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Relationship between the 3D Footprint of an Agricultural Tire and Drawbar Pull Using an Artificial Neural Network
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Applied Engineering in Agriculture. 38(2): 293-301. (doi: 10.13031/aea.13851) @2022
Authors: Maurizio Cutini, Corrado Costa, Massimo Brambilla, Carlo Bisaglia
Keywords: Field efficiency, Phenolic resin, Traction, Tractor.
Improvement of tractor traction provides better field efficiency. Drawbar pull increased with tire footprint length. Drawbar pull decreased with increasing tire footprint volume and depth. 3D footprint parameters, which the 2D footprint do not contain, affected the drawbar pull significantly. The ANN highlighted the relation adequately.
Improvement of tractor traction provides better field efficiency.
Drawbar pull increased with tire footprint length.
Drawbar pull decreased with increasing tire footprint volume and depth.
3D footprint parameters, which the 2D footprint do not contain, affected the drawbar pull significantly.
The ANN highlighted the relation adequately.
Abstract.Improving the traction of an agricultural tractor on the field increases its working efficiency and capacity. Heavy work, like plowing, entails high levels of tire slip, which is directly related to power loss when the transmission of drawbar pull is required. Accordingly, it is possible to hypothesize that a tire with a higher traction capability could increase the working efficiency of the machine. The natural evolution for measuring the geometrical parameters of tires has led to the consideration of three-dimensional (3D) footprints since the distribution of the vertical stresses at the soil–tire interface may be highly non-uniform. In this study, the data acquired from 3D footprints of 10 sets of tires underwent processing along with the data from drawbar tests carried out with the same tires on soil terrain at different slip ratios. Subsequently, artificial intelligence multivariate methods based on artificial neural networks allowed traction prediction and verified the importance that the acquired geometrical parameters have on the measured drawbar pull. The study confirmed the correlation of the geometrical parameters of the 3D tire footprint with the drawbar pull and the results of the artificial intelligence modelling underlined the impact of these acquisitions. However, further work that considers various lug geometries is required to extend the generalizability of the studied methodology.(Download PDF) (Export to EndNotes)