• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.204-208
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• 1998

The fundamental issues to evaluate machine tool's performance through simulation pertain to the physical models of the machine tool itself and of process while the practical problems are related to the development of the modular software structure. It allows the composition of arbitrary machine/process models along with the development of programs to evaluate each state of machining process. Surface roughness is one of the fundamental factors to evaluate machining process and performance of machine tool, but it is not easy to evaluate surface roughness due to its tribological complexity. This paper presents an algorithm to calculate surface roughness considering cutting geometry, cutting parameters, and contact dynamics of cutting between tool and workpiece as well as tool wear in turning process. The designed virtual machining system can be used to evaluate the surface integrity of a turned surface during the design and process planning phase for the design for manufacturability analysis of the concurrent engineering.

• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.19-25
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• 2017

A design methodology for parametric design optimization of washing machine door is presented. We develop a motion simulator and a robotic door to simulate the various motion of washing machine doors. The motion of the washing machine door is related to hinge parameters. Springs and dampers are usually used in the hinge of washing machine door for controlling motion of the door. A physical simulator of the door motion is used for finding candidate parameters of the hinge and a robotic door whose motion is controlled algorithmically is used for consumer tests. Through the consumer evaluation on the robotic motion, the optimized parameters are determined. We find the optimal parameters as a function of angle and angular velocity of the door.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.51-60
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• 2023

Quantum machine learning (QML) is an area of quantum computing that leverages its principles to develop machine learning algorithms and techniques. QML is aimed at combining traditional machine learning with the capabilities of quantum computing to devise approaches for problem solving and (big) data processing. Nevertheless, QML is in its early stage of the research and development. Thus, more theoretical studies are needed to understand whether a significant quantum speedup can be achieved compared with classical machine learning. If this is the case, the underlying physical principles may be explained. First, fundamental concepts and elements of QML should be established. We describe the inception and development of QML, highlighting essential quantum computing algorithms that are integral to QML. The advent of the noisy intermediate-scale quantum era and Google's demonstration of quantum supremacy are then addressed. Finally, we briefly discuss research prospects for QML.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1863-1872
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• 2006

In FMS (Flexible Manufacturing System) and CIM (Computer Integrated Manufacturing), machine-tools have been the target of integration in the last three decades. The conventional concept of integration is being changed into the autonomous manufacturing device based on the knowledge evolution by applying advanced information technology in which an open architecture controller, high-speed network and internet technology are included. In the advanced environment, the machine-tools is not the target of integration anymore, but has been the key subject of cooperation. In the near future, machine-tools will be more improved in the form of a knowledge-evolutionary intelligent device. The final goal of this study is to develop an intelligent machine having knowledge-evolution capability and a management system based on internet operability. The knowledge-evolutionary intelligent machine-tools is expected to gather knowledge autonomically, by producing knowledge, understanding knowledge, reasoning knowledge, making a new decision, dialoguing with other machines, etc. The concept of the knowledge-evolutionary intelligent machine is originated from the machine control being operated by human experts' sense, dialogue and decision. The structure of knowledge evolution in M2M (Machine to Machine) and the scheme for a dialogue agent among agent-based modules such as a sensory agent, a dialogue agent and an expert system (decision support agent) are presented in this paper, with intent to develop the knowledge-evolutionary machine-tools. The dialogue agent functions as an interface for inter-machine cooperation. To design the dialogue agent module in an M2M environment, FIPA (Foundation of Intelligent Physical Agent) standard platform and the ping agent based on FIPA are analyzed in this study. In addition, the dialogue agent is designed and applied to recommend cutting conditions and thermal error compensation in a tapping machine. The knowledge-evolutionary machine-tools are expected easily implemented on the basis of this study and shows a good assistance to sensory and decision support agents.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.90-95
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• 2005

To perform the finish outside-diameter grinding process of ferrules which are widely used as fiber optic connectors, a high-precision centerless grinding machine is necessary. In this study, the thermal characteristics of the high-precision centerless grinding machine such as the temperature distribution, temperature rise and thermal deformation, are estimated based on the virtual prototype of the grinding machine and the heat generation rates of heat sources related to the machine operation conditions. The reliability of the predicted results is demonstrated by the temperature characteristics measured from the physical prototype. Especially, the predicted and measured results show the fact that the high-precision centerless grinding machine consisted of the hydrostatic GW and RW spindle systems, hydrostatic RW feeding mechanism, RW swivel mechanism, on-machine GW and RW dressers, and concrete-filled steel bed, has very stable thermal characteristics.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.60-67
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• 2008

The vibrations and noise origin in electric material is due to several coupled physical phenomena. The revolving electric machine complete modeling is complex; it does not allow simple parametric machine structure studies for various operation modes. This work presents a simple electromagnetic model which makes possible the machine principal parts flow estimation from flux density. Special interest is given in determining Switched Reluctance Machine (S.R.M) radial acceleration in accordance with the current supply. Our focus will be only on the magnetic origin efforts that are dominating in the S.R.M. The efforts calculation versus the current is presented in the case of a machine with a linearized rate. These efforts are considered as a tangential force producing the torque and a radial force that generates no torque. The application is realized on a 6/4 low power S.R.M type (6 stator teeth and 4 teeth rotor). The mechanical response is substituted in a transfer function. The model takes account of the power supply of the machine, the relation between the current supply and the efforts as well as the vibratory response of the machine to these efforts. Finally, the model is validated by comparison with similar experimental results within the framework of the definite assumptions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.119-128
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• 1993

Effective machine capacity is affected by the physical and geometrical conditions of the fields. In the small and scattered farmland structure field efficiency is greatly influenced by plot geometry. In this paper, a method for estimating field efficiency and effective machine capacity was developed . The developed method was applied to Korean paddy cultivation. Various time elements related to farm operations for small and scattered plots are discussed in this paper . Available working time is divided into two parts, viz. the preparation time for machine operation and actual working time. Two kinds of machine efficiencies, namely , Machine Efficiency 1, applicable on a single large plot or set of well consolidated plots ; and Machine Efficiency 2, applicable on small and scattered multiple plots, are considered. Based assumptions made and steps followed to construct the model are discussed. Effective capacity of each machine based on different plot geometries are calculated y the model. Machine efficiency on a single plot increases with increase in the dimension of longer side of the plot . Low speed, low theoretical capacity machines have higher machine efficiency which is only slightly influenced by plot geometry. As plot geometry is improved , the machine efficiency of high speed, high capacity machines increases rapidly. The effects of short side length and plot size on machine efficiency on a single plot depend on the type of farm operation. For a particular plot shape, as plot size increases, machine efficiency on multiple plots increases rapidly. The effects of consolidation on machine efficiency is highly significant if the plot size is small and/or machine size is large.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.1-10
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• 2022

The purposes of this study were to suggest 1) a future direction for Korea's machine tool industry and 2) how to secure the safety and reliability of emerging intelligent or automated machine tooling. The study concludes that, overseas, the machine tool industry is growing again while promoting innovation by converging with ICT. Accordingly, Korea also promotes ICT innovation to advance the machine tool industry, which is at the core of the national economy. As a result, unlike in the past, the frequency of serious injuries like entrapment accidents has recently decreased, while the proportion of collision accidents has increased. In addition, a new type of accident has become possible. Since ICT is network-based, the distinction between work and rest can become ambiguous; there is a risk of hacking, working hours and places are flexible and there are risk factors for diseases like chronic fatigue due to overload of specific personnel. As robots and automation are introduced, there is also a high probability of problems caused by physical and psychological burdens on system operators and resulting fatigue.

• The Journal of Korean Physical Therapy
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• v.11 no.3
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• pp.13-21
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• 1999

Active movement is able to actively contract his muscles and move a segment either with or without assistance. This movement maintain physiologic elasticity and contractility of the participating muscles, provide sensory feedback from the contracting muscles and stimulus for bone integrity as well as increase circulation and prevent thrombus formation, in addition to develop coordination and moor skills for functional activities. Passive movement is the motion to the external force; gravity, machine, another individuals. Active movement is more activated rather than passived on the central nervous system. Therefore, we think that active movement is more effected facilitating through specific inhibitory mobilization of muscle.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).