酿酒葡萄低漏损鱼鳞式集果装置设计与试验

Design and Experiment of Low-loss Fish-scale Fruit Collection Device for Wine Grape Harvesting

摘要:

针对酿酒葡萄机械化采收过程中集果环节漏果率高、果实损伤严重等问题,设计了一种低漏损鱼鳞式接果输送装置。本研究建立了集果装置的动力学仿真模型,分析了其运动过程。在形状优化和单因素试验的基础上以漏果面积为评价指标,进行了弹簧劲度系数、对侧接果板重叠宽度、单侧接果板阵列间距和空白组的四因素三水平正交试验,结果表明前3个因素分别取0.7 N/mm、45 mm和100 mm时漏果面积最小,为1 036 mm2。建立了RecurDyn – EDEM耦合仿真模型,对多个品种进行了仿真集果试验,验证了方案的可行性。研制了集果装置样机,通过果粒落点分布标定试验得到风险漏果区的果粒分布比例φ小于15%。集果试验结果表明,装置对多个品种漏果率平均值小于4%,最大值为白玉霓品种(5.81%),最小值为熊岳白品种(3.19%)。漏果率与果粒直径整体呈负相关关系,且果粒破损越严重漏果率越高。仿真试验值与实际试验值的最大绝对误差为4.16%,证明模型具有较好的准确性。

Abstract:

Aiming at the problems of high fruit loss rate and severe berry damage during mechanized wine grape harvesting, a low-loss fish-scale fruit-catching and conveying device was designed. A dynamic simulation model of the fruit collection device was established to analyze its motion characteristics. Based on structural optimization and single-factor experiments, an orthogonal experiment with four factors and three levels was conducted with fruit loss area as the evaluation index. The results showed that the minimum fruit loss area was 1 036 mm2 when the spring stiffness coefficient, overlap width of opposite fruit-catching plates, and spacing of unilateral fruit-catching plate arrays were 0.7 N/mm, 45 mm, and 100 mm, respectively. A RecurDyn – EDEM coupled simulation model was established to perform simulated fruit collection tests for different grape varieties, and the feasibility of the device was verified. A prototype was developed, and the berry distribution ratio φ in the risk fruit-loss region was less than 15% based on berry falling-point distribution tests. The results of fruit collection tests showed that the average fruit loss rate of the device was less than 4% for different grape varieties, with the maximum value of 5.81% for ‘Baiyuni’ and the minimum value of 3.19% for ‘Xiongyuebai’. The fruit loss rate was negatively correlated with berry diameter, and increased with the aggravation of berry damage. The maximum absolute error between simulation and experimental results was 4.16%, indicating that the model had good accuracy. The research result can provide a theoretical basis and technical support for mechanized wine grape harvesting.