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

DOI	Trouver le DOI : https://doi.org/10.1002/adv.1993.060120102
Auteur	Rechercher : Diraddo, R. W.¹; Rechercher : Garcia-Rejon, A.¹
Affiliation	Conseil national de recherches du Canada
Format	Texte, Article
Résumé	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
Date de publication	1993-04-01
Maison d’édition	Wiley
Dans	Advances in Polymer Technology 12, nº 1 : 3–24.
Langue	anglais
Publications évaluées par des pairs	Oui
Numéro du CNRC	CNRC 35334 NRC-IMI93AP-41300-1453-G
Numéro NPARC	23004450
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	03b74fcb-5cf1-4653-aabd-eaf7b171e131
Enregistrement créé	2018-11-02
Enregistrement modifié	2020-04-24