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Title:Razvoj napovednih modelov z uporabo strojnega učenja za zmanjšanje izmeta v proizvodnji podjetja Talum, d. d.
Authors:ID Gojkošek, Alen (Author)
ID Vrbančič, Grega (Mentor) More about this mentor... New window
ID Siwiak, Marian (Comentor)
Files:.pdf MAG_Gojkosek_Alen_2024.pdf (2,61 MB)
MD5: 031365A94206E73F91A9EE351579005A
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo se osredotoča na razvoj napovednih modelov za napovedovanje izmeta aluminijastih izdelkov v proizvodnji podjetja Talum. Raziskava vključuje analizo proizvodnega procesa, obdelavo podatkov in uporabo različnih tehnik strojnega učenja. Z uporabo metod, kot so eksplorativna analiza podatkov, inženiring značilk in k-kratno prečno preverjanje, so bili razviti in ovrednoteni modeli za napovedovanje izmeta. Rezultati kažejo na izboljšano razumevanje dejavnikov, ki vplivajo na izmet, in ponujajo priložnosti za optimizacijo proizvodnega procesa. Delo zaključujejo priporočila za implementacijo modelov in nadaljnje raziskave na tem področju.
Keywords:strojno učenje, aluminijasti ulitki, napovedovanje izmeta, optimizacija proizvodnje, analiza podatkov
Place of publishing:Maribor
Publisher:[A. Gojkošek]
Year of publishing:2024
PID:20.500.12556/DKUM-90015 New window
UDC:004.85/.89:658.5(043.2)
COBISS.SI-ID:224418307 New window
Publication date in DKUM:22.10.2024
Views:200
Downloads:84
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-SA 4.0, Creative Commons Attribution-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-sa/4.0/
Description:This Creative Commons license is very similar to the regular Attribution license, but requires the release of all derivative works under this same license.
Licensing start date:21.08.2024

Secondary language

Language:English
Title:Development of predictive models using machine learning to reduce manufacturing waste in the company Talum, d. d.
Abstract:This master's thesis focuses on developing predictive models for forecasting scrap in aluminum product manufacturing at Talum company. The research includes analysis of the production process, data processing, and application of various machine learning techniques. Using methods such as exploratory data analysis, feature engineering, and k-fold cross-validation, models for predicting scrap were developed and evaluated. Results show improved understanding of factors influencing scrap and offer opportunities for production process optimization. The thesis concludes with recommendations for model implementation and further research in this field.
Keywords:machine learning, aluminium castings, scrap prediction, production optimisation, data analysis


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