Optimization of Sawing Parameters for Apple Tree Branches and Study on the Influence of Support System Based on Explicit Dynamics and Response Surface Methodology

In the mechanized pruning process of apple trees, reasonably matching cutting parameters is the key to reducing energy consumption and improving pruning quality. The conventional empirical parameter configuration usually ignores the vibration suppression effect of the branch support system, resulting in unstable cutting processes and poor cross-section quality. This study systematically investigated the influences of saw blade rotational speed, feed speed, and active support system on the sawing process of apple branches, aiming to obtain optimal operating parameters through a closed-loop research method of “simulation, optimization, and verification”. An explicit dynamic finite element model was established for multi-branch staggered sawing with three saw blades. The influence trends of each factor were analyzed via single-factor tests. A three-factor, three-level orthogonal experiment was designed based on the Box–Behnken method, and a response surface prediction model of sawing force was constructed. Regression analysis showed that the established model was extremely significant (p < 0.01). The order of factors affecting sawing force from primary to secondary was as follows: feed speed > number of support components > saw blade rotational speed. Multi-objective optimization yielded the optimal parameter combination: rotational speed of 2500 r/min, feed speed of 2 km/h, and five support components. A prototype was manufactured according to these parameters, and field verification tests were carried out in orchards. Taking the qualified rate of cross-section quality and the missed-cut rate as evaluation indexes, the qualified rate under optimized parameters reached 95.07%, which was significantly higher than 83.11% under traditional parameters, and the missed-cut rate decreased from 11.27% to 2.63%. Results indicate that the collaborative optimization mode of “medium-high rotational speed, moderate feed speed, and active support” enables the low-vibration and high-quality sawing of apple branches. The combined method of explicit dynamics, response surface methodology, and field verification provides a systematic solution for intelligent parameter configuration of orchard pruning equipment.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2026-04-14
DOI
https://doi.org/10.3390/agriculture16080863
Primary Topic
Tree Root and Stability Studies
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 Sawing Parameters for Apple Tree Branches and Study on the Influence of Support System Based on Explicit Dynamics and Response Surface Methodology

L. F. Liu, Xin Yang, Jianping Li, Hongjie Liu et al.
Agriculture
Tree Root and Stability Studies
article

Optimization of Sawing Parameters for Apple Tree Branches and Study on the Influence of Support System Based on Explicit Dynamics and Response Surface Methodology

L. F. Liu, Xin Yang, Jianping Li, Hongjie Liu, Yingjie Shi, Pengfei Wang
article en

Abstract

In the mechanized pruning process of apple trees, reasonably matching cutting parameters is the key to reducing energy consumption and improving pruning quality. The conventional empirical parameter configuration usually ignores the vibration suppression effect of the branch support system, resulting in unstable cutting processes and poor cross-section quality. This study systematically investigated the influences of saw blade rotational speed, feed speed, and active support system on the sawing process of apple branches, aiming to obtain optimal operating parameters through a closed-loop research method of “simulation, optimization, and verification”. An explicit dynamic finite element model was established for multi-branch staggered sawing with three saw blades. The influence trends of each factor were analyzed via single-factor tests. A three-factor, three-level orthogonal experiment was designed based on the Box–Behnken method, and a response surface prediction model of sawing force was constructed. Regression analysis showed that the established model was extremely significant (p < 0.01). The order of factors affecting sawing force from primary to secondary was as follows: feed speed > number of support components > saw blade rotational speed. Multi-objective optimization yielded the optimal parameter combination: rotational speed of 2500 r/min, feed speed of 2 km/h, and five support components. A prototype was manufactured according to these parameters, and field verification tests were carried out in orchards. Taking the qualified rate of cross-section quality and the missed-cut rate as evaluation indexes, the qualified rate under optimized parameters reached 95.07%, which was significantly higher than 83.11% under traditional parameters, and the missed-cut rate decreased from 11.27% to 2.63%. Results indicate that the collaborative optimization mode of “medium-high rotational speed, moderate feed speed, and active support” enables the low-vibration and high-quality sawing of apple branches. The combined method of explicit dynamics, response surface methodology, and field verification provides a systematic solution for intelligent parameter configuration of orchard pruning equipment.

AgricultureVol. 16(8)
Hebei Agricultural University (CN)
Modern Agricultural Technology Industry System of Shandong province
Affordable and clean energy
Openalex Percentile: Top 26%
Tree Root and Stability Studies
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.