OPTIMIZATION OF INJECTION MOLDING QUALITY BASED ON BP NEURAL NETWORK AND PSO

Abstract

An electronic product shell is prone to uneven shrinkage, warpage and sink marks, resulting in a large number of unqualified products and increased costs. Therefore, volumetric shrinkage, warpage deformation and sink mark index are selected as optimization goals. Based on the orthogonal test and entropy weight method, a multi-objective optimization was transformed into a comprehensive evaluation optimization. A BP neural network combined with a particle swarm optimization algorithm was used to obtain the optimal combination of process parameters, simulated by Moldflow to reduce volumetric shrinkage to 3.46 %, warpage deformation to 2.538 mm, and the sink mark index to 1.87 % so as to improve the injection molding quality of the plastic parts and meet the requirements of qualified parts. The combination of the BP neural network and particle swarm optimization algorithm can prevent the defects such as large shrinkage, warpage and sink marks