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Modeling of membrane inflation in blow molding : neural network prediction of initial dimensions from final part specifications

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1002/adv.1993.060120102
AuthorSearch for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada
FormatText, Article
Abstract
The use of neural networks in the modeling of the inflation stage of the blow molding process is discussed. The simulation is enacted in the reverse process direction, predicting initial membrane dimension requirements from final part thickness distributions. This situation has practical implications because tooling costs and machine down times can be minimized with the information obtained. The optimal network topology entails simultaneous pre‐ and postprocessing of the data. An adaptive window is employed for modeling of the effects of adjacent segments. The optimal window length is 16 for both the input and output layers in the network topology. Simulations are run for bottles blown using various constant and pulsed die gaps
Publication date
PublisherWiley
In
  • Advances in Polymer Technology 12, no. 1: 3–24.
LanguageEnglish
Peer reviewedYes
NRC numberCNRC 35334
NRC-IMI93AP-41300-1453-G
NPARC number23004450
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Record identifier03b74fcb-5cf1-4653-aabd-eaf7b171e131
Record created2018-11-02
Record modified2020-04-24
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