• Transactions of Materials Processing
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• v.12 no.2
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• pp.83-87
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• 2003

Current state of plastic processes of steel bearing parts is surveyed. According to the advances in plastic forming technologies and their great advantage to mass production, plastic processes are adopted in manufacturing majority of bering parts. The rings are forged or ring rolled and the rolling elements, i.e, balls or rollers are cold formed before fine machining. Bearing's steel retainers are mainly press formed using cold rolled seel strips. Including the general explanation about above processes, some details of forging technology, control of forging temperature and after cooling process, and examples of computer simulation are described.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.685-686
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.97-98
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• 2009

• The Journal of the Acoustical Society of Korea
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• v.16 no.3E
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• pp.37-43
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• 1997

A frequency-variable ultrasonic vibrator is designed, which is made of a multi-layered PZT vibrator with a conical horn. Transmission line equations for the conical horn are derived and analyzed using the equivalent circuit method in order to analyze the characteristics of the vibrator. The controllability of the driving characteristics by varying the electrical impedance is confirmed by the experimental results of the free admittance characteristics and the vibrational velocity distributions.

• Design & Manufacturing
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• v.9 no.3
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• pp.34-40
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• 2015

In the study, Three-dimensional drawing parts for conformal cooling circuit cavity & core and their 3D Metal parts using DMLS(Direct MetalLaser Sintering) and NC integrated machining center were showned. For conformal cooling circuit cavity and core parts, I discussed its practicality to DMLS multiple machinins process introducing general manufacturing process and comparing with them.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.798-801
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• 1993

This paper presents the decision support system using fuzzy knowledge to adapt the cutting conditions chosen by a conventional expert system to a particular machine tool, workpiece and clamping system. These preliminary results demonstrate the capability of fuzzy logic to adjust cutting parameters taking into account parameters difficult to quantify.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• Safety and Health at Work
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• v.15 no.3
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• pp.317-326
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• 2024

Background: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion: This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.4
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• pp.52-60
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• 1999

In this study the effect of roundness error with respect to the cutting conditions using the external cylindrical work piece by end mill cutting in a machining center was studied. the end mill used in this study is HSS coated with Ti-N which is of Ø 12-4 flutes. The material of workpiece is SM20C and cutting oil is used as a cooling flued The cutting experiments were carried out for the several cutting conditions(depth of cut height of end mill feed rate revolution per minute and cutting direction) and their roundness effects were compared using the least squares circle measuring method. The experimental results are summarized as follows : 1) The cutting depth is dominant for the roundness of a cylindrical work piece and the cutting speed must be determined precisely when the cutting depth is large 2) When the cutting direction in circular manufacturing is the same with the spindle rotation i.e up-cutting condition the surface roundness is also improved.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.145-150
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• 2000

Micro punching is one of general methods to fabricate simple holes such as permanent ink-jet printer nozzles. A thin punch, that is need for micro punching, usually has been obtained by mechanical machining. There are some method to obtain a thin punch from a cylindrical rod, e.g., microgrinding and WEDG (Wire Electro-Discharge Grinding). Inefficiently, only one punch can be obtained from these machining methods. In contrast with these methods, many punches can be fabricated simultaneously by electrochemical process. Electrochemical process has usually aimed to obtain very sharp probe for atomic force microscopy (AFM) or scanning tunneling microscopy (STM), and it has not been considered the whole shape of a probe in spite of good merits. In this paper, an ultrathin punch with a tapered shape and a cylindrical tip is newly fabricated by electrochemical process.