机制(生物学)
弹簧(装置)
卷轴
交货地点
还原(数学)
农学
农业工程
生物
工程类
数学
机械工程
物理
几何学
量子力学
作者
Yuxuan Chen,Shiguo Wang,Bin Li,Yang Liu,Zhong Tang,Xiaoying He,Jianpeng Jing,Weiwei Zhou
出处
期刊:Agriculture
[Multidisciplinary Digital Publishing Institute]
日期:2025-06-27
卷期号:15 (13): 1378-1378
标识
DOI:10.3390/agriculture15131378
摘要
As a vital oil and cereal crop in China, soybean requires efficient and low-loss harvesting to ensure food security and sustainable agricultural development. However, pod-shattering losses during soybean harvesting in Xinjiang remain severe due to low pod moisture content and poor mechanical strength, while existing studies lack a systematic analysis of the interaction mechanism between reeling devices and pods. The current research on soybean harvester headers predominantly focuses on conventional rigid designs, with limited exploration of flexible reel mechanisms and their biomechanical interactions with soybean pods. To address this, this study proposes an optimization method for low-loss harvesting technology based on mechanical-crop interaction mechanisms, integrating dynamic simulation, contact mechanics theory, and field experiments. Texture analyzer tests revealed pod-shattering force characteristics under different compression directions, showing that vertical compression exhibited the highest shattering risk with an average force of 14.3271 N. A collision model between the spring tooth and pods was established based on Hertz contact theory, demonstrating that reducing the elastic modulus of the spring tooth and increasing the contact area significantly minimized mechanical damage. Simulation verified that the PVC-nylon spring tooth reduced the maximum equivalent stress on pods by 90.3%. Furthermore, the trajectory analysis of spring-tooth tips indicated that effective pod-reeling requires a reel speed ratio (Δ) exceeding 1.0. Field tests with a square flexible spring tooth showed that the optimized reel reduced header loss to 1.371%, a significant improvement over conventional rigid teeth. This study provides theoretical and technical foundations for developing low-loss soybean harvesting equipment. Future work should explore multi-parameter collaborative optimization to enhance adaptability in complex field conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI