Open AccessOptimization of injection molding of display panel based on PSO-BP neural network
Author(s) -
Qiuli Li,
Leping Bu,
Luo Hui,
Hailin Li,
Yijiang Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1986/1/012076
Subject(s) - particle swarm optimization , artificial neural network , shrinkage , image warping , molding (decorative) , mold , deformation (meteorology) , process (computing) , materials science , computer science , composite material , artificial intelligence , algorithm , operating system
aiming at the practical production problem of large thin-wall plastic parts with large warping deformation and shrinkage during injection molding, the injection process parameters were optimized by CAE technology and neural network prediction method, to obtain high quality plastic finished products. Particle swarm optimization (PSO) algorithm was used to improve the BP neural network, and based on the neural network, the prediction model between injection process parameters and warpage deformation, volume shrinkage was constructed. The min-variables of the two parameters are accurately predicted by the model, and the best injection molding parameters are obtained. Through the verification of the mold test, the molding quality of the plastic parts is improved, the production cycle of the mold is shortened, and the economic benefit of the mold production is improved.