| Title: | Intelligent system for prediction of mechanical properties of material based on metallographic images |
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| Authors: | ID Paulič, Matej (Author)
ID Močnik, David (Author)
ID Ficko, Mirko (Author)
ID Balič, Jože (Author)
ID Irgolič, Tomaž (Author)
ID Klančnik, Simon (Author) |
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| Files: |  Tehnicki_vjesnik_2015_Paulic_et_al._Intelligent_system_for_prediction_of_mechanical_properties_of_material_based_on_metallographic_image.pdf (2,02 MB)
MD5: FD4116BECE56054C2E052628D11AEFC8
PID: 20.500.12556/dkum/d36875c8-9c67-45c9-ab03-3e31da93f6fc
 http://hrcak.srce.hr/149370
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| 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. |
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| Keywords: | artificial neural network, factor of phase coherence between the surfaces, fracture toughness, image processing, mechanical properties, metallographic image, ultimate tensile strength, yield strength |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2015 |
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| Number of pages: | str. 1419-1424 |
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| Numbering: | Letn. 22, št. 6 |
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| PID: | 20.500.12556/DKUM-66818  |
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| ISSN: | 1330-3651 |
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| UDC: | 620.172.25:669:004.92 |
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| ISSN on article: | 1330-3651 |
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| COBISS.SI-ID: | 19203862 |
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| DOI: | 10.17559/TV-20130718090927 |
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| NUK URN: | URN:SI:UM:DK:NEVWIE8N |
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| Publication date in DKUM: | 12.07.2017 |
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| Views: | 1701 |
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| Downloads: | 455 |
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| Metadata: |    |
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| Categories: | Misc.
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