• Journal of the Korean Society for Precision Engineering
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• v.16 no.4 s.97
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• pp.229-236
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• 1999

The surface, generated by end milling operation, is deteriorated by tool runout, vibration, tool wear and tool deflection, etc. Among them, the effect of tool deflection on surface accuracy is analyzed. Surface generation model for the prediction of the topography of machined srufaces has been developed based on cutting mechanism and cutting tool geometry. This model accounts for not only the ideal geometrical surface, but also the deflection of tool due to cutting force. For the more accurate prediction of cutting force, flexible end mill model is used to simulate cutting process. Computer simulation has shown the feasibility of the surface generation system. Using developed simulation system, the relations between the shape of end mill and cutting conditions are analyzed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.2
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• pp.268-274
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• 2013

Electric discharge machining drill (EDM-drill) is an efficient process for the fabrication of micro-diameter deep metal hole. As there is non-physical contact between tool (electrode) and workpiece, EDM-drill is widely used to machine the hard machining materials such as high strength steel, cemented carbide, titanium alloys. The electro-thermal energy forces the electrode to wear out together with the workpiece to be machined. The electrode wear occurs inside of a machining hole. and It causes hard to monitor the machining state, which leads the productivity and the quality to decrease. Thus, this study presents a methodology to estimated the electrode wear amount while two coefficients (scale factor and shape factor) of the logarithmic regression model are evaluated from the experiment result. To increase the accuracy of estimation model, the linear transformation method is adopted using the differences of initial electrode wear differences. The estimation model is verified through experiment. The experimental result shows that within minute error, the estimation model is able to predict accurately.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.47-51
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• 1997

The high speed machining which can improve the production and quality in machining has been adopted remarkably in dietmold industry. As the speed of machine tool spindle increases, the machinability evaluation and monitoring of high speed machining is necessary. In this study, the machinability of 30, 000rpm class spindle was evaluated by using the developed tool dynamometer and the machining properties of high hardened and toughness materials in high speed were examined. Finally, the in-process monitoring technologies of tool wear were presented through the prediction by the experimental formula and pattern recognition by the neural network.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• Design & Manufacturing
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• v.16 no.4
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• pp.1-6
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• 2022

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.2
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• pp.453-467
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• 2018

The excavation performance and the cutting tool wear prediction of shield TBM are very important issues for design and construction in TBM tunneling. For hard-rock TBMs, CSM and NTNU model have been widely used for prediction of disc cutter wear and penetration rate. But in case of soft-ground TBMs, the wear evaluation and the excavation performance have not been studied in details due to the complexity of the ground behavior and therefore few testing methods have been proposed. In this study, a new soil abrasion and penetration tester (SAPT) that simulates EPB TBM excavation process is introduced which overcomes the drawbacks of the previously developed soil abrasivity testers. Parametric tests for penetration rate, foam mixing ratio, foam concentration were conducted to evaluate influential parameters affecting TBM excavation and also ripper wear was measured in laboratory. The results of artificial soil specimen composed of 70% illite and 30% silica sand showed TBM additives such as foam play a key role in terms of excavation and tool wear.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.2
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• pp.38-45
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• 1996

This study was focused on the prediction of the approprite tool life by clarifying the correlation between progressive drill wear and AE signal. on drilling SM45C the following results have been obtained; RMSAE, AE CUM-CNTS had a tendency to increase slowly according to wear size, at 1000rpm, 150mm/min However, these increased suddenly in the range of 0.20~0.22mm wear, about 102 holes and had a tendency to go up and down until the drilling was impossible. The sudden increase of AE signals shows that something is wrong and it is closely connected with drill wear and chipping. It also makes the working surface bad From the above results, AE signals could be used to monitor the drill's condition and to determine the right time to change tools.

• Transactions of Materials Processing
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• v.31 no.2
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• pp.103-108
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• 2022

Systematic quality control based on real time data is required for modern factories. This study introduced a method of predicting punch wear in the trimming process of automobile parts. Based on monitoring data of the mass production process using a bolt-type piezo sensor, it was shown that precursor symptoms of die wear could be predicted from the change in load pattern with respect to production volume. The load pattern that changed according to the wear of the die was verified by numerical analysis.

• Tribology and Lubricants
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• v.10 no.4
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• pp.13-26
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• 1994

Effects of resin contents, number of carbonization, graphitization, sliding speed, and oxidation on friction and wear behavior of carbon/carbon composite materials were investigated. Friction and wear tests were carried out under various sliding conditions. An experimental setup was designed and built in the laboratory. Stainless steel disks were used as the counterface material. Friction coefficient, emperature, and wear factor were measured with a data acquisition system. Wear surfaces were observed by the scanning electron microscope. It has been shown that the average friction coefficient was increased with the sliding speed in the range of 1.43~6.10 m/s, but it as decreased in the range of 6.10~17.35 m/s. Specimens prepared by different numbers of carbonization. showed variations in friction coefficient and friction coefficient of the graphitized specimen was the highest. Friction coefficients depended on contribution of the plowing and adhesive components. As the number of carbonization was increased, wear factor was reduced. Wear factor of the graphitized specimens dropped further. In the case of graphitized specimens, sliding speed had a large influence on wear behavior. When the tribological experiments were conducted in nitrogen atmosphere, the wear factor was decreased to two thirds of the wear factor obtained in air. It is obvious that the difference was affected by oxidation. Results of friction and wear tests were applied to a neural network system based on the backpropagation algorithm. A neural network may be a valuable tool for prediction of tribological behavior of the carbon/carbon composite material if ample data are present.