• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.1-7
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• 2005

The prediction for tool life is one of the most important factors for increasing reliability, stability, and productivity of manufacturing system. This paper deals with a tool life prediction method in view of reliability assessment for cutting tools. In this study, flank wear was focused among multi-factors deciding the tool wear state. First, tool life was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning, including parameters of cutting speed, feed rate, and cutting depth. Second, each of cutting conditions of end-milling was equivalently converted to apply ball end-mill data to the extended Taylor equation. The web-based prediction program for tool life was developed as one of reliability assessment programs for machine tools.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.317-322
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• 2004

This paper deals with a prediction method of tool life in view of the reliability assessment. In this study, the flank wear was studied among multi-factors deciding the tool wear state. Firstly, tool lift was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning data, including parameters of cutting speed, feed rate, and cutting depth. Secondly, each of cutting conditions of endmilling was equivalently converted to apply ball endmill data to the extended Taylor equation. The web-based reliability prediction program for tool lift is being developed as one of reliability assessment programs to for the machine tools.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.90-96
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• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.2 s.17
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• pp.5-12
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• 2004

The erosion wear of gun barrel occurs due to heat and chemical reactions. The high pressure and temperature in chamber increase the erosion wear. It is known that the metal phase transfer is the primary wear factor in a gun barrel under high temperature. In this paper, the tool of wear prediction in high pressure gun tube has been developed. The program developed has three modules such as DIRECT(interior ballistics analysis module), INVERSE(gun design module), and WEAR(wear prediction module). The prediction of wear was compared with the experimental data which was collected in the field unit. The prediction results shows good trend with the collected data.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1051-1055
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• 1997

Hot forging is widely used in the manufacturing of automotive component. The mechanical, thermal load and thermal softening which is happened by the high temperature die in hot forging. Tool life of hot forging decreases considerably due to the softening of the surface layer of a tool caused by a high thermal load and long contact time between the tool and workpieces. The service life of tools in hot forging process is to a large extent limited by wear, heat crack, plastic deformation. These are one of the main factors affecting die accuracy and tool life. It is desired to predict tool life by developing life prediction method by FE-simulation. Lots of researches have been done into the life prediction of cold forming die, and the results of those researches were trustworthy, but there have been little applications of hot forming die. That is because hot forming process has many factors influencing tool life, and there was not accurate in-process data. In this research, life prediction of hot forming die by wear analysis and plastic deformation has been carried out. To predict tool life, by experiment of tempering of die, tempering curve was obtained and hardness express a function of main tempering curve.

• Design & Manufacturing
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• v.17 no.1
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• pp.7-12
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• 2023

Recently, with the emergence of the 4th industrial revolution, the demand for smart factories and factory automation is increasing. In this study, a tool life prediction program was developed to select optimal machining conditions using CNC milling equipment, which is widely used in flexible production and automation. The equipment used in the experiment was Hwacheon Machine Tool's 5-axis machining equipment, and the tool used was a 17F2R tool. For the machining path, the down-milling cutting method was selected and long-term machining was performed. The analysis standard for side wear on the tool was set at 0.1 to 0.2 mm, and tool life data and wear data were obtained in the cutting experiment. The program was created through the data obtained from the experiment, and a prediction rate of over 90% was secured when comparing the experimental data and the predicted data.

• 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 Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.46-53
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• 1999

Cutting force characteristics is closely related with tool wear on the end milling. And it is found that the tool wear can be properly obtained by observation through the tool-maker's microscope when STS 304 is cut using an end mill. The relationship between the tool wear and the cutting force is established based on data obtained from a series of experiments. A cutting force model can be derived from basic cutting force model using parasitic force components of this tool wear. The results of th simulation using the cutting force model proposed in this paper were verified experimentally and a good agreement was partly obtained. The proposed model is capable of predicting increased cutting force due to tool wear.