SIMULATION TEST FOR A PUSH-TYPE PRECISION METERING DEVICE BASED ON DISCRETE ELEMENT METHOD

SIMULATION TEST FOR A PUSH-TYPE PRECISION METERING DEVICE BASED ON DISCRETE ELEMENT METHOD

ABSTRACT To solve the problem of slow seed filling of soybean seeds under their own gravity, this study presents the design of a push-type precision metering device. Through a theoretical analysis, the key components and working principle of the push-type precision metering device were determined. T...

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Bibliographic Details
Main Authors: Lingyu Liu, Xiangcai Zhang, Xiupei Cheng, Zhongcai Wei, Xianliang Wang
Format: Article
Language:English
Published: Sociedade Brasileira de Engenharia Agrícola 2024-11-01
Series:Engenharia Agrícola
Subjects:
DEM
single-seed
mechanical mean
regression analysis
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162024000100339&lng=en&tlng=en
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Summary:ABSTRACT To solve the problem of slow seed filling of soybean seeds under their own gravity, this study presents the design of a push-type precision metering device. Through a theoretical analysis, the key components and working principle of the push-type precision metering device were determined. The operational process was divided into two stages: dropping of seeds, and pushing of seeds. The discrete element method and a regression analysis were used to optimize the working parameters during the seed dropping stage. The optimal combination was found by the response surface optimization method, and both the seed dropping module and the whole machine were tested on a test bench with this parameter combination. The results showed that the optimal solution was obtained with a seed stirring disk speed of 17.57 r min-1, 8.8 seed stirring rods (rounded to nine), and a central arc length proportion of 3.5% in the seed entry port. The predicted number of seeds dropped into the seed discharged cylinder (Y1) under this parameter combination was 678.96, with a coefficient of variation of seed dropping uniformity (Y2) of 6.33%. The experimental results gave values for Y1 of 691.37 seeds and Y2 of 6.93%, which were close to the predicted results.
ISSN:0100-6916

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