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Title:Optimizacija procesnih parametrov pri postopku brizganja plastike : magistrsko delo
Authors:ID Bistrović, Dominik (Author)
ID Gotlih, Janez (Mentor) More about this mentor... New window
ID Logožar, Klavdij (Mentor) More about this mentor... New window
Files:.pdf MAG_Bistrovic_Dominik_2025.pdf (5,90 MB)
MD5: 64FFB5A1C28551D93B4FB46DAFE8323D
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Optimizacija parametrov pri injekcijskem brizganju plastike je eden od najpomembnejših korakov pri načrtovanju proizvodnega procesa injekcijskega brizganja s katerim zagotovimo boljšo kakovost izdelka in stabilnost proizvodnega procesa. Kot študijski primer je bilo izbrano ohišje za Raspberry Pi, ki predstavlja tankostenski izdelek, primeren za optimizacijo v programskem okolju Autodesk Moldflow Insight 2023. Teoretični del opisuje osnovne principe tehnologije brizganja ter vpliv ključnih procesnih parametrov na najpogostejše napake pri izvedbi procesa brizganja. V eksperimentalnem delu je bila izvedena vrsta numeričnih optimizacij s katerimi so analizirani vplivni parametri in njihov vpliv na končni izdelek. Z uporabo statistične metode DOE (Design of Experiments) smo preučili kombinacije, ki vodijo k zmanjšanju napak, krajšem ciklu in večji ponovljivosti procesa. Rezultati kažejo, da je z optimizacijo mogoče doseči bistvene izboljšave kakovosti izdelka ter skrajšanja časa cikla brez sprememb geometrije izdelka.
Keywords:injekcijsko brizganje, optimizacija procesa, Moldflow Insight, Raspberry Pi ohišje, numerična simulacija, kakovost izdelka, DOE
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[D. Bistrović]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (X, 84 f.))
PID:20.500.12556/DKUM-93042 New window
UDC:678.027.74-048.33(043.2)
COBISS.SI-ID:241220099 New window
Publication date in DKUM:27.06.2025
Views:218
Downloads:37
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:02.06.2025

Secondary language

Language:English
Title:Optimization of process parameters in the plastic injection molding process
Abstract:The optimization of parameters in plastic injection molding is one of the most important steps in designing a manufacturing process, as it ensures improved product quality and production stability. The Raspberry Pi enclosure was selected as the case study, representing a thin-walled product suitable for optimization using the Autodesk Moldflow Insight 2023 software environment. The theoretical part describes the basic principles of injection molding technology and the influence of key process parameters on the most common defects occurring during the molding process. In the experimental part, a series of numerical optimizations were carried out to analyse the influential parameters and their impact on the final product. Using the statistical DOE (Design of Experiments) method, parameter combinations were studied that lead to reduced defects, shorter cycle times, and greater process repeatability. The results show that optimization can achieve significant improvements in product quality and reductions in cycle time without changing the product geometry.
Keywords:injection molding, process optimization, Moldflow Insight, Raspberry Pi enclosure, numerical simulation, product quality, DOE


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