Structure design and rice threshing performance of the variable-speed inertial pulley for simulating artificial threshing

Due to the small models and compact structures,small harvesters have also caused problems such as poor threshing separation performance and large loss rates.In order to solve unstable cleaning effects of small harvesters when they facing different working conditions,this study selected rice plants in hilly areas for the experiment.Tensile breaking force of different parts of mature rice was tested,which revealed the fracture mechanism of each part under the combined force.Inertial threshing method was used to simulate artificial plate bin and design three kinds of non-circular pulley variable speed transmission threshing mechanism.With the help of transient inertia force,threshing force was compensated.This paper tested the harvesting performance of the variable speed threshing device with the help of the harvest performance test.Results show when facing the small rice planting area,the T/2 variable speed threshing device has better cleaning performance,and also the harvest loss rate of T/4 variable speed threshing device is the lowest.Compared with the constant speed threshing device,the impurity content rate of the variable speed threshing device is increased by 0.64%to 8.76%;the loss rate is reduced by 0.45%to 1.79%,which provides a basis for the optimization design of small combine harvester in hilly areas.

BP neural network model for material distribution prediction based on variable amplitude anti-blocking screening DEM simulations

The material feeding changing of combine harvester is easy to cause accumulation and blockage of the vibrating screen,which seriously affects the harvest operation.In order to alleviate such accumulation and blockages on the vibrating screen surface,the guide chute rotation angle of the improved variable amplitude screening mechanism was selected as the target variable,and EDEM-RecurDyn was employed to simulate the anti-blocking process of the variable amplitude under a changing feeding quantity(0.5 kg/s abnormal,0.2 kg/s normal)of materials(rice grain and stem mixture).A BP(an error back propagation algorithm)neural network was designed and the prediction model of the material distribution was subsequently constructed on the variable screening surface under different chute angles during abnormal feeding.The results revealed a continuous decrease in the quality and time of the material blockage at the front end of the screen surface with the increasing guide chute angle.At the guide chute angle of 20°-45°and adjustment time of 3-6 s,the blocked and accumulated materials at the front-end screen surface was be moved back to Grid 6 for screening.However,overtime,the screen surface materials continued to move back under the chute angle of 40°-45°,which had a great impact on the screening performance.At the guide chute angle of 30°-35°and adjustment time of 4 s,the materials on the screen surface were evenly distributed in Grid 1-6.This was able to alleviate the accumulation and blockage of the screen surface materials.The R of the material distribution prediction model(BP neural network)on the screen surface was determined as 0.97,indicating the high reliability and accuracy of the material distribution model on the screen surface based on the BP neural network.This work provides an important reference for the variable amplitude intelligent control of screen surface material anti-blocking.