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1.
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: 543; Downloads: 108
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2.
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: 726; Downloads: 343
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3.
Inteligentno toleriranje sklopov glede na tehnološke zmožnosti obdelovalnih postopkov
David Močnik, 2016, doctoral dissertation

Abstract: Sodobna proizvodnja je podvržena najrazličnejšim zahtevam, ki jih povezuje zahteva po učinkovitosti. Da zadostimo tej zahtevi, je treba tolerance sestavnih delov sklopa načrtovati s premislekom. Z ustreznim načrtovanjem toleranc lahko namreč zelo vplivamo na zmanjšanje proizvodnih stroškov. V ta namen je v doktorski disertaciji za reševanje kompleksnega problema načrtovanja toleranc razvit in predstavljen inteligentni sistem toleriranja, ki s pomočjo metod umetne inteligence, na podlagi vhodnih podatkov porazdeli tolerance sestavnih delov, tako da so stroški izdelave minimalni. Razvita sta dva različna modula za načrtovanje toleranc; modul z optimizacijo z rojem delcev in modul, ki temelji na gravitacijskem iskalnem algoritmu. Uporaba razvitega sistema je prikazana na dveh realnih primerih. Primerjane so vrednosti najnižjih doseženih stroškov s posamezno optimizacijsko metodo, vrednosti predlaganih toleranc in izbrani proizvodni procesi – stroji, ki jih je predlagala inteligenca. Skozi opravljena testiranja pri zasnovi inteligentnega sistema toleriranja se je optimizacija z rojem delcev izkazala za najučinkovitejšo metodo. Razvit je tudi uporabniški vmesnik, ki omogoča enostavno načrtovanje toleranc. V zaključku raziskave je tudi potrjena teza doktorske disertacije, hkrati pa so tudi podane smernice za nadaljnji razvoj in raziskave.
Keywords: načrtovanje toleranc, izdelovalni stroški, optimizacija, inteligentno toleriranje, optimizacija z rojem delcev, gravitacijski iskalni algoritem
Published: 01.07.2016; Views: 1100; Downloads: 118
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4.
ANALIZA VPLIVA POSTAVITVE OPTIČNIH KOMPONENT NA MERILNO NEGOTOVOST LASERSKEGA INTERFEROMETRA
David Močnik, 2010, undergraduate thesis

Abstract: Za učinkovit nadzor koordinatnih merilnih naprav in za analizo merilnih pogreškov oz. vplivov na merilno negotovost je postopek meritve z laserskim interferometrom zelo primeren. Seveda pa se pojavljajo določene omejitve glede točnosti oz. negotovosti meritve. Diplomsko delo zajema nekaj teoretičnih osnov meroslovja, predstavljen je princip merjenja dimenzij z laserskim interferometrom ter koncept negotovosti meritve z laserskim interferometrom. V nadaljevanju je izvedena analiza variant postavitev optičnih komponent za meritev pomika na koordinatni merilni napravi. Pri tem je glavna pozornost namenjena variantam s katerimi se da eliminirati Abbejev pogrešek, ki ima v mnogih primerih pomemben prispevek k negotovosti meritve. Na koncu je podan tudi predlog postavitve optičnih komponent za meritev razdalje na koordinatni merilni napravi v Laboratoriju za tehnološke meritve.
Keywords: laserski interferometer, meritev razdalje, merilna negotovost, Abbejev pogrešek
Published: 31.03.2010; Views: 2240; Downloads: 166
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