Optimization Of Neural Network In The Plastic Injection Moulding

The state and variation history of polymer in cavity have direct influence on final part performance and quality in injection molding. It is an important method in polymer processing field to simulate the polymer processing, predict the state and variation of the melt and aid to design the mold and setup the processing parameters. However, simulation of molding can only replace the trial-and-error and the correct also depend on the design's experience. It is difficult to get the best process condition and the influence of disturbance make it more difficult to get consistent good quality part. The investigation of on-line quality control for injection molding to make the molding keep on the desirable level and produce high quality part consistently is very valuable practically.In this paper, we mainly carried out the research on Process Control and Optimization of process variables in plastics injection molding process, optimized process variables and made process control on single variable of plastic injection molding process by using Artificial Neural Network.Orthogonal array experiment was done by using Taguchi DOE Method to optimize process variables. We got the relative importance of various factors and the optimal factor level combination and chose the most importance process variables as control variables in process control. SlN ratio of optimal factor level combination was estimated and tested with CAE simulation.