發(fā)布時間：2018-04-16 18:48

  本文選題：柔性 + 定量包裝��； 參考：《西南科技大學》2016年碩士論文

【摘要】：我國的包裝機械技術與世界先進水平存在較大差距,主要體現(xiàn)在自動化程度低、產(chǎn)品可靠性差、精度較低和技術更新慢等方面。隨著信息技術與現(xiàn)代制造技術的快速發(fā)展,定量包裝機械進入了一個新的發(fā)展時期,柔性與包裝精度成為了許多學者和公司研究的熱點。依據(jù)用戶提出的要求,開發(fā)了柔性定量包裝系統(tǒng)的實驗樣機,致力于實現(xiàn)自動化包裝和高精度稱重。運用模塊化思想將系統(tǒng)分為了袋庫機構、送袋機構、張袋機構、張緊機構、稱重取袋機構和螺旋喂料機構六個部分,完成了結構設計和運動原理分析;以PLC和工業(yè)機器人為控制器,完成了控制系統(tǒng)硬件組態(tài),設計了真空吸盤回路、氣缸動作回路、各機械模塊的控制策略和上位機監(jiān)控軟件;安排了三因素五水平的正交試驗,探討了落差高度、螺旋轉速和物料的堆積密度對稱重精度的影響規(guī)律;以正交試驗數(shù)據(jù)為基礎,分別通過線性回歸分析和RBF神經(jīng)網(wǎng)絡,建立了稱重誤差的預測模型,分析了兩種預測模型的擬合誤差。利用三種敞口袋進行了系統(tǒng)可靠性實驗,結果表明:工業(yè)機器人的引入使系統(tǒng)具有極強的柔性,可適應不同的包裝環(huán)境;牛皮紙袋和紙塑復合袋等硬袋子的包裝成功率大于98%,軟袋子的包裝成功率稍低且會產(chǎn)生局部變形,是一個需要改進的問題。同時采用三種物料進行了稱重實驗,結果表明:兩種預測模型均可有效地減小稱重誤差,且RBF神經(jīng)網(wǎng)絡模型的精度更高,為高精度包裝提供了一種離線誤差補償方法。
[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.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TB486.3
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本文編號：1760157