대형 상용차용 독립 현가부품 플래쉬 부피 예측 모델 개발 (Development of Flash Volume Prediction Model for Independent Suspension Parts for Large Commercial Vehicles)

서지정보

· 발행기관 : 한국소성∙가공학회
· 수록지 정보 : 소성∙가공 / 32권 / 6호 / 352 ~ 359페이지
· 저자명 : 박지우

초록

Recently, independent suspension systems have been applied not only to passenger cars but also to large commercial vehicles. Therefore, the need for research to domestically produce such independent suspensions for large commercial vehicles is gradually increasing. In this paper, we conducted research on the manufacturing technology of the relay lever, which are integral components of independent suspension systems for large commercial vehicles. Our goal was to reduce the flash volume generated during the forging process. The shape variables of the initial billet were adjusted to find proper forming conditions that could minimize flash volume while performing product forming smoothly. Shape variables were set as input variables and the flash volume was set as an output variable, and simulations were carried out to analytically predict the volume of the flash area for each variable condition. Based on the data obtained through numerical simulations, a regression model and an artificial neural network model were used to develop a prediction model that can easily predict the flash volume for variable conditions. For the corresponding prediction model, a goodness of-fit test was performed to confirm a high level of fit. By comparing and analyzing the two prediction models, the high level of fit of the ANN model was confirmed.

영어초록

Recently, independent suspension systems have been applied not only to passenger cars but also to large commercial vehicles. Therefore, the need for research to domestically produce such independent suspensions for large commercial vehicles is gradually increasing. In this paper, we conducted research on the manufacturing technology of the relay lever, which are integral components of independent suspension systems for large commercial vehicles. Our goal was to reduce the flash volume generated during the forging process. The shape variables of the initial billet were adjusted to find proper forming conditions that could minimize flash volume while performing product forming smoothly. Shape variables were set as input variables and the flash volume was set as an output variable, and simulations were carried out to analytically predict the volume of the flash area for each variable condition. Based on the data obtained through numerical simulations, a regression model and an artificial neural network model were used to develop a prediction model that can easily predict the flash volume for variable conditions. For the corresponding prediction model, a goodness of-fit test was performed to confirm a high level of fit. By comparing and analyzing the two prediction models, the high level of fit of the ANN model was confirmed.

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