薏苡脱壳机关键部件作业参数优化  

Operating Parameter Optimization of Key Components of Coix Lacryma-jobi Sheller

作　　者：朱会霞[1] 李彤煜[1] 王吉权[2] 马巍 王辉暖 ZHU Huixia;LI Tongyu;WANG Jiquan;MA Wei;WANG Huinuan(College of Management,Liaoning University of Technology,Jinzhou Liaoning 121001,China;College of Engineering,Northeast Agriculture University,Harbin 150030,China;College of Animal Husbandry and Veterinary Medicine,Jinzhou Medical University,Jinzhou Liaoning 121001,China)

机构地区：[1]辽宁工业大学管理学院,辽宁锦州121001 [2]东北农业大学工程学院,哈尔滨50030 [3]锦州医科大学畜牧兽医学院,辽宁锦州121001

出　　处：《机械设计与研究》2019年第6期192-196,共5页Machine Design And Research

基　　金：国家肖然科学基金(31601914);辽宁省自然科学基金(2019-ZD-0804)资助项目;辽宁省教育厅高校基本科研(JQW201715407);辽宁省杜会科学规划基金(LI8DG1W8)资助项目。

摘　　要：针对回归分析方法优化薏苡脱壳机关键部件作业参数时拟合度精度和准确性不高的问题,提出了一种高精度的遗传神经网络优化方法。选取动盘转速、动静磨盘间隙、静盘工作面宽度三个关键作业参数为试验因素,以脱净率和破碎率为性能指标,训练遗传神经网络,结果表明拟合精度明显优于二次回归方法。脱净率由原设备的33.80%提升至50.20%,破碎率由原设备的9.70%下降至2.00%。薏苡脱壳机性能得到较好改善。该方法为薏苡脱壳机设备参数的智能化控制提供了一种较可靠的方法。Aiming at the problem that the accuracy and precision of the fitting are not the best by using the regression analysis method to optimize operating parameter of key components of coix lacryma-jobi sheller,a high precision genetic neural network optimization method is proposed.The three key operating parameters of rotation speed of rotary plate,distance between rotary and stationary plate,working face width of stationary plate are selected as experimental factors.The shelling rate and breakage rate are the performance indexes.After these were trained by genetic neural network,the results show that the fitting accuracy is significantly better than the quadratic regression method.The shelling rate increased from 33.80%to 50.20%,the breakage rate decreased from 9.70%to 2.00%.The performance of coix lacryma-jobi sheller is improved.This provides a more reliable method for intelligent control of equipment parameters of coix lacryma-jobi sheller.

关 键 词：薏苡脱壳机 作业参数优化 遗传神经网络 

分 类 号：S226[农业科学—农业机械化工程]