基于EDEM的茶鲜叶分级机的筛分率的研究

为了提高鲜叶分级机的筛分率,以柳叶种机采鲜叶为试验原料,对6CFJ-70型鲜叶分级机的关键技术参数进行了研究,利用Solidworks 2014对鲜叶分级机进行3D建模,基于离散元法建立鲜叶仿真颗粒模型和接触力学模型,设置关键仿真技术参数,运用EDEM 2018软件对鲜叶在锥形滚筒中的运动进行了数值模拟,得到影响筛分率的关键参数为锥形滚筒转速与倾角。为了对上述参数进行优化,以筛分率为目标函数,设计了2因素5水平的二次旋转正交组合试验,运用响应面法得到二次回归模型且通过了相关验证试验。结果表明,影响鲜叶分级效率的主要因素是锥形滚筒转速和锥形滚筒倾角。当锥形滚筒的转速为24 r·min^-1,锥形滚筒倾角为6°时,鲜叶的筛分率为81.7%,具有较好的鲜叶分级效果,本文的研究内容可以为鲜叶分级机的设计与优化提供技术参考。

Research on Screening Rate of Fresh Tea Leaves Classifier Based on EDEM

In order to improve the screening rate of fresh tea leaves classifier, the key technical parameters of 6CFJ-70 fresh tea leaves classifier were studied with machine picking fresh leaves of willow-Leaf tea plant. Solidworks 2014 was used to build the 3D model of fresh tea leaves classifier. Based on discrete element method, the simulation granular model and contact mechanics model of fresh tea leaves were established, and the key simulation technical parameters were set up. EDEM 2018 software was used to simulate the conical drum of fresh tea leaves. The movement of the conical drum was simulated numerically, and the key parameters affecting the screening rate were the rotational speed and inclination angle of the conical drum. In order to optimize the above parameters, a quadratic rotation orthogonal combination experiment with 2 factors and 5 levels was designed with screening rate as objective function. The quadratic regression model was obtained by response surface method and the related validation tests were carried out. The results show that the main factors affecting the classification efficiency of fresh leaves are the rotational speed of conical drum and the inclination of conical drum in turn. When the rotating speed of conical drum is 24 r·min^-1 and the inclination angle of conical drum is 6 degrees, the screening rate of fresh leaves is 81.7%, which has a good effect of fresh leaves classification. The research content of this paper can provide technical reference for the design and optimization of fresh tea leaves classifier.