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데이터마이닝 기법을 적용한 타이어 FLATSPOT 예측 프로그램 개발
최대원(Daewon Choi),한상협(Sang-Hyub Han),김기현(Gihyun Kim),박태구(Taeku Park),조춘택(Choontack Cho) 한국자동차공학회 2007 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
Tire FlatSpot is due to a vehicle parked without moving for a long time In general, tire flatspot phemomemon will be disappeared when a driver have a tire rolled for a few minutes. Flatspot is a low harmonic phenomenon and primarily contributes to the third or fourth harmonics of tire run out. The tire non uniformity caused by a flatspot may induce noticeable vibrations in some vehicles during operation in the decay process. The magnitude and decay of a flatspor depends on many factors - tire construction, material creep properties, tire radial load and time duration, inflation, tire and ambient temperatures, tire mileage, etc. This Paper did apply Data Mining Algorithm to prediction program development of Tire FlatSpot. This program will indicate tire design guide line to tire engineer correctively and quickly.
전문가 지식과 퍼지 논리를 적용한 타이어 PRAT 예측 프로그램 개발
최대원(Daewon Choi),김기현(Gihyun Kim),박태구(Taeku Park),조춘택(Choontack Cho) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
It is generally known that the PRAT(Plysteer Residual Aligning Torque) is a tire design factor to affect vehicle pull. This paper was written to predict a tire PRAT using knowledge information and Fuzzy Logic. Input data of this PRAT Prediction Program are Lateral groove angle of tread pattern. Steel Belt Angle and Cap Ply Laying Condition. This program will indicate tire design guide line to tire engineer correctively and quickly.
퍼지 추론 및 인공 신경망 알고리즘 결합에 의한 타이어 회전저항 예측
최대원(Daewon Choi),김기현(Gihyun Kim),장석희(Sukhee Jang),박태구(Taeku Park),조춘택(Choontack Cho) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
Tire manufacturers are trying to reduce the tire rolling resistance for decreasing vehicle fuel consumption. Tire rolling resistance is caused by hysteretic losses of various tire components. This paper represents a prediction system that can forecast the tire rolling resistance in advance before making tire. The algorithm of Fuzzy Inference and Neural Network was applied tor this prediction system. Tire construction and component factors were treated by Fuzzy Inference and Neural Network Algorithm respectively. The achievement of this paper will be applied to automotive and tire industry to reduce energy consumption.