Development of DEM-MBD coupling model for draft force prediction of agricultural tractor with plowing depth

During agricultural tillage, draft force is the most important factor affecting the performance of soil-machine system. In this study, a full-scale soil-tool-machine coupling model based on the coupling of DEM (discrete element method) and MBD (multibody dynamics) was established to predict draft force according to tillage depth during tillage operation. First, plowing field test was performed through a field load measurement system to develop DEM-MBD coupling model for the draft force prediction according to the tillage depth during the calibration and validation process of the DEM-MBD coupling model. The DEM soil bed was modeled by considering the target tillage depth and reflecting the soil property distribution that changes according to the soil depth. For the soil model, particle mass and surface energy were calibrated using bulk density values measured in the field and shear vane test results. In addition, the weight distribution ratio of each wheel in static state was measured, and the center of gravity was calculated for MBD modeling of the tractor-implement system. Addi-tionally, calibration of tire parameters was performed using the travel speed results of field tests. As a result of the DEM-MBD coupled simulation, the prediction accuracy of travel speed and draft force were 93.2% (86.6%- 99.4) and 90.8% (86.4%-99.3%), respectively, depending on the tillage depth. In addition, the prediction ac-curacy of the draft force was 11-32% higher than the 67.4-70.6% using the ASABE standard D497.4 method. This study is considered to be able to gradually replace the existing field tests with digital twin-based simulation tests. This can provide valuable insights for optimal design while minimizing the development time and cost of soil-machine systems.

Automated summary

In this study, a coupled DEM-MBD model was established to predict the draft force of a soil-machine system based on tillage depth. Field tests and calibration were conducted to validate the model, and the results showed high accuracy in predicting draft force compared to the traditional method.