2021
DOI: 10.3390/app112412158
|View full text |Cite
|
Sign up to set email alerts
|

Development and Evaluation of Low-Damage Maize Snapping Mechanism Based on Deformation Energy Conversion

Abstract: Reducing ear damage is the key to improving the quality of maize harvests. In order to reduce the impact and damage of the ear caused by the ear snapping mechanism, this paper proposes a method to convert ear deformation energy during collision into elastic potential energy in the ear snapping mechanism. According to the above method, a low-damage maize snapping mechanism was designed. In order to verify the feasibility of energy conversion in reducing damage, the dynamic model of the contact between the ear a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
2

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 22 publications
(22 reference statements)
0
1
0
Order By: Relevance
“…The seed removal and cob removal models were established to determine the best combination of parameters for corn picking and harvesting. Zhang et al designed a low-injury corn picking mechanism and verified through experiments that the primary and secondary order of influencing the damage rate of the cob was the picking roller speed, spring stiffness, and forward speed [3]. Monhollen, NS developed a corn grain loss assessment system comprised of a machine vision image system to evaluate cutter seed loss and harvest loss [4].…”
Section: Introductionmentioning
confidence: 99%
“…The seed removal and cob removal models were established to determine the best combination of parameters for corn picking and harvesting. Zhang et al designed a low-injury corn picking mechanism and verified through experiments that the primary and secondary order of influencing the damage rate of the cob was the picking roller speed, spring stiffness, and forward speed [3]. Monhollen, NS developed a corn grain loss assessment system comprised of a machine vision image system to evaluate cutter seed loss and harvest loss [4].…”
Section: Introductionmentioning
confidence: 99%