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이응신(Lee, Eung-Shin),이장무(Lee, Jang Moo),송준규(Song, Jun Gyu) 한국신재생에너지학회 2005 한국신재생에너지학회 학술대회논문집 Vol.2005 No.06
Starting with the development of environmentally friendly electric controlled engine, sensors have played an increasing role in automotive technology. Electric power steering (EPS) systems are more and more replacing hydraulic systems. To improve fuel economy, the power assistance is provided by an electric drive. In this paper, development of a unique torque sensor for EPS is introduced. A capacitive type torque sensor is concluded to be suitable for EPS because its output is accurate, linear and robust against the variance of temperature, and patents have not applied so much.
김승우,이장무,Lee, Jang Moo 대한기계학회 1979 대한기계학회논문집 Vol.3 No.3
An optimim reduction gear ratio problem for a subway rapid transit car in Seoul was solved by using a computer program package, which is a modified and extened version of the simple model by Mischke. The optimum value of reduction gear ratio was evaluated by minimizing the total start-to stop time.The validity of the computer program package was verified by cross-checking the calculated values of gear ratio and dynamic characteristics with the actual and measured values.
이신영,이장무,Lee, Sin-Young,Lee, Jang-Moo 대한기계학회 1999 大韓機械學會論文集A Vol.23 No.6
In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on, and cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling process for various cutting conditions, a mathematical model is important and this model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging farces of cutting tests. In this paper, the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in teaming stage as the omitted numbers of experimental data increases the average errors increase.
박찬일,이장무,Park, Chan-Il,Lee, Jang-Moo 대한기계학회 1996 大韓機械學會論文集A Vol.20 No.5
Gear vibration is caused by the mesh stiffness, gear accuracy, and assembling errors. For these reasons, helical gear has the azial, radial, and rotational vibrations. In this study, the mesh stiffness is calculated by considering the tooth bending, contact, and foundation deformations. Rotational vibration of helical gear with tooth error is modeled by the nonlidear equation of motion with single degree of freedom and is anlyzed numerically. Also, by a specially designed experimental set-up, the analysis are cross-checked and the vibration characteristics of helical gear are discussed.