In the process of injection molding, the vacuum pump rear housing is prone to
warping deformation and volume shrinkage, which affects its sealing performance.
The main reason is the improper control of the injection process and the large
flat structure of the vacuum pump rear housing, which does not meet its
production and assembly requirements (the warpage deformation should be
controlled within 1 mm and the volume shrinkage within 8.6%). To address this
issue, this study initially utilized orthogonal experiments to obtain training
samples and conducted a preliminary analysis using gray relational analysis.
Subsequently, a predictive model was established based on a one-dimensional
convolutional neural network (1D CNN). Input parameters from the injection
molding process, including melt temperature, mold temperature, packing pressure,
packing time, injection pressure, injection time, and cooling time, were used
while warping deformation and volume shrinkage were considered as outputs.
Global optimization was performed using the non-dominated sorting genetic
algorithm II (NSGA-II), and the optimal combination of process parameters was
evaluated using the criterion importance through intercriteria
correlation—technique for order preference by similarity to ideal solution
(CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators
outperformed the optimization results from orthogonal experiments, confirming
the effectiveness of the injection molding process parameter optimization method
based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product
warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%,
and all of them meet the production requirements.
