TY  - JOUR
AU  - Stajčić, Ivana
AU  - Stajčić, Aleksandar
AU  - Serpa, Cristina
AU  - Vasiljević-Radović, Dana
AU  - Randjelović, Branislav
AU  - Radojević, Vesna
AU  - Fecht, Hans
PY  - 2022
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/5571
AB  - Polymers and polymer matrix composites are commonly used materials with applications extending from packaging materials to delicate electronic devices. Epoxy resins and fiber-reinforced epoxy-based composites have been used as adhesives and construction parts. Fractal analysis has been recognized in materials science as a valuable tool for the microstructural characterization of composites by connecting fractal characteristics with composites’ functional properties. In this study, fractal reconstructions of different microstructural shapes in an epoxy-based composite were performed on field emission scanning electron microscopy (FESEM) images. These images were of glass fiber reinforced epoxy as well as a hybrid composite containing both glass and electrospun polystyrene fibers in an epoxy matrix. Fractal reconstruction enables the identification of self-similarity in the fractal structure, which represents a novelty in analyzing the fractal properties of materials. Fractal Real Finder software, based on the mathematical affine fractal regression model, was employed to reconstruct different microstructure shapes and calculate fractal dimensions to develop a method of predicting the optimal structure–property relations in composite materials in the future.
PB  - MDPI
T2  - Fractal and Fractional
T1  - Microstructure of Epoxy-Based Composites: Fractal Nature Analysis
VL  - 6
IS  - 12
SP  - 741
DO  - 10.3390/fractalfract6120741
ER  - 

@article{
author = "Stajčić, Ivana and Stajčić, Aleksandar and Serpa, Cristina and Vasiljević-Radović, Dana and Randjelović, Branislav and Radojević, Vesna and Fecht, Hans",
year = "2022",
abstract = "Polymers and polymer matrix composites are commonly used materials with applications extending from packaging materials to delicate electronic devices. Epoxy resins and fiber-reinforced epoxy-based composites have been used as adhesives and construction parts. Fractal analysis has been recognized in materials science as a valuable tool for the microstructural characterization of composites by connecting fractal characteristics with composites’ functional properties. In this study, fractal reconstructions of different microstructural shapes in an epoxy-based composite were performed on field emission scanning electron microscopy (FESEM) images. These images were of glass fiber reinforced epoxy as well as a hybrid composite containing both glass and electrospun polystyrene fibers in an epoxy matrix. Fractal reconstruction enables the identification of self-similarity in the fractal structure, which represents a novelty in analyzing the fractal properties of materials. Fractal Real Finder software, based on the mathematical affine fractal regression model, was employed to reconstruct different microstructure shapes and calculate fractal dimensions to develop a method of predicting the optimal structure–property relations in composite materials in the future.",
publisher = "MDPI",
journal = "Fractal and Fractional",
title = "Microstructure of Epoxy-Based Composites: Fractal Nature Analysis",
volume = "6",
number = "12",
pages = "741",
doi = "10.3390/fractalfract6120741"
}

Stajčić, I., Stajčić, A., Serpa, C., Vasiljević-Radović, D., Randjelović, B., Radojević, V.,& Fecht, H.. (2022). Microstructure of Epoxy-Based Composites: Fractal Nature Analysis. in Fractal and Fractional
MDPI., 6(12), 741.
https://doi.org/10.3390/fractalfract6120741

Stajčić I, Stajčić A, Serpa C, Vasiljević-Radović D, Randjelović B, Radojević V, Fecht H. Microstructure of Epoxy-Based Composites: Fractal Nature Analysis. in Fractal and Fractional. 2022;6(12):741.
doi:10.3390/fractalfract6120741 .

Stajčić, Ivana, Stajčić, Aleksandar, Serpa, Cristina, Vasiljević-Radović, Dana, Randjelović, Branislav, Radojević, Vesna, Fecht, Hans, "Microstructure of Epoxy-Based Composites: Fractal Nature Analysis" in Fractal and Fractional, 6, no. 12 (2022):741,
https://doi.org/10.3390/fractalfract6120741 . .

TY  - JOUR
AU  - Mitić, Vojislav V.
AU  - Lazović, Goran
AU  - Ribar, Srđan
AU  - Lu, Chun-An
AU  - Radović, Ivana
AU  - Stajčić, Aleksandar
AU  - Fecht, Hans
AU  - Vlahović, Branislav
PY  - 2020
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4004
AB  - This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nanoBaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop
the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a
simple function to process input signal, as well as adjustable parameter which has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics
parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U), and relative capacitance change, from the level of the bulk sample down to the grains boundaries.
PB  - Taylor & Francis
T2  - Integrated Ferroelectrics
T1  - The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination
VL  - 212
IS  - 1
SP  - 135
EP  - 146
DO  - 10.1080/10584587.2020.1819042
ER  - 

@article{
author = "Mitić, Vojislav V. and Lazović, Goran and Ribar, Srđan and Lu, Chun-An and Radović, Ivana and Stajčić, Aleksandar and Fecht, Hans and Vlahović, Branislav",
year = "2020",
abstract = "This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nanoBaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop
the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a
simple function to process input signal, as well as adjustable parameter which has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics
parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U), and relative capacitance change, from the level of the bulk sample down to the grains boundaries.",
publisher = "Taylor & Francis",
journal = "Integrated Ferroelectrics",
title = "The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination",
volume = "212",
number = "1",
pages = "135-146",
doi = "10.1080/10584587.2020.1819042"
}

Mitić, V. V., Lazović, G., Ribar, S., Lu, C., Radović, I., Stajčić, A., Fecht, H.,& Vlahović, B.. (2020). The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination. in Integrated Ferroelectrics
Taylor & Francis., 212(1), 135-146.
https://doi.org/10.1080/10584587.2020.1819042

Mitić VV, Lazović G, Ribar S, Lu C, Radović I, Stajčić A, Fecht H, Vlahović B. The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination. in Integrated Ferroelectrics. 2020;212(1):135-146.
doi:10.1080/10584587.2020.1819042 .

Mitić, Vojislav V., Lazović, Goran, Ribar, Srđan, Lu, Chun-An, Radović, Ivana, Stajčić, Aleksandar, Fecht, Hans, Vlahović, Branislav, "The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination" in Integrated Ferroelectrics, 212, no. 1 (2020):135-146,
https://doi.org/10.1080/10584587.2020.1819042 . .