柔性定量包装系统的开发与研究

发布时间：2018-04-16 18:48

  本文选题：柔性 + 定量包装　； 参考：《西南科技大学》2016年硕士论文

【摘要】：我国的包装机械技术与世界先进水平存在较大差距,主要体现在自动化程度低、产品可靠性差、精度较低和技术更新慢等方面。随着信息技术与现代制造技术的快速发展,定量包装机械进入了一个新的发展时期,柔性与包装精度成为了许多学者和公司研究的热点。依据用户提出的要求,开发了柔性定量包装系统的实验样机,致力于实现自动化包装和高精度称重。运用模块化思想将系统分为了袋库机构、送袋机构、张袋机构、张紧机构、称重取袋机构和螺旋喂料机构六个部分,完成了结构设计和运动原理分析;以PLC和工业机器人为控制器,完成了控制系统硬件组态,设计了真空吸盘回路、气缸动作回路、各机械模块的控制策略和上位机监控软件;安排了三因素五水平的正交试验,探讨了落差高度、螺旋转速和物料的堆积密度对称重精度的影响规律;以正交试验数据为基础,分别通过线性回归分析和RBF神经网络,建立了称重误差的预测模型,分析了两种预测模型的拟合误差。利用三种敞口袋进行了系统可靠性实验,结果表明:工业机器人的引入使系统具有极强的柔性,可适应不同的包装环境;牛皮纸袋和纸塑复合袋等硬袋子的包装成功率大于98%,软袋子的包装成功率稍低且会产生局部变形,是一个需要改进的问题。同时采用三种物料进行了称重实验,结果表明:两种预测模型均可有效地减小称重误差,且RBF神经网络模型的精度更高,为高精度包装提供了一种离线误差补偿方法。
[Abstract]:There is a big gap between China's packaging machinery technology and the advanced level of the world, which is mainly reflected in the low degree of automation, poor product reliability, low precision and slow technological renewal.With the rapid development of information technology and modern manufacturing technology, quantitative packaging machinery has entered a new period of development, flexibility and packaging accuracy has become the focus of many scholars and companies.An experimental prototype of flexible quantitative packaging system is developed to realize automatic packaging and high precision weighing according to the requirements of users.The system is divided into six parts: bag storehouse mechanism, bag feeding mechanism, bag tensioning mechanism, tensioning mechanism, weighing bag mechanism and spiral feeding mechanism. The structure design and motion principle analysis are completed.Taking PLC and industrial robot as controller, the hardware configuration of the control system is completed, the vacuum sucker circuit, cylinder action loop, control strategy of each mechanical module and upper computer monitoring software are designed, and three factors and five levels of orthogonal test are arranged.The influence of drop height, helical speed and bulk density on symmetrical weight accuracy is discussed. Based on orthogonal test data, the prediction model of weighing error is established by linear regression analysis and RBF neural network, respectively.The fitting error of two prediction models is analyzed.The system reliability experiments are carried out with three kinds of open bags. The results show that the introduction of industrial robot makes the system very flexible and can adapt to different packaging environments.The successful rate of packaging for hard bags such as Kraft paper bags and paper plastic composite bags is more than 98. The success rate of soft bags is slightly lower and will produce partial deformation which is a problem that needs improvement.At the same time, three kinds of materials are used to carry out weighing experiments. The results show that the two prediction models can effectively reduce the weighing error, and the RBF neural network model has higher accuracy, which provides an off-line error compensation method for high-precision packaging.
【学位授予单位】：西南科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TB486.3
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本文编号：1760157