• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.830-835
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• 1995

To quickly determine the effect of the substitute component on the machine's performance is very important in the defign and the manufacturing processes. And minimizing machine cost and maximizing machine quality mandata predictability of machine accuracy. In the study, in order to evaluate the effects of the component's geometric errors and dimensions on the machining accuracy of gantry-type 5-axis machining centers, a geometric error analysis and virtual manufacturing system is developed based on the mathematical model for the shape generation motion of machine tool considering the component's geometric errors and dimensions, the solid modeling techniques and so on.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.10
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• pp.172-179
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• 1998

To quickly determine the effect of the substitute component on the machine's performance is very important in the design and manufacturing processes. And minimizing machine cost and maximizing machine quality mandate predictability of machine accuracy. In this study, in order to evaluate the effects of the component's geometric errors and dimensions on the machining accuracy of gantry-type 5-axis machining centers, a geometric error analysis and virtual manufacturing system are developed based on the mathematical model for the shape generation motion of machine tool considering the component's geometric errors and dimensions, the solid modeling techniques and so on.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.218-224
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• 1999

In the feed drive systems of machine tools that consist of many mechanical components such as motor, coupling, ballscrew, nut or table, a torsional vibration is often generated because of its elastic elements in torque transmission. Generally, the accuracy of motion control system is strongly influenced by the dynamic behavior of coupled transmission components. Especially, a torsional vibration caused by the elasticity of mechanical elements might deteriorate the quick movement of system and lead to shorten the life time of the mechanical transmission elements. So, it is necessary to analyze the electromechanical system mathematically to optimize the dynamic characteristics of the feed system. In this paper, the mathematical model of a feed drive system was developed and its mechanical characteristics were analyzed on the basis of the proposed model. The design concepts of speed control loop to stabilize a feed drive system were also proposed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.435-440
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• 2001

In this study, torsional and lateral vibrations of a gear box transmission system were analyzed theoretically using some mathematical models and examined to determine the causes of its excessive vibrations. As the results, it was found there exist possibility of resonance between gear mesh frequencies and lateral vibration mode of the transmission shaft during the third shifting mode operation. In order to avoid this resonance, we proposed changing the arrangement of gears on the transmission shaft. The measured vibration levels of the improved gear box were dramatically reduced. These results may be helpful to design a machine tool gear box with low noise and vibration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.323-328
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• 1992

The vibrations of the main spindles of the M/C tools is the most important in the con- sideration of the dynamic performance of the M/C tools. In order to analyze and predict the dynamic behaviour of the machine tool structure it is necessary to have the mathematical model of the system. The system identification is the procedure to provide us with the mathematical model of the system of which we want to know the dynamic characteristics. This study illustrates a procedure of the system identification of the structure of the M/C tools to predict the dynamic behaviour of the machine and further to have the basis for the design of M/C tools.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1212-1220
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• 2004

The structural unbalance mass and laundry are the important sources of the severe vibrations of automatic washing machines. In this paper, a mathematical model is developed for the dynamic analysis of the vertical axis automatic washing machines of pulsator-type. In the model, the rigid body motion of tub assembly is represented by six degrees of freedom and the dynamics of automatic hydraulic balancer is represented by one degree of freedom. The fundamental elastic modes of the tub shell and four suspension bars are also taken into account in the mathematical model, based on analytical and experimental modal analysis results. The 12 degrees of freedom equations of motion are derived by using the Lagrange's equations and the present dynamic model is evaluated by comparing the numerical simulation results with experimentally measured data.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.11-18
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• 2019

One of the key objectives of control systems is to maintain a system in a specific stable state. To achieve this goal, a variety of control techniques can be used and it is often uses a feedback control method. As known this kind of control methods requires mathematical model of the system. This article presents seesaw unstable system with two propellers which are controlled without use of a mathematical model instead. The goal was to control it using training data. For system control we use a logistic regression technique which is one of machine learning method. We tested our controller on the real model created in our laboratory and the experimental results show that instability of the seesaw system can be fixed at a given angle using the decision boundary estimated from the classification method. The results show that this control method for structural equilibrium can be used with relatively more accuracy of the decision boundary.