Research On Dynamic Adjustment Of Injection Molding Process Parameters Based On Product Surface Quality And Variation Degree Prediction

Plastic injection molding has the advantages of high production efficiency,low cost,small weight and good flexibility to complex geometric shapes.It is an important manufacturing technology of plastic products.The product quality depends on the material,product structure and design of mold,as well as the technological parameters of the injection molding process,among which the technological parameters are the most direct and effective control factors.At present,experienced technical workers adjust parameters by trial and error because of the coupling relationship between the process parameters.With the development of intelligent equipment,the automation and intelligence of injection molding process are the future development trend.Therefore,it is of great significance to study the coupling relationship of technological parameters in the injection molding process and to ensure the good quality consistency of injection molding products through dynamic adjustment of technological parameters.The first chapter summarizes the research background and significance of this paper,the research status of injection molding technology and product quality control technology,and expounds the main research content and organizational framework.The second chapter analyzes the coupling relationship between injection molding process parameters and product quality.The hierarchy mechanism of coupling strength is established.The propertiesand and classification of the process parameters and the process of injection molding process is studied.In view of the nonlinear coupling adjustment problem of injection molding process parameters,the product flaw normalization preprocessing is conducted and the the target model of adjusting process parameters is built.The third chapter proposes a quality prediction model for injection mold products in the time dimension based on the deep learning improved hybrid algorithm ProphetLSTM.The change rate is obtained through the product quality change data.The judgment conditions and methods based on change rate are proposed.Conservative modification of the time dimension prediction of product quality is conducted to improve the stability of the process parameter dynamic adjustment system and product manufacturing accuracy.The fourth chapter proposes a process parameters dynamic adjustment method based on Kriging model EGO global optimization.It periodically updates the training sample data and update forecast model by using the Latin hypercube sampling and sifting the effective data of visual inspection.The main parameters and significance degree affecting the quality are obtained through the Taguchi orthogonal experiment design and experimental results of variance analysis.We build the BP neural network model of signal-to-noise ratio to determine the initial process parameters,adopt the method of compound agent model dynamic adjusting process parameters in the process of production.In chapter five,a progressive dynamic adjustment strategy of process parameters based on quality prediction of injection molding products is proposed and the corresponding control system is developed.Through simulation analysis and actual injection molding debugging,the effectiveness of the progressive dynamic adjustment strategy is verified by taking the front of plastic plate as the test object for example analysis and comparison.The sixth chapter summarizes the research work and results of the paper,and prospects for further research.