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Spatial model of the unsprung wheeled machine’s dynamic system

Chwastek S.

Journal of KONES

2014

Vol. 21, No. 2

45--52

Mobile heavy machines as unsprung vehicles exhibit low dissipation ability, hence the ride even at low speeds may give rise to intensive vertical and angular vibration. Vibrations thus produced are mostly in the low-frequency range and hence energy dissipation in tires will reduce the vibration intensity in a minor degree only. Particularly dangerous situations occur when the road wheels break away from the road surface due to the ’galloping’ effect. Kinematic excitation acting on the wheels is mostly uncorrelated stochastic (random) processes, giving rise to the "snake meandering" effect. That implies a major restriction on the ride velocity, which negatively affects the machine performance. The motion of tired wheels will always involve certain slipping. While investigating the feasibility of increasing the efficiency of the vibration reduction systems, one ought to take into account the variable adhesion of road wheels due to different dynamic loading acting on the vehicle axles during the ride. This study investigates the motion of unsprung mobile machines, taking into account the dynamic processes in the driving system under the conditions of the variable adhesion of road wheels. The model of interaction between a tired wheel and the terrain takes into account the relationship between the road wheel adhesion factor and the slipping action, as well as the impacts of the differential gear on distribution of drive torque. The 3D (spatial) model of a backhoe loader is considered. It is a two-axle self-propelled machine on a wheeled chassis. The mathematical model constitutes nonlinear and non-stationary differential equations of motion. Their stability is therefore associated with vibration intensity. Simulations in the time domain were supported by Matlab-Simulink. The purpose of this study is to improve the safety features during the ride of mobile heavy machines, basing on the parametric optimization of the model.