Optimization Of Longitudinal Heat Sealing Process Parameters And Human-machine Interaction System Development For Pillow-type Packaging Machines

With the improvement of people’s living standards,the demand for various packaging products is gradually expanding.As a commonly used equipment for packaging various products,pillow-type packaging machines occupy a large share in the market.In the current fiercely competitive market environment,pillow-type packaging machines are developing towards automation,informatization,and intelligence.Relatively speaking,the development of pillow-type packaging machines in China is relatively backward,mainly reflected in the complexity of human-machine interaction systems and the difficulty in selecting process parameters.To address these problems,this paper designs a highly intelligent human-machine interaction system to achieve the automation of pillow-type packaging machines.Based on the RBF neural network and genetic algorithm,an optimization strategy for longitudinal heat sealing process parameters is implemented to achieve automatic optimization of longitudinal and heat sealing process parameters.The research in this topic has achieved intelligent upgrade of pillow-type packaging machines and improved production efficiency.Firstly,the production and processing process and operation mode of pillow-type packaging machines were analyzed,and the overall control equipment was selected.Secondly,the high-performance development framework Qt was used for software development to realize various interface interaction functions such as operation,debugging,maintenance,and machine locking,ensuring the stability,scalability,and security of the human-machine interaction system.Automated film length measurement using limiting and averaging filtering and electronic cam pillow-type multi-axis motion control algorithm are used to achieve automated and rapid production of pillow-type packaging machines.Then,the TCP/IP communication protocol is selected as the communication bus with the underlying motion controller to achieve fast and stable data exchange.Finally,various data of the entire system are saved through binary files and SQLite database to ensure data integrity and real-time.To further improve the intelligence level of pillow-type packaging machines,based on the improvement of the human-machine interaction system,this paper designed an optimization strategy for longitudinal heat sealing process parameters.First,the RBF neural network model is used to predict the heat sealing strength,solving the problem of difficult prediction due to the complex coupling relationship between the heat sealing distance,temperature,speed,and strength in the longitudinal heat sealing process,and the model is transplanted into the human-machine interaction system.Then,based on the RBF neural network model,the fitness function of the genetic algorithm is designed,and the coding method and genetic operator of the genetic algorithm are determined by analyzing the relationship between process parameters.Finally,the optimal process parameters are obtained through population iteration,and the optimized process parameters are tested on the machine.Tests showed that the human-machine interaction system designed in this paper can achieve precise control and production of pillow-type packaging machines,and achieved the goal of efficient production.The process parameters optimized by the RBF neural network-genetic algorithm optimization strategy were input into the pillow-type packaging machine for processing,and the heat sealing strength of the products obtained was around the target heat sealing strength of 27.67N/15 mm,with a maximum error of no more than 10%,proving that the system and its strategy can achieve parameter optimization of pillow-type packaging machines and is better than products processed under other process parameters.Currently,this human-machine interaction system has been installed in batches in pillow-type packaging machines for production and has achieved good economic benefits.