Research on Performance of Cutting Bamboo Branches with Blade Based on BP Neural Network
In order to investigate the factors affecting the cutting performance of the blade when cutting bamboo bran-ches,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 meas-ure,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 sev-eral 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.
bamboo cuttingtestblade cutting performanceBP neural networknumber of hidden layer nodes
NETL
NSTL
万方数据
维普
