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Title:Uporaba umetne nevronske mreže za klasifikacijo obrabljenosti orodja pri rezkanju : diplomsko delo
Authors:Pudič, Žan (Author)
Brezočnik, Miran (Mentor) More about this mentor... New window
Files:.pdf UN_Pudic_Zan_2020.pdf (1,23 MB)
MD5: 5265C22DD4AF5B2676EA0122AF619273
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Diplomsko delo zajema reševanje problema klasifikacije obrabljenosti orodja pri rezkanju s pomočjo umetne inteligence. Namen dela je bil spoznati delovanje umetnih nevronskih mrež in postaviti arhitekturo ter optimizirati njeno delovanje. Za objektivno oceno sposobnosti napovedovanja postavljene nevronske mreže smo dobljene rezultate primerjali z rezultati, ki so bili dobljeni z metodo odločitvenih dreves. Rezultat dela je napovedovalni model z visoko stopnjo prilagojenosti na učnih in testnih podatkih, ki lahko v realnem času klasificira obrabljenost orodja.
Keywords:rezkanje, klasifikacija obrabljenosti orodja, strojno učenje, umetna nevronska mreža, odločitvena drevesa
Year of publishing:2020
Place of performance:Maribor
Publisher:[Ž. Pudič]
Number of pages:VII, 29 str.
Source:Maribor
UDC:004.8:[539.375.6:621.937](043.2)
COBISS_ID:42828547 New window
NUK URN:URN:SI:UM:DK:O3JT3UKU
Views:181
Downloads:71
Metadata:XML RDF-CHPDL 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:14.09.2020

Secondary language

Language:English
Title:The use of artificial neural network for classification of tool wear in milling
Abstract:The diploma work deals with the classification of the tool wear during milling process by the use of artificial intelligence. The purpose of this work was to obtain knowledge of the neural networks, set up their architecture and optimize their operation. For an objective assessment of the abilities of artificial neural network, we compared the obtained results by the neural networks with the results obtained by the decision tree method. Result of the work is a prediction model with a high degree of adaptability to learning and test data, that can classify tool wear in real time.
Keywords:milling, classification of tool wear, machine learning, artificial neural network, decision trees


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