针对目前机采茶鲜叶分级设备存在的分级效率低、对鲜叶损伤大等问题。以振动式机采茶鲜叶分级机为试验对象,提出了一种茶鲜叶粘弹性物料建模方法,并以振动频率、振动方向角、振幅为自变量,以筛分率、优质茶筛分率为目标优化值,基于EDEM软件对其进行仿真试验,运用响应面优化法得到优化参数为振动频率29.0 Hz、振幅9.0 mm、振动方向角34.7°。在此参数下,仿真试验得到的筛分率为75.38%,优质茶筛分率为95.65%。根据最优参数组合开展仿真与样机验证试验,结果表明,样机验证试验得到的筛分率为71.07%,优质茶筛分率为93.26%。结合仿真试验和样机试验结果,筛分率的准确性达到93.9%,而优质茶筛分率的准确性达到97.4%。基于响应面分析法的仿真优化参数具有较高的可靠性,在此最优参数下,分级机具有较好的分级效果。本研究可以为机采茶鲜叶分级设备的优化提供参考。
Abstract
In view of the low grading efficiency and large damage to fresh leaves of the current machine-picked tea fresh leaf grading equipment, a vibrating machine-picking fresh tea leaf classifier was used in this study as the test object, and a new tea leaf viscoelastic material modeling method was proposed. The vibration frequency, vibration direction angle, and amplitude were used as independent variables. The sieving rate and famous tea sieving rate were used as target optimization values. The simulation test was carried out based on the EDEM software. Using the response surface optimization method, the optimized parameters were obtained as the vibration frequency of 29.0 Hz, the amplitude of 9.0 mm, and the vibration direction angle of 34.7°. Under this parameter, the sieving rate obtained by the simulation test was 75.38%, and the sieving rate of famous tea was 95.65%. According to the optimal parameter combination, the simulation and prototype verification test were carried out, and the results show that the sieving rate obtained by the prototype verification test was 71.07%, and the sieving rate of famous tea was 93.26%. Combining the simulation test and prototype test results, the accuracy of the screening rate reached 93.9%, and the accuracy of the screening rate of famous tea reached 97.4%. The simulation optimization parameters based on response surface analysis had high reliability. Under this optimal parameter, the classifier had a better classification effect. This study provided a reference for the optimization of machine-picked tea fresh leaf grading equipment.
关键词
机采茶鲜叶 /
鲜叶物料建模 /
响应面优化 /
振动分级机
Key words
fresh leaves material modeling /
machine-picked fresh tea leaves /
response surface optimization /
vibration classifier
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基金
财政部和农业农村部：国家现代农业产业技术体系（CARS-19）; 浙江省农业重大技术协同推广计划（2020XTTGCY02-03）
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