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Title:Optimizacija tehnoloških veličin globokega vleka z uporabo metode roja delcev
Authors:ID Popovič, Katja (Author)
ID Ficko, Mirko (Mentor) More about this mentor... New window
ID Klančnik, Simon (Comentor)
Files:.pdf MAG_Popovic_Katja_2017.pdf (1,62 MB)
MD5: D0E492F587260CEA6076A93743697DA8
PID: 20.500.12556/dkum/98d05c2b-6e08-4b3a-92f6-38dc9c7b8ea5
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Magistrsko delo obravnava optimizacijo tehnoloških parametrov globokega vleka pločevine, ki vplivajo na kakovost izdelkov. Razviti optimizacijski modeli, ki so predstavljeni v magistrski nalogi, temeljijo na rezultatih numeričnih simulacij. Predstavili smo razvoj regresijskih modelov in modelov na osnovi evolucijskega računanja, ki napovedujejo kakovost izdelka po preoblikovalnem postopku s pomočjo matematičnih metod in z genetskim programiranjem. Izdelane modele smo uporabili v algoritmu za optimizacijo z rojem delcev, s katero smo dobili nove vrednosti tehnoloških parametrov. Slednje smo nato uporabili v nadaljnjih numeričnih simulacijah globokega vleka ter rezultate kritično analizirali.
Keywords:pločevina, globoki vlek, računalniške simulacije, genetsko programiranje, optimizacija z rojem delcev
Place of publishing:Maribor
Publisher:[K. Popovič]
Year of publishing:2017
PID:20.500.12556/DKUM-68284 New window
UDC:004.89:621.983(043.2)
COBISS.SI-ID:21202966 New window
NUK URN:URN:SI:UM:DK:JZPZUMY3
Publication date in DKUM:27.09.2017
Views:1691
Downloads:245
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:14.09.2017

Secondary language

Language:English
Title:Optimization of the deep drawing parameters using the particle swarm optimization
Abstract:This master thesis presents optimization of deep drawing process parameters that affect metal during deep drawing process. The already developed optimization models in this master thesis are based on the results of numerical simulations. We presented the development of regression models and evolutionary models of surface quality of the sheet metal product, according to mathematical methods and with genetic programming. We used models in the particle swarm optimization, which gave us new values of technological parameters. The latter was then used in further numerical simulations of deep drawing process and the results were critically analyzed.
Keywords:sheet metal, deep drawing, computer simulation, artificial intelligence, genetic programming, particle swarm optimization


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