Abstract: 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.

Key words: fresh tea leaves classifier, discrete element method, EDEM, response surface analysis