A strategy for problem solving of filling imbalance in geometrically balanced injection molds

Krzysztof Wilczyński , Przemysław Narowski

Abstract

Simulation and experimental studies were performed on filling imbalance in geometrically balanced injection molds. An original strategy for problem solving was developed to optimize the imbalance phenomenon. The phenomenon was studied both by simulation and experimentation using several different runner systems at various thermo-rheological material parameters and process operating conditions. Three optimization procedures were applied, Response Surface Methodology (RSM), Taguchi method, and Artificial Neural Networks (ANN). Operating process parameters: the injection rate, melt temperature, and mold temperature, as well as the geometry of the runner system were optimized. The imbalance of mold filling as well as the process parameters: the injection pressure, injection time, and molding temperature were optimization criteria. It was concluded that all the optimization procedures improved filling imbalance. However, the Artificial Neural Networks approach seems to be the most e°Cient optimization procedure, and the Brain Construction Algorithm (BSM) is proposed for problem solving of the imbalance phenomenon.

Author Krzysztof Wilczyński (FPE / IoMP)
Krzysztof Wilczyński,,
- The Institute of Manufacturing Processes
, Przemysław Narowski (FPE / IoMP)
Przemysław Narowski,,
- The Institute of Manufacturing Processes
Journal seriesPolymers, ISSN 2073-4360
Issue year2020
Vol12
No4
Pages1-20
Publication size in sheets0.95
Article number805
ASJC Classification1600 General Chemistry; 2507 Polymers and Plastics
DOIDOI:10.3390/POLYM12040805
URL https://www.mdpi.com/2073-4360/12/4/805
Languageen angielski
File
polymers-12-00805.pdf 6.93 MB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 09-09-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.057; WoS Impact Factor: 2017 = 2.935 (2) - 2017=3.509 (5)
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