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Title:Uporaba strojnega učenja za modeliranje tehnoloških procesov : magistrsko delo
Authors:Kovič, Klemen (Author)
Brezočnik, Miran (Mentor) More about this mentor... New window
Tominc, Polona (Mentor) More about this mentor... New window
Files:.pdf MAG_Kovic_Klemen_2020.pdf (1,69 MB)
MD5: 76B14C1706D00859627FA28D0BE41FB7
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:V magistrski nalogi predstavimo metode strojnega učenja in njihovo rabo za modeliranje treh različnih tehnoloških procesov: trdega struženja, visokohitrostnega struženja in rezkanja. Podatke smo pridobili iz znanstvenih člankov, objavljenih v mednarodnih revijah. Za modeliranje tehnoloških procesov smo uporabili multiplo linearno regresijo, naključne gozdove, stohastično gradientno pospeševanje in metodo cubist. Na koncu predstavimo prediktivne sposobnosti posameznih metod in primerjamo rezultate. Napovedi omenjenih štirih metod tudi primerjamo z rezultati, ki so jih poročali avtorji člankov, in identificiramo najuspešnejšo metodo strojnega učenja za vsak proces. Za zaključek še teoretično raziščemo vpliv strojnega učenja na industrijo in poslovanje podjetij.
Keywords:tehnološki procesi, struženje, rezkanje, visokohitrostne obdelave, modeliranje, strojno učenje, optimizacija poslovnih procesov
Year of publishing:2020
Place of performance:Maribor
Publisher:[K. Kovič]
Number of pages:XII, 69 str.
Source:Maribor
UDC:004.85:621.9(043.2)
COBISS_ID:27598339 New window
NUK URN:URN:SI:UM:DK:B6RR5QEP
Views:312
Downloads:43
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FS
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:22.05.2020

Secondary language

Language:English
Title:Application of machine learning in modelling of technological processes
Abstract:In the master's thesis we present the machine learning methods and their use for modelling of three different technological processes: hard turning, high speed turning and milling. Data was obtained from scientific articles published in international journals. Multiple linear regression, random forests, stochastic gradient boosting and the cubist method were used to model the technological processes. Finally, we present the predictive capabilities of each method and compare the results. We also compare the predictions of these four methods with the results reported by the authors of the articles and identify the most successful machine learning method for each process. To conclude, we explore the impact of machine learning on industry and business operations.
Keywords:technological processes, turning, milling, high speed machining, modelling, machine learning, business process optimization


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