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Title:Uvajanje Azure Ai asistenta za napovedovanje okvar strojev
Authors:ID Petek Regoršek, Aljaž (Author)
ID Klančnik, Simon (Mentor) More about this mentor... New window
ID Simonič, Marko (Comentor)
Files:.pdf MAG_Petek_Regorsek_Aljaz_2026.pdf (3,63 MB)
MD5: 57C28B5F5EE291C7EA2D315292707DC8
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Nenačrtovane zaustavitve CNC-strojev povzročajo izgubo časa, višje stroške vzdrževanja in zmanjšano učinkovitost proizvodnje. V magistrskem delu je bil razvit Azure AI-asistent za podporo napovedovanju okvar. Rešitev vključuje model strojnega učenja v Azure Machine Learning, Azure Function, OpenAPI povezavo in uporabo pristopa RAG za tehnično dokumentacijo. Model je dosegel skupno točnost 98,66 %, vendar je zaznal približno 66,4 % dejanskih okvar in spregledal 114 okvar. Sistem je zato obravnavan kot demonstracija prenosljive arhitekture, ne kot neposredno potrjena rešitev za realni CNC-stroj.
Keywords:umetna inteligenca, Microsoft Azure, AI asistent, CNC stroj
Place of publishing:Maribor
Year of publishing:2026
PID:20.500.12556/DKUM-99153 New window
Publication date in DKUM:03.09.2026
Views:124
Downloads:1
Metadata:XML 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:04.08.2026

Secondary language

Language:English
Title:Implementation of an Azure Ai assistant for predictive maintenance
Abstract:Unplanned CNC machine downtime results in lost time, higher maintenance costs, and reduced production efficiency. This master's thesis involved the development of an Azure AI assistant designed to support failure prediction. The solution incorporates a machine learning model within Azure Machine Learning, an Azure Function, an OpenAPI connection, and a Retrieval-Augmented Generation (RAG) approach applied to technical documentation. Although the model achieved an overall accuracy of 98.66%, it detected approximately 66.4% of actual failures and missed 114 failure instances. Consequently, the system is regarded as a demonstration of a transferable architecture rather than a fully validated solution for a real-world CNC machine.
Keywords:artificial intelligence, Microsoft Azure, AI assistant, CNC machine


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