The Development of a Draft Force Prediction Model for Agricultural Tractors Based on the Discrete Element Method in Loam and Clay Loam

Abstract

In the field of agricultural machinery, various empirical field tests are conducted to measure design loads for the optimal design and implementation of tractors. However, conducting field tests is costly and time-consuming, with many constraints on weather and field soil conditions, and research utilizing simulations has been proposed as an alternative to overcome these shortcomings. The objective of this study is to develop a DEM-based draft force prediction model that reflects differences in soil properties. For this, soil property measurements were conducted in two fields (Field A in Daejeon, Republic of Korea, and Field B in Chuncheon, Republic of Korea). The measured properties were used as parameters for DEM-based particle modeling. For the interparticle contact model, the EEPA contact model was used to reflect the compressibility and stickiness of cohesive soils. To generate an environment similar to real soil, particle mass and surface energy were calibrated based on bulk density and shear torque. The soil property measurements showed that Field B had a higher shear strength and lower cone index and moisture content compared to Field A. The actual measured draft force was 19.47% higher in Field B than in Field A. In this study, this demonstrates the uncertainty in predicting draft force by correlating only one soil property and suggests the need for a comprehensive consideration of soil properties. The simulation results of the tillage operation demonstrated the accuracy of the predicted shedding force compared to the actual field experiment and the existing theoretical calculation method (ASABE D497.4). Compared to the measured draft force in the actual field test, the predictions were 86.75% accurate in Field A and 74.51% accurate in Field B, which is 84% more accurate in Field A and 37.32% more accurate in Field B than the theoretical calculation method. This result shows that load prediction should reflect the soil properties of the working environment, and is expected to be used as an indicator of soil–tool interaction for digital twin modeling processes in the research field of bio-industrial machinery.