Artificial neural network applied on sintered BaTiO3-ceramic density
It is very important to determine microstructure parameters of consolidated ceramic samples, because it opens new frontiers for further microelectronics miniaturization and integrations. Therefore, controlling, predicting and designing the ceramic materials’ properties are the objectives in...
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| Format: | Article |
| Language: | English |
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International Institute for the Science of Sintering, Beograd
2022-01-01
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| Series: | Science of Sintering |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/0350-820X/2022/0350-820X2204425R.pdf |
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| author | Ranđelović Branislav M. Ribar Srđan Mitić Vojislav V. Marković Bojana Fecht Hans Vlahović Branislav |
| author_facet | Ranđelović Branislav M. Ribar Srđan Mitić Vojislav V. Marković Bojana Fecht Hans Vlahović Branislav |
| author_sort | Ranđelović Branislav M. |
| collection | DOAJ |
| description | It is very important to determine microstructure parameters of consolidated
ceramic samples, because it opens new frontiers for further microelectronics
miniaturization and integrations. Therefore, controlling, predicting and
designing the ceramic materials’ properties are the objectives in ceramic
materials consolidating process, within the science of sintering. In order
to calculate the precise values of desired microstructure parameter at the
level of the grains’ coating layers based on the measurements on the bulk
samples, we applied the artificial neural networks, as a powerful
mathematical tool for mapping input-output data. Input signals are
propagated forward, as well as the adjustable coefficients that contribute
the calculated output signal, denoted as error, which is propagated
backwards and replaced by examined parameter. In our previous research, we
used neural networks to calculate different electrophysical parameters at
the nano level of the grain boundary, like relative capacitance, breakdown
voltage or tangent loss, and now we extend the research on sintered
material’s density calculation. Errors on the network output were
substituted by different consolidated samples density values measured on the
bulk, thus enabling the calculation of precise material’s density values
between the layers. We performed the neural network theoretical experiments
for different number of neurons in hidden layers, according to experimental
ceramics material’s density of ρ=5.4x103[kg/m3], but it opens the
possibility for neural networks application within other density values, as
well. |
| first_indexed | 2024-04-10T04:29:43Z |
| format | Article |
| id | doaj.art-a673008fd29444b9b2f33ace03cebba0 |
| institution | Directory Open Access Journal |
| issn | 0350-820X 1820-7413 |
| language | English |
| last_indexed | 2024-04-10T04:29:43Z |
| publishDate | 2022-01-01 |
| publisher | International Institute for the Science of Sintering, Beograd |
| record_format | Article |
| series | Science of Sintering |
| spelling | doaj.art-a673008fd29444b9b2f33ace03cebba02023-03-10T08:05:53ZengInternational Institute for the Science of Sintering, BeogradScience of Sintering0350-820X1820-74132022-01-0154442543810.2298/SOS2204425R0350-820X2204425RArtificial neural network applied on sintered BaTiO3-ceramic densityRanđelović Branislav M.0Ribar Srđan1Mitić Vojislav V.2Marković Bojana3Fecht Hans4https://orcid.org/0000-0002-2917-0631Vlahović Branislav5https://orcid.org/0000-0001-8965-1480University of Nis, Faculty of Electronic Engineering, Nis, Serbia + University of K. Mitrovica, Faculty of Teachers Education, Leposavić, SerbiaUniversity of Belgrade, Faculty of Mechanical Engineering, Belgrade, SerbiaUniversity of Nis, Faculty of Electronic Engineering, Nis, SerbiaUniversity of Nis, Faculty of Electronic Engineering, Nis, SerbiaInstititute of Functional Nanosystems, University of Ulm, Ulm, GermanyNorth Carolina Central University (NCCU), Durham, N. Carolina, USAIt is very important to determine microstructure parameters of consolidated ceramic samples, because it opens new frontiers for further microelectronics miniaturization and integrations. Therefore, controlling, predicting and designing the ceramic materials’ properties are the objectives in ceramic materials consolidating process, within the science of sintering. In order to calculate the precise values of desired microstructure parameter at the level of the grains’ coating layers based on the measurements on the bulk samples, we applied the artificial neural networks, as a powerful mathematical tool for mapping input-output data. Input signals are propagated forward, as well as the adjustable coefficients that contribute the calculated output signal, denoted as error, which is propagated backwards and replaced by examined parameter. In our previous research, we used neural networks to calculate different electrophysical parameters at the nano level of the grain boundary, like relative capacitance, breakdown voltage or tangent loss, and now we extend the research on sintered material’s density calculation. Errors on the network output were substituted by different consolidated samples density values measured on the bulk, thus enabling the calculation of precise material’s density values between the layers. We performed the neural network theoretical experiments for different number of neurons in hidden layers, according to experimental ceramics material’s density of ρ=5.4x103[kg/m3], but it opens the possibility for neural networks application within other density values, as well.http://www.doiserbia.nb.rs/img/doi/0350-820X/2022/0350-820X2204425R.pdfneural networkceramics materialssinteringdensityerror |
| spellingShingle | Ranđelović Branislav M. Ribar Srđan Mitić Vojislav V. Marković Bojana Fecht Hans Vlahović Branislav Artificial neural network applied on sintered BaTiO3-ceramic density Science of Sintering neural network ceramics materials sintering density error |
| title | Artificial neural network applied on sintered BaTiO3-ceramic density |
| title_full | Artificial neural network applied on sintered BaTiO3-ceramic density |
| title_fullStr | Artificial neural network applied on sintered BaTiO3-ceramic density |
| title_full_unstemmed | Artificial neural network applied on sintered BaTiO3-ceramic density |
| title_short | Artificial neural network applied on sintered BaTiO3-ceramic density |
| title_sort | artificial neural network applied on sintered batio3 ceramic density |
| topic | neural network ceramics materials sintering density error |
| url | http://www.doiserbia.nb.rs/img/doi/0350-820X/2022/0350-820X2204425R.pdf |
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