fiber reinforced plastics. The wear behavior of cutting tool is studied by means of turning, changing the cutting speed and feed in the wide range. Moreover, the theoretical model applicable to the cutting speed of wide range is analysed. The main results obtained are as follows: The relation between the tool wear and the cutting speed is divided into three range in case of the constant cutting distance. 1) At the low cutting speed, the tool wear is independent of the cutting speed, but dependent mainly on the contact length between tool and glass fiber(lst range). 2) At the high cutting speed, the tool wear is independent of the contact length, and dependent on the cutting speed only(2nd range). The tool wear increases in proportion to the cutting speed. 3) At the higher cutting speed than the speed in the 2nd range, the tool wear is independent both of the cutting speed and the contact length(3rd range). 4) In the 3rd range, tool flank wear is constant and is observed that only the wear of cutting edge increases.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.58-63
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• 1993

The charactistics of AE(Acoustic Emission) signal is related to cutting conditions, tool materials and tool geometry in metal cutting. The tool geometry change which is derived from tool wear affects the source of AE signal in machining process. The relationship between AE signal and tool wear was experimentally investigated. THe value of RMS(Root Mean Sequare) and Amplitude of AE signal were increased in tool wear condition. Also the high value of Count per Hit and Count vs. Frequency was observed in this condtion. As a result, tool wear can be effectively detected by AE signal during cutting operation.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.8
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• pp.116-125
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• 1998

In this paper, main target is to select parameters for prediction of tool wear and detection of tool fracture. The research about choosing parameter for prediction of tool wear is done by using force ratios. Also current sensor, tool-dynamometer, and accelerometer are used for researching detection method of tool fracture. Experiment is done using Taguchi's method in medium machining conditions. Parameter which is best for prediction of tool wear and detection of tool fracture by deviation analysis is selected. In this paper, tool wear means flank wear.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.174-181
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• 1999

In this paper the technique to predict tool wear theoretically in shearing process is suggested. The tool wear in the process affects the tolerances of final pans, metal flows and costs of processes. In order to predict the tool wear 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 from finite element simulation, such as nodal velocities and nodal forces, are transformed into sliding velocity and normal pressure on tool monitoring points respectively. The monitoring points are automatically generated and the wear rates on these points are accumulated during the process. It is assumed that the wear depth on the tool surface is linear function of the lot sizes based upon the known experimental results. The influence of clearance between die and punch upon tool wear is also discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.20-29
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• 2004

A Tool wear monitoring system is indispensable for better machining productivity with guarantee of machining safety by informing the tool changing time in automated and unmanned CNC machining. Different from monitoring using other signals, the monitoring of spindle current has been used without requiring additional sensors on machine tools. For the reliable tool wear monitoring, current signal only of tool wear should be extracted from other parameters to avoid exhaustive analyses on signals in which all parameters are fused. In this paper, influences of force components of parameters on measured spindle current are investigated and a hybrid approach to cutting force regulation is employed for tool wear signal extraction in the spindle current. Finally, wear levels are verified with experimental results by means of real-time feedrate aspects changed to regulate the force component of tool wear.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.69-73
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• 1996

Tool wear monitoring is very important aspect in metal cutting because tool wear effects quarity and precision of workpiece, tool life etc. In this study we detected force signal through tool dynamometer in turning and using it we conducted 6th AR modeling and fractal analysis. Finally the back-propagation model of the neural network is utilized to monitor tool wear and features are extracted through AR model and fractal analysis.