20.500.12556/DKUM-66818 Intelligent system for prediction of mechanical properties of material based on metallographic images Inteligentni sustav za predviđanje mehaničkih svojstava materijala na osnovu metalografskih slika 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. U radu se predstavlja razvijeni inteligentni sustav za predviđanje mehaničkih svojstava materijala na temelju metalografskih slika. Sustav se sastoji od dva modula. Prvi je modul algoritam za dobivanje karakteristika iz metalografskih slika. Prvi algoritam očitava metalografsku sliku dobivenu mikroskopom, zatim se dobivaju karakterisike razvijenim algoritmom, i na kraju algoritam izračunava omjere mikrostrukture materijala. U ovom istraživanju potrebno je što točnije odrediti omjere grafita, ferita i ausferita iz metalografskih slika. Drugi modul razvijenog sustava je sustav za predviđanje mehaničkih svojstava materijala. Predviđanje mehaničkih svojstava materijala izvršeno je pomoću feed-forward umjetne neuronske mreže. Kao ulazi u umjetnu neuronsku mrežu rabljeni su izračunati omjeri grafita, ferita i ausferita, dok su mehanička svojstva materijala upotrebljena kao ciljevi za uvježbavanje. Uvježbavanje umjetnih neuronskih mreža obavljeno je na prilično maloj bazi podataka, no mijenjajući parametre nama je to uspjelo. Umjetna neuronska mreža je naučila do te mjere da je greška bila prihvatljiva. S orijentiranom neuronskom mrežom uspješno smo predvidjeli mehanička svojstva izuzetog uzorka. artificial neural network factor of phase coherence between the surfaces fracture toughness image processing mechanical properties metallographic image ultimate tensile strength yield strength umetne nevronske mreže mehanika loma lomna žilavost procesiranje slik mehanske lastnosti natezna trdnost napetost tečenja true false true Angleški jezik Hrvaški jezik Znanstveno delo 2017-07-12 08:46:35 2017-07-12 08:47:24 2022-08-01 13:12:21 2019-07-11 15:59:56 2015 0 0 str. 1419-1424 št. 6 Letn. 22 2015 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1330-3651 620.172.25:669:004.92 15346181 19203862 10.17559/TV-20130718090927 1330-3651 URN:SI:UM:DK:NEVWIE8N Tehnicki_vjesnik_2015_Paulic_et_al._Intelligent_system_for_prediction_of_mechanical_properties_of_material_based_on_metallographic_image.pdf Tehnicki_vjesnik_2015_Paulic_et_al._Intelligent_system_for_prediction_of_mechanical_properties_of_material_based_on_metallographic_image.pdf 1 FD4116BECE56054C2E052628D11AEFC8 d61db80c008eebfda2479250e8d28188444419212260af92e7b186cc2b015897 66340f57-7c0e-11eb-bb7a-00155d0001ca 20.500.12556/dkum/d36875c8-9c67-45c9-ab03-3e31da93f6fc https://dk.um.si/Dokument.php?lang=slv&id=114044 http://hrcak.srce.hr/149370 1 https://dk.um.si/Dokument.php?lang=slv&id=114043 Fakulteta za strojništvo 0 0 0