Soybean plants cultivated using mulched drip irrigation planting technology have the following characteristics during the harvest period: green stems and leaves, and a high straw/grain ratio. Moreover, the threshing device of a soybean combine harvester is difficult to adapt to, resulting in an increase in the accumulation and unevenness of the threshed mixture. This leads to an increase in impurity content and the loss rate. We conducted a single-factor experiment on a self-developed longitudinal/axial-flow soybean threshing and separation test bench, employing drum speed, feeding rate, and threshing clearance as experimental factors. The influence of the soybean threshing and separation device’s working parameters on the distribution and uniformity of the threshed mixture in the axial and radial directions of the drum was explored through experiments. The results showed that the mass of the threshed mixture and soybean seeds showed a trend of first rapidly increasing and then slowly decreasing in the axial direction of the drum. Additionally, the mass showed a distribution feature of large values on both sides and small values in the middle in the radial direction. A lower drum speed, greater threshing clearance, and a smaller feeding rate make the radial distribution of a threshed mixture more uniform. Based on the combination of the crushing rate and unthreshed rate, the optimal working parameter combination was determined to be as follows: a drum speed of 500 r/min, a feeding rate of 6 kg/s, and a threshing clearance of 25 mm. The findings of this research offer valuable insights for the structural optimization and design enhancement of threshing and cleaning mechanisms within soybean combine harvesters.

Inadequate parameter design of the cleaning device in soybean combine harvesters leads to elevated levels of machine harvesting losses and impurity rates. To provide fundamental data for the optimization of structural parameters of soybean cleaning sieves, it is of great significance to study the collision and bouncing characteristics of soybeans on the cleaning sieve surface and the impact of parameters on the coefficient of restitution (COR). The current study designed a collision platform, using soybeans at the harvest stage as the research subject. The experimental factors included drop height, wall inclination angle, wall movement speed, and wall material. Through single-factor experiments and orthogonal experiments, the effects of different collision parameters on the rebound trajectory and COR of soybeans were investigated. This study focuses on soybeans at the harvest stage as the test subjects. Experiments were conducted on a collision platform and recorded with a high-speed camera to capture the three-dimensional motion trajectories of the soybeans using the principle of specular reflection. Through single-factor experiments, the jumping characteristics of the soybeans on sieve surfaces with different motion characteristics were analyzed. The impact of drop height (400–650 mm), wall inclination angle (8–13°), wall movement speed (0.6–1.1 m/s), and wall material (stainless steel plates and polyurethane plates) on the coefficient of restitution (COR) was calculated and clarified. Multi-factor orthogonal experiments were conducted to determine the significance order of the different factors affecting the COR. Three-dimensional models of the soybeans and the collision platform were constructed using SolidWorks software, and the collision between the soybeans and the cleaning wall was simulated using EDEM software. The micro-forces and energy transfer during the soybean collision were analyzed. The results indicated that the COR of soybeans decreases as the drop height increases, but increases with wall inclination angle and wall movement speed. Additionally, the COR is higher when the soybeans collide with stainless steel plates compared to polyurethane plates. The order of influence of the four factors on the COR were: wall material > wall inclination angle > wall speed > drop height. This study provides important reference value for the efficient and low-loss design of cleaning devices.

To address the issue of header loss in millet combine harvesters, a double-chain millet harvester header was designed based on the principles of contact mechanics and tribology. Key component parameters of the header were determined, with the divider length and width measuring 0.56 m and 0.30 m, respectively. The divider angle was 40°, and the spiral angle was also 40°. A prototype was manufactured, and field performance tests were conducted. The results showed that the total header loss rate of the double-chain header was lower than that of the single-chain header under various combinations of header height and harvesting speed. The lowest total header loss rate, 3.12%, occurred when the header height was 0.2 m and the harvesting speed was 1.667 m/s, with a grain loss rate of 0.55% and a spike loss rate of 2.57%. This research provides a theoretical foundation for the development of low-loss, high-efficiency millet combine harvesters.

Mulched drip irrigation is an important water-saving production system for soybean cultivation in arid and semi-arid regions. However, soybean materials harvested from this system exhibit specific physical characteristics, including relatively dry pods and seeds and high stem moisture content. This study characterized the physical and mechanical properties of soybean seeds at the harvest stage and established a discrete element method (DEM) parameter system for harvesting simulation. Key properties and contact behaviors were experimentally measured, and DEM parameters were calibrated and validated through physical tests and virtual simulations. The results showed that the calibrated parameters accurately reproduced the macroscopic stacking behavior of soybean seeds, with the angle of repose validation showing a relative error of only 0.545%. The proposed parameter system considers the geometric and mechanical characteristics of harvest-stage soybean seeds and improves the applicability of DEM simulations for harvesting process analysis. This study provides digital support for optimizing soybean harvesting machinery, reducing seed damage, and improving harvest quality in water-efficient production systems.