Optimization of Cutting Parameters for Cotton Stalks Using Reciprocating Bionic Cutters Based on Finite Element Simulation and Experiment

Regarding the current issues in Xinjiang, China, during the harvesting of cotton stalks, the lack of specialized, efficient, and durable cutting blades for cotton stalks causes uneven cutting, high power consumption, and short blade life. In this study, a biomimetic serrated blade was designed based on the Trictenotomidae mandible for efficient, low-power-consumption cutting. The biomimetic design, FEM-SPH coupled simulation, bench test, combined with response surface methodology, and field test were used. The simulation results showed that under the same working conditions, the maximum shear stress was 34.81% lower than that for the ordinary blade and 22.05% lower than that for the ordinary serrated blade. And the bench test results showed that cutting power consumption was reduced by about 20.12% and 15.69% compared to the ordinary cutting blade and serrated cutting blade, respectively. When cutting velocity was 1.3 m/s, cutting inclination angle was 11°, and ratio of cutting velocity and feeding velocity was 1.1, the biomimetic serrated cutting blade could achieve effective cutting of cotton stalks and obtain better quality of cutting—the cutting power per unit area and the cutting-edge angle after cutting cotton stalks were 52.08 kJ/m2 and 6°, respectively. The research results can provide a theoretical basis and support for the utilization of cotton stalks out of the field, as well as the cutting of other similar crop stalks.

Authors

Institutions

Publication Details

Journal
AgriEngineering
Published
2026-04-27
DOI
https://doi.org/10.3390/agriengineering8050164
Primary Topic
Agricultural Engineering and Mechanization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Cutting Parameters for Cotton Stalks Using Reciprocating Bionic Cutters Based on Finite Element Simulation and Experiment

Jiahong Tang, Silin Cao, Yi Yang, Jianhua Xie et al.
AgriEngineering
Agricultural Engineering and Mechanization
article

Optimization of Cutting Parameters for Cotton Stalks Using Reciprocating Bionic Cutters Based on Finite Element Simulation and Experiment

Jiahong Tang, Silin Cao, Yi Yang, Jianhua Xie, Weirong Huang
article en

Abstract

Regarding the current issues in Xinjiang, China, during the harvesting of cotton stalks, the lack of specialized, efficient, and durable cutting blades for cotton stalks causes uneven cutting, high power consumption, and short blade life. In this study, a biomimetic serrated blade was designed based on the Trictenotomidae mandible for efficient, low-power-consumption cutting. The biomimetic design, FEM-SPH coupled simulation, bench test, combined with response surface methodology, and field test were used. The simulation results showed that under the same working conditions, the maximum shear stress was 34.81% lower than that for the ordinary blade and 22.05% lower than that for the ordinary serrated blade. And the bench test results showed that cutting power consumption was reduced by about 20.12% and 15.69% compared to the ordinary cutting blade and serrated cutting blade, respectively. When cutting velocity was 1.3 m/s, cutting inclination angle was 11°, and ratio of cutting velocity and feeding velocity was 1.1, the biomimetic serrated cutting blade could achieve effective cutting of cotton stalks and obtain better quality of cutting—the cutting power per unit area and the cutting-edge angle after cutting cotton stalks were 52.08 kJ/m2 and 6°, respectively. The research results can provide a theoretical basis and support for the utilization of cotton stalks out of the field, as well as the cutting of other similar crop stalks.

AgriEngineeringVol. 8(5)
Xinjiang Agricultural University (CN), Xinjiang Academy of Agricultural and Reclamation Science (CN)
National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 8%
Agricultural Engineering and Mechanization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.