摆杆驱动式残膜回收机的设计与参数优化

Design and parameter optimization of swing rod driven residual plastic film collector

针对现有的杆齿式残膜回收机存在拾膜弹齿轴易卡顿、卸膜不可靠等问题,改进设计了摆杆驱动式残膜回收机。在原杆齿式残膜回收机的基础之上增加了起膜装置,改变了拾膜齿轴与支撑盘的连接方式,利用四杆机构将卸膜机构的回转运动转变为摆动往复运动。为确定机具作业时的最优参数组合,优化整机结构,以拾膜齿入土深度、土槽台车前进速度、拾膜齿线速度与土槽台车前进速度比(速比)为主要因素,拾膜率、卸膜率为评价指标,对拾膜机构和卸膜机构进行三因素三水平响应面试验。通过Design-Expert数据分析软件,建立各因素与拾膜率、卸膜率的二次回归模型,分析了各因素对拾膜率、卸膜率的显著性,结果表明各因素影响拾膜、卸膜率的大小顺序为:土槽台车前进速度、拾膜齿线速度与土槽台车前进速度比、拾膜齿入土深度。并对试验参数进行优化,确定了最佳工作参数组合为拾膜齿入土深度为65 mm,土槽台车前进速度为1.2 m/s,速比为1.0。根据优化结果进行验证试验,结果表明拾膜率为85.6%,卸膜率为86.7%,预测模型与试验结果相差较小,优化后的模型可靠。

Although plastic sheet-covered cultivation technology has brought great development to agricultural production, the plastic film residual in the field has also seriously polluted the soil. Because plastic film is difficult to degrade under natural conditions, its residue in the soil will not only hinder the growth of crop roots, and make it difficult for crops to absorb water, but also prevent the infiltration of water in the soil, making the soil moisture content decline, and ultimately leading to crop yield reduction. The traditional pole-tooth residual plastic film collector mainly uses elastic tooth to recover the surface plastic film residue. The structure of the machine is simple, but it is difficult to unload the plastic film, and the working parts are easy to be wrapped by the film, which ultimately leads to the low recovery rate of the plastic film residue. According to the typical mulching planting situation in Xinjiang cotton field and the structural characteristics of the pole-tooth residual plastic film collector, the swing rod driven residual plastic film collector was improved and designed. In particular, the technology improvement of the spring-tooth picking device and the scraper discharging device was carried out, which solved the problems of easy jamming of the elastic tooth shaft and unreliable film unloading. Compared with the traditional pole-tooth residual plastic film collector, the swing rod driven residual plastic film collector mainly improved in the following aspects: the lifting film device was increased, and the effect of picking-up film was improved;the connection mode between the picking-up tooth shaft and the supporting plate was changed, and the operation of the picking-up residual plastic film mechanism was improved;four-bar linkage was used to change the gyratory motion of unloading film mechanism into swing reciprocating motion, so as to reduce residual film winding. In order to verify the performance of the mechanism, determine the optimal combination of parameters, and optimize the structure of the whole machine, the picking-up film mechanism and the unloading film mechanism were tested. Taking the penetration depth of the picking-up teeth, the forward speed of the trolley, the linear speed of the picking-up teeth and the forward speed ratio (the ratio of linear speed of the picking-up tooth to forward speed of soil bin trolley) of the trolley as the main factors, and the picking-up rate and the unloading rate as the testing indexes, the three-factor and three-level response tests of the picking-up mechanism and the unloading mechanism were carried out. The quadratic regression model between each factor and the picking-up film rate and unloading film rate were established by the software of Design-Expert. The significant influence of each factor on the picking-up film rate and unloading film rate were analyzed, and the test parameters were optimized. The results showed that the order of influencing factors on the picking-up film rate and the unloading film rate was as follows: the forward speed of soil bin trolley, the speed ratio, and the depth of picking-up teeth into soil. The optimum working parameters were determined as follows: the penetration depth of the picking teeth of 65 mm, 1.2 m/s of the forward speed of the soil bin trolley, and the speed ratio of 1.0. According to the optimization results, the validation test was carried out. The result showed that the picking-up film rate was 85.6%, the unloading film rate was 86.7%, which were less different from the prediction model, and the optimized model was reliable.