Authors

Abstract

To enhance the operational efficiency and structural design accuracy of rock-picking machinery, this study proposes a Discrete Element Method (DEM)-based approach for the optimization of the lifting shovel in the 1JS-200 rock picker. A coupling simulation model of the shovel-soil-rock system was established to analyze the dynamic interaction mechanisms between the shovel and mixed granular media. Key parameters influencing excavation performance-including forward speed, digging depth, and shovel angle-were optimized using a Box-Behnken Design (BBD) response surface methodology. Regression models were constructed for torque and rock excavation efficiency (REE), and the optimal combination was determined to be a forward speed of 0.5 m/s, a digging depth of 170 mm, and a shovel angle of 40°, under which the predicted REE reached 88.1% with a torque of approximately 386 N·m. To validate the simulation results, field tests were conducted under optimal parameter conditions. The experimental results showed that REE values fluctuated within 2%, and torque errors remained within 4% of the predicted values, confirming the accuracy and applicability of the model. This research provides a practical, data-driven design method for rock-picking implements and offers theoretical and technical support for improving rock separation efficiency, reducing structural wear, and advancing the intelligent development of agricultural machinery.

Abstract in Chinese

为提升农田捡石机作业效率与结构设计的科学性，本文以1JS-200型捡石机的起石铲为对象，提出了一种基于离散元法（DEM）的结构优化方法。构建了起石铲–土壤–砾石系统的动力学模型，模拟分析了提升铲与混合颗粒介质之间的动态相互作用机制。在此基础上，采用Box–Behnken响应面设计方法，对作业速度、挖掘深度及铲角等关键参数进行优化，构建了扭矩与岩石清理效率（REE）的二次回归模型。结果表明，最优参数组合为作业速度0.5 m/s、挖掘深度170 mm、铲角40°，在此条件下REE为88.1%，扭矩约为386 N·m。为验证模拟结果的可靠性，开展了田间验证试验。试验结果显示，实测REE波动范围控制在2%，扭矩误差控制在4%以内，验证了模型的准确性与适用性。研究成果为捡石作业设备的数据驱动式结构优化提供了实用路径，同时为提升捡石效率、降低设备磨损、推动农机装备智能化发展提供了理论依据与技术支撑。