Research On The Control Of Melt Fluctuation And Repetition Accuracy In Injection Molding

Injection molding is one of the most important plastic processing methods.In recent years,quality control has become a research hotspot with the continuously improving of quality requirements for injection molded parts.The quality of injection molded parts is determined by the melt’s quality,and the melt’s temperature and pressure are essential indicators of the quality of the melt.Furthermore,the temperature and pressure of the melt are essential factors that affect the quality of the part;The repeated precision of forming is an essential indicator of part quality,and improving the repeated precision of forming is the prerequisite and foundation for achieving quality control.The main work of this topic is as follows:(1)Using temperature and pressure sensors,displacement sensors,injection molding machine acquisition modules,and data acquisition cards,an injection molding machine parameter data acquisition platform is established to achieve online collection of injection molding parameters such as melt temperature,melt pressure,mold locking force,injection pressure,back pressure,screw rotation speed,and screw torque;Design standard stretch spline molds;The characterization methods of melt temperature difference,melt pressure fluctuation,and molding weight repeatability accuracy is proposed.(2)Based on single factor experiments,the effects of melt temperature,back pressure,screw speed,and plasticizing stroke on the temperature difference of the plasticized melt were studied;Orthogonal experiments were used to determine the degree of influence of various factors on the temperature difference of the plasticizing melt through variance and range analysis.BP neural network is used to establish a prediction model for the melt temperature difference in the injection molding process and the plasticizing stage.Based on the prediction model,genetic algorithms are used to optimize process parameters and reduce the melt temperature difference in the plasticizing stage.Based on FLUENT,the melt temperature during the injection phase was simulated to explore the influence of different injection speeds on the melt temperature difference during the injection phase.(3)The relationship between melt pressure fluctuations and tensile strength of products was explored through experiments;Based on single factor experiments,the effects of melt temperature,back pressure,screw speed,and plasticizing temperature on melt pressure fluctuations were studied;Orthogonal experiments were used to determine the degree of influence of various factors on melt pressure fluctuations through variance and range analysis;BP neural network is used to establish a prediction model for injection molding process and pressure fluctuations.Based on the prediction model,genetic algorithms are used to optimize process parameters,reduce pressure fluctuations,and improve melt quality.(4)A single factor experiment was designed to study the effects of injection speed,screw rotation speed,back pressure,melt temperature on the weight repetition accuracy of the product under the full filling experiment;In order to explore the precision of the injection device,the effects of injection speed,back pressure,screw rotation speed,melt temperature,and ejection on the weight repetition accuracy were investigated in an underfilled experiment.