Development of Regression Model for Predicting the Maximum Static Friction Force of Tractors with a Front-End Loader
2023
Kim, S.J. | Gim, D.H. | Jang, M.K. | Hwang, S.J. | Kim, J.H. | Yang, Y.J. | Nam, J.S.
PURPOSE: In this study, regression models that can predict the maximum static friction force of tractors with a front-end loader were developed. METHODS: For the development of the regression models, the maximum static friction force of a tractor was measured under various load and soil conditions. Tests were then conducted on pavement and off-roads while varying the payload of the front-end loader and rear ballast weight. The maximum static friction force measured in the regression analysis was set as a dependent variable, and independent variables were set by combining the tractor weight, payload of the front-end loader, and vertical reaction forces of the front and rear axles. The accuracy of the multiple regression models developed through regression analysis was evaluated based on the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). RESULTS: In the analysis results, the regression model constructed based on the vertical reaction forces of the front and rear axles exhibited the highest accuracy with R², RMSE, and MAE values of 0.37, 0.69 kN, and 0.59 kN, respectively, followed by the regression model constructed based on the total weight. CONCLUSION: The utilization of the regression model constructed based on the vertical reaction force of the front and rear axles achieved the highest accuracy. But separate calculations were needed according to the load conditions. Therefore, the consideration of the regression model constructed based on the total weight is determined to be more efficient in predicting the maximum static friction force.
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