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1.
OPTIMIZACIJA POSTOPKA OBDELAVE KONZOLE MENJALNIKA
Matej Paulič, 2011, undergraduate thesis

Abstract: Namen diplomske naloge je optimizirati postopek obdelave konzole, tako da bo obdelovalni čas čim krajši in izbrati takšna orodja in parametre rezanja, da bo strošek le-teh čim manjši. V diplomski nalogi so predstavljeni izračuni posameznih obdelovalnih časov, izračun potrebnega števila orodij za obdelavo serije izdelkov, strošek vseh potrebnih orodij pri obdelavi serije in delovni potek obdelave konzole menjalnika, ki se bo obdelovala v podjetju ECOM Ruše d.o.o..
Keywords: čelna poravnava, frezanje, vrtanje, obdelovalno orodje, orodni list, delovni potek, obdelovalni čas, optimizacija
Published: 13.10.2011; Views: 1354; Downloads: 121
.pdf Full text (3,42 MB)

2.
Model inteligentnega sistema za prilagajanje postavitve obdelovanca v delovni prostor obdelovalnega stroja
Matej Paulič, 2015, doctoral dissertation

Abstract: Moderni obdelovalni sistemi zahtevajo nenehno posodabljanje in vključevanje najnovejših tehnologij v tehnološke postopke. Raziskave in razvoj obdelovanih postopkov se v zadnjem obdobju nagiba k rešitvam, ki na eni strani povečujejo hitrosti, večajo fleksibilnost in točnost, na drugi strani pa skrajšujejo razvojni cikel izdelka. Uvajanje računalniškega vida na obdelovalne stroje nam omogoča, da lahko brez večjih težav zaznamo postavitev (pozicijo) in orientacijo obdelovanca, ki ga bomo obdelovali. V raziskavi predlagamo uporabo metod umetne inteligence za zasnovo naprednega sistema, ki bo sposoben samodejno prilagoditi postavitev obdelovanca v obdelovalni prostor stroja., ter prilagoditi CNC-program za novo pozicijo vpetega obdelovanca. Predlagana je uporaba algoritmov optimizacije z rojem delcev, kot tudi uporaba mehke logike. V zaključku raziskave so podani rezultati, ki utemeljujejo in dokazujejo uporabnost sistema. Podani so tudi predlogi za nadaljnji razvoj in raziskave.
Keywords: Rezkanje, obdelovanec, računalniški vid, inteligentni sistem, skupinska inteligenca, optimizacija postavitve
Published: 06.11.2015; Views: 745; Downloads: 82
.pdf Full text (4,57 MB)

3.
Intelligent system for prediction of mechanical properties of material based on metallographic images
Matej Paulič, David Močnik, Mirko Ficko, Jože Balič, Tomaž Irgolič, Simon Klančnik, 2015, original scientific article

Abstract: This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample.
Keywords: artificial neural network, factor of phase coherence between the surfaces, fracture toughness, image processing, mechanical properties, metallographic image, ultimate tensile strength, yield strength
Published: 12.07.2017; Views: 362; Downloads: 196
.pdf Full text (2,02 MB)
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4.
Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation
David Močnik, Matej Paulič, Simon Klančnik, Jože Balič, 2014, original scientific article

Abstract: As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning.
Keywords: artificial neural network, dimensional dviation, particle swarm optimization, regression
Published: 12.07.2017; Views: 258; Downloads: 72
.pdf Full text (1,17 MB)
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