| 不同土壤对甘蔗入土切割负载压力影响的研究 |
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| 引用本文： | 麻芳兰,李科,罗晓虎,滕筱,莫德庆.不同土壤对甘蔗入土切割负载压力影响的研究[J].农机化研究,2022,44(1):165-173. |
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| 作者姓名： | 麻芳兰 李科 罗晓虎 滕筱 莫德庆 |
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| 作者单位： | 广西大学 机械工程学院, 南宁 530004 |
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| 基金项目： | 广西科技开发重点项目(桂科AB18281016);亚热带农业生物资源保护与利用国家重点实验室开放项目(SKLCUSA-b201706)。 |
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| 摘 要： | 针对甘蔗收获机入土切割系统负载压力的预测适应性差、准确性低的问题,通过正交试验探究在不同土壤类型下切割系统的负载压力与入土切割深度、土壤含水率、甘蔗密度及土壤硬度等因素之间的关系并对各影响因素的显著性进行排序;根据试验结果搭建基于BP神经网络的负载切割压力的预测模型并进行验证。试验及验证结果表明:各土壤中入土深度、土壤含水率、甘蔗密度对切割系统负载压力影响显著,红壤的土壤硬度影响显著,而冲积壤的入土深度与土壤含水率交互作用影响较大;预测验证得出黄壤、红壤、冲击壤的平均相对误差分别为1.81%、3.46%、3.79%。研究成果可为提高甘蔗收获机入土切割负载压力预测控制系统的适应性、可靠性提供数据支持和理论依据,对其实际应用具有一定参考价值。
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| 关 键 词： | 甘蔗收获机 负载压力 土壤类型 影响因素 BP神经网络 |
Study of Different Soils on the Load Pressure of Sugarcane Cutting |
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| Ma Fanglan,Li Ke,Luo Xiaohu,Teng Xiao,Mo Deqing.Study of Different Soils on the Load Pressure of Sugarcane Cutting[J].Journal of Agricultural Mechanization Research,2022,44(1):165-173. |
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| Authors: | Ma Fanglan Li Ke Luo Xiaohu Teng Xiao Mo Deqing |
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| Institution: | (College of Mechanical Engineering,Guangxi University,Nanning 530004,China) |
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| Abstract: | Aimed at the problems of poor adaptability and low accuracy in predicting the load pressure of the soil cutting system of sugarcane harvester,this paper explore the relationship between the load pressure of the cutting system under different soil types with the factors and rank the significance of each influencing factor such as cutting depth,soil moisture content,sugarcane density and soil hardness through orthogonal experiments.According to the test results,the prediction model of load cutting pressure based on BP neural network is built and verified.The results of experiment and verification showed that the soil depth,soil moisture content and sugarcane density in each soil had a significant influence on the load pressure of the cutting system,and the soil hardness of red soil had a significant influence,while the soil depth of alluvial soil had a significant influence on the interaction of soil moisture content.The results of forecast verification showed that the average relative errors of yellow soil,red soil and alluvial soil were 1.81%,3.46%and 3.79%respectively.This study provides data support and theoretical basis for improving the adaptability and reliability of the load pressure prediction and control system of sugarcane harvester for soil cutting,and has certain reference significance for its practical application. |
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| Keywords: | sugarcane harvesting machinee load pressure type of soil influence factors BP neural network |
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