Improved Prediction Model for Maximum Static Friction Force of Agricultural Tractor with Front-End Loader on Paved...

Area of Science:

• Agricultural Engineering
• Tractor Dynamics
• Tire-Surface Interaction

Background:

• Tractor slip, overturning, and rollover are influenced by static friction.
• Existing models for predicting tractor static friction lack sufficient accuracy.
• Tractor safety, particularly on slopes, is compromised by inadequate friction prediction.

Purpose of the Study:

• To enhance the accuracy of a previously developed maximum static friction force prediction model for tractors equipped with front-end loaders.
• To investigate the influence of loading conditions on tractor static friction.
• To refine prediction models using measured data from various tractor configurations.

Main Methods:

• Measured maximum static friction forces of three tractors on paved roads under diverse loading conditions.
• Employed regression analysis to improve existing prediction models with new data.
• Compared the accuracy of models utilizing different variables, including front and rear axle reaction forces.

Main Results:

• Maximum static friction increased with greater rear ballast weight and decreased with increased payload.
• Regression models developed in this study showed significantly improved accuracy over previous models.
• The model incorporating both front and rear axle reaction forces demonstrated the highest prediction accuracy.

Conclusions:

• The reaction force of the rear axle has a more substantial impact on maximum static friction than the front axle.
• The developed regression model accurately predicts maximum static friction for tractors with front-end loaders on paved surfaces.
• Future research should extend these models to various soil types and dynamic conditions.