| Title: | Prediction of the hardness of hardened specimens with a neural network |
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| Authors: | ID Babič, Matej (Author)
ID Kokol, Peter (Author)
ID Belič, Igor (Author)
ID Panjan, Peter (Author)
ID Kovačič, Miha (Author)
ID Balič, Jože (Author)
ID Verbovšek, Timotej (Author)
ID Inštitut za kovinske materiale in tehnologije (Copyright holder) |
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| Files: |  Materiali_in_Tehnologije_2014_Babic_et_al._Prediction_of_the_Hardness_of_Hardened_Specimens_With_a_Neural_Network.pdf (632,41 KB)
MD5: F51262F532C5ED0E2A6C1C9DDF626E4B
 http://mit.imt.si/Revija/izvodi/mit143/babic.pdf
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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: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimize the structure and properties of tool steel, it is necessary to take into account the effect of the self-organization of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased. |
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| Keywords: | fractal dimension, fractal geometry, neural network, prediction, hardness, steel, tool steel, laser |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2014 |
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| Number of pages: | str. 409-414 |
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| Numbering: | Letn. 48, št. 3 |
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| PID: | 20.500.12556/DKUM-65203  |
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| ISSN: | 1580-2949 |
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| UDC: | 004.896:621.785.616.07 |
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| ISSN on article: | 1580-2949 |
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| COBISS.SI-ID: | 17827094 |
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| NUK URN: | URN:SI:UM:DK:J1Q7C08G |
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| Publication date in DKUM: | 17.03.2017 |
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| Views: | 2216 |
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| Downloads: | 179 |
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| Metadata: |    |
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| Categories: | Misc.
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