ArticleScientific reports2022
Numerical simulation of closed plastic impeller molding process and its parameter optimization.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Variotherm assisted precision injection molding of plastic optical lenses based on improved cooling stage control model.Scientific reports · 2025Article
- Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach.Scientific reports · 2024Article
- Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features.Frontiers in chemistry · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Aiming at the warping deformation and volume shrinkage of the closed plastic impeller, the relevant compression molding process parameters were optimized.Based on the theoretical equation of the pressing pressure for general sheet plastics, the theoretical derivation of the pressing pressure suitable for cylindrical cavities was carried out, and the theoretical derivation of the pressing pressure suitable for closed plastic impeller molding was derived, and the pressing pressure was calculated to be 15 MPa. The warpage deformation and shrinkage rates under different combinations of process parameters were obtained, and the influence of process parameters on warpage deformation and shrinkage rates, as well as the two combinations of process parameters that satisfy the best warpage deformation and shrinkage rates, respectively, were obtained by extreme difference analysis. A GA-BP neural network model was established for the prediction of the process parameters of closed plastic impeller compression molding, and the prediction curves were fitted into a function to obtain a set of process parameter combinations with optimal warpage and shrinkage at the same time by using the multi-objective optimization function of NSGA-II algorithm.
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Registered trials
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