【摘要】 为探究竹枝切割时的刀片切割性能影响因素，支持后续打枝装置的设计，开展竹枝切割刀片性能研究试验，通过单因素试验研究，利用切割阻力作为衡量标准，探究刀片切割性能与关键参数(刀片的滑动角、楔角和滑动速度)之间的相互关系。试验结果显示，随着刀片滑动角和楔角的减小，刀片切割性能呈现明显改善。同时，随着刀片滑动速度的增加，切割性能也呈现相应提升趋势。在多组实验中，采用不同的刀片滑切角度、楔角和滑切速度参数，对不同直径尺寸的竹枝进行切割，并收集了切割阻力的数据构成数据集，构建一个3层BP神经网络模型，研究了刀片切割性能与滑切角度、楔角以及滑切速度之间的关联，并应用相关模型进行了拟合和预测。在BP神经网络中，当隐含层节点数设定为9时，成功建立了刀片切割阻力模型，精准地预测了刀片切割过程中的阻力变化，对刀片切割竹枝性能研究具有一定参考价值。更多还原

【Abstract】 In order to investigate the factors affecting the cutting performance of the blade when cutting bamboo branches, and to support the design of subsequent pruning devices, the study carried out a bamboo branch cutting blade performance research experiments, through a one-way experimental study, the use of cutting resistance as a measure, to explore the interrelationships between the cutting performance of the blade and the key parameters(sliding angle, wedge angle and sliding speed of the blade). The experimental results show that the blade cutting performance shows a significant improvement with the reduction of blade sliding angle and wedge angle, and at the same time, the cutting performance also shows a corresponding trend of improvement with the increase of blade sliding speed. In several sets of experiments, different blade sliding angles, wedge angles and sliding speeds were used to cut bamboo branches with different diameter sizes, and the data on cutting resistance were collected to form a dataset. A three-layer BP neural network model was constructed to investigate the association between blade cutting performance and sliding angles, wedge angles and sliding speeds, and the relevant model was applied for fitting and prediction. In the BP neural network, when the number of nodes in the hidden layer was set to 9, the blade cutting resistance model was successfully established, which accurately predicted the change of resistance during the blade cutting process,and has certain reference value for the study of blade cutting performance of bamboo branches.更多还原