Virtual Testing of Counterbalance Forklift Trucks: Implementation and Experimental Validation of a Numerical Multibody Model

This study investigates the dynamic behavior of a recently developed counterbalance forklift truck. The final objective is creating virtual testing tools based on numerical multibody models to evaluate the dynamic stresses experienced by the forklift family of interest during a reference operating c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Machines (Basel) 2020-06, Vol.8 (2), p.26
Hauptverfasser: Martini, Alberto, Bonelli, Giovanni Paolo, Rivola, Alessandro
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This study investigates the dynamic behavior of a recently developed counterbalance forklift truck. The final objective is creating virtual testing tools based on numerical multibody models to evaluate the dynamic stresses experienced by the forklift family of interest during a reference operating cycle, defined by the manufacturer’s testing protocols. This work aims at defining sufficiently accurate and easy-to-implement modelling approaches and validation procedures. It focuses on a specific test, namely the passage of a speed-bump-like obstacle at high velocity, which represents one of the most severe conditions within the reference cycle. Indeed, unlike most of the other wheeled vehicles, forklifts typically do not have advanced suspension systems and their dynamic response is significantly affected by ground irregularities. To this end, a preliminary model of the complete forklift, featuring rigid bodies and a simplified tire–ground contact model, is implemented with a commercial software. Experimental tests are conducted on the forklift to measure the vehicle vibrations when running on the obstacle, for model validation purposes. After model updating, the results provided by the numerical simulations match the experimental data satisfactorily. Hence, the modelling and validation strategies are proven viable and effective.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines8020026