Numerical analysis and parameter optimization of soil-loosening plow compatible with the shovel-type seedbed preparation machine

IF 8.9 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-09-11 DOI:10.1016/j.compag.2025.110981

Jianxin Lin , Yan Kang , Qingxi Liao , Wenbin Du , Lin Li , Qingsong Zhang

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引用次数: 0

Abstract

The soil-loosening plow (SLP) was mounted in front of the shovel-type seedbed preparation machine (SSPM) to form a combined tillage machine (CTM), which is used for rapeseed seedbed preparation in rice-rapeseed rotation regions. A discrete element method-multibody dynamics (DEM-MBD) simulation model was developed in this manuscript, and the model’s accuracy was validated by comparing the power take-off torque (PT), draft force (DF), and clod crushing rate (CCR) of the SSPM during the simulation and experiment processes. Based on the validated model, the CTM was used as the simulation test implement. Tillage depth, wing installation height, and wing installation angle of the SLP were chosen as simulation factors. Simulation tests were conducted using the CCR and power consumption as evaluation indices. Single-factor experiment results showed that the DF power and total power consumption of the CTM increased progressively with greater tillage depth of the SLP, whereas the PT power consumption exhibited an opposite trend. As the wing installation height increased, the DF power consumption of CTM decreased, while the PT power consumption increased. With increasing wing installation angle, the DF power consumption of CTM first decreased and then increased. With the objective function of minimizing PT power consumption and total power consumption, the optimal parameters were determined to be a tillage depth of 160 mm, a wing installation height of 22 mm, and a wing installation angle of 5°. A comparative simulation was then performed between the CTM configured with these optimal parameters and the SSPM without installing the SLP. The simulation results indicated that, in comparison with the SSPM, the CTM increased the number of broken soil bonds by 10.50 %, the average tillage resistance on shovel and PT were reduced by 35.26 % and 31.06 %, respectively. These results indicated that the optimized SLP effectively lowered the PT requirement and tillage resistance on shovel during CTM operation. The experiments conducted on fields following rice harvest confirmed these simulation results. After CTM operation, the tillage depth stability coefficient, straw burial rate, and CCR reached 82.39 %, 86.69 %, and 83.54 %, respectively—improvements of 13.78 %, 10.17 %, and 7.89 % over the SSPM.

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与铲式整地机配套的松土犁的数值分析及参数优化

将松土犁（SLP）安装在铲式整地机（SSPM）前，组成联合耕作机（CTM），用于水稻-油菜轮作地区的油菜整地。建立了离散元法-多体动力学（DEM-MBD）仿真模型，并通过仿真和实验过程中SSPM的动力输出扭矩（PT）、气流力（DF）和碎泥率（CCR）的比较，验证了模型的准确性。在验证模型的基础上，采用CTM作为仿真测试工具。选取SLP的耕作深度、机翼安装高度和机翼安装角度作为模拟因素。以CCR和功耗为评价指标，进行了仿真试验。单因素试验结果表明，随着SLP耕作深度的增加，CTM的DF功率和总功耗逐渐增加，而PT功耗则相反。随着机翼安装高度的增加，CTM的DF功耗降低，而PT功耗增加。随着机翼安装角度的增大，CTM的DF功率消耗先减小后增大。以PT功率消耗和总功率消耗最小为目标函数，确定了最优参数为耕作深度160 mm，机翼安装高度22 mm，机翼安装角度5°。然后在配置了这些最优参数的CTM与未安装SLP的SSPM之间进行了比较模拟。模拟结果表明，与SSPM相比，CTM使土壤黏结断裂数增加了10.50%，铲和PT的平均耕作阻力分别降低了35.26%和31.06%。结果表明，优化后的SLP有效降低了CTM作业中对PT的要求和对铲的耕作阻力。水稻收获后在田间进行的试验证实了这些模拟结果。CTM操作后，耕深稳定系数、秸秆掩埋率和CCR分别达到82.39%、86.69%和83.54%，比SSPM分别提高了13.78%、10.17%和7.89%。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.