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Parameter Optimization Study of Adaptive Spiral Profiling Automatic Rubber Cutter for Natural Rubber Trees

文献类型： 外文期刊

作者： Zhang, Heng 1 ; Zhang, Guohai 2 ; Li, Yuan 3 ; Zhang, Lina 4 ;

作者机构： 1.Shandong Univ Technol, Inst Modern Agr Equipment, Zibo 255000, Peoples R China

2.Shandong Univ Technol, Coll Agr Engn & Food Sci, Zibo 255000, Peoples R China

3.Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Key Lab Appl Res Trop Crop Informat Technol Hainan, Haikou 571000, Peoples R China

4.Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Peoples R China

关键词： natural rubber tree; natural rubber extraction; automatic rubber cutting machine; improvement of cutting quality; forestry machinery; parameter optimization

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 16 卷 3 期

页码：

收录情况： SCI

摘要： Long-term tracking tests revealed that the adaptive spiral profiling automatic rubber cutter is prone to "knife off" and "knife jumping" states during the cutting process. These issues result in inconsistent skin thickness and poor cutting surface uniformity and can even damage natural rubber trees, leading to reduced yield, disease, and potentially death. This paper presents experimental research on the parameters affecting the performance of the adaptive spiral profiling automatic rubber cutter. It explores the impact of key parameters on cutter performance and establishes a mathematical model relating these parameters to the peel thickness and cutting surface uniformity rates. Through multi-objective parameter optimization, the optimal parameter combination was determined: a cutting speed of 15 s/knife, a blade thickness of 0.50 mm, and a bending angle of 27.5 degrees. In six tests, the average peel thickness and cutting surface uniformity rates were 97.5% and 96.5%, respectively. These results meet national standards, suggesting that the optimized parameters significantly improve the rubber cutter's performance. The results meet national standards, demonstrating that the optimized parameters improve the peel thickness and cutting surface uniformity. The regression model is reliable and provides a foundation for selecting better parameter combinations for future prototypes.

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