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包装生产线智能灌装系统设计
引用本文:胡应坤,蓝茂焜.包装生产线智能灌装系统设计[J].包装工程,2021,42(23):214-218.
作者姓名:胡应坤  蓝茂焜
作者单位:广东工贸职业技术学院,广州 510510
基金项目:广东省教育科学“十三五”规划(高等教育科学研究专项)(2020GXJK526)
摘    要:目的 为提高灌装生产线的灌装精度和速度,设计一种包装生产线智能灌装控制系统.方法 阐述灌装生产线基本结构,包括输送、空瓶检测、灌装、质量检测、二次补灌、套盖以及成品输出等工位.以二次补灌为重点研究对象,设计一种基于小脑神经网络PID控制的微量灌装控制系统,可弥补常规PID控制的不足,同时采用卡尔曼滤波器消除操作过程干扰信号和噪声.通过实验验证所述控制系统的可行性和有效性.结果 分析了灌装特性,认为影响灌装精度的因素比较多且相互干扰.实验结果表明,所有样本的质量偏差均没有超过0.1 g,灌装过程中生产线运行稳定、快速、可靠.结论 所述微量灌装控制系统能够提高灌装精度、速度,可满足包装生产线高精度、高速度的要求.

关 键 词:灌装  小脑神经网络  卡尔曼滤波  PID控制
收稿时间:2021/4/16 0:00:00

Design of Intelligent Filling System for Packaging Production Line
HU Ying-kun,LAN Mao-kun.Design of Intelligent Filling System for Packaging Production Line[J].Packaging Engineering,2021,42(23):214-218.
Authors:HU Ying-kun  LAN Mao-kun
Affiliation:Guangdong Polytechnic of Industry & Commerce, Guangzhou 510510, China
Abstract:The work aims to design an intelligent filling control system for packaging production line to improve the filling precision and speed. The basic structure of filling production line, including delivery, empty bottle detection, filling, quality detection, secondary filling, cover and finished product output stations, was described. Taking the secondary filling irrigation as the research object, a micro filling control system based on PID control of cerebellum neural network was designed, which could make up for the deficiency of conventional PID control. At the same time, kalman filter was used to eliminate the interference signal and noise during operation. The feasibility and effectiveness of the control system were verified by experiments. The filling characteristics were analyzed and there were many factors affecting the filling accuracy which interfered with each other. The experimental results showed that the quality deviation of all samples did not exceed 0.1 g, and the production line ran stably, quickly and reliably during the filling process. The micro filling control system can improve the filling accuracy and speed, and can meet the requirements of high precision and high speed of packaging production line.
Keywords:filling  cerebellar neural network  kalman filtering  PID control
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