THERMAL SCIENCE
International Scientific Journal
SINTERING TEMPERATURE INFLUENCE ON GRAINS FUNCTION DISTRIBUTION BY NEURAL NETWORK APPLICATION
ABSTRACT
Artificial neural networks application in science and techonology begun during 20th century. This biophysical and biomimetic phenomena is based on extensive research which have led to understanding how neural as a living organism nerve system basic element processes signals by a simple algorithm. The input signals are massively parallel processed, and the output presents the superposition of all parallel processed signals. Artificial neural networks which are based on these principles are useful for solving various problems as pattern recognition, clustering, functional optimization. This research analyzed thermophysical parameters at samples based on Murata powders and consolidated by sintering process. Among different physical properties we applied out neural network approach on grain sizes distribution as a function of sintering temperature, T, (from 1190-1370°C). In this paper, we continue to apply neural networks to prognose structural and thermophysical parameters. For consolidation sintering process is very important to prognose and design many parameters but especially thermal like temperature, to avoid long and even wrong experiments which are wasting the time and materials and energy as well. By this artificial neural networks method we indeed provide the most efficient procedure in projecting the mentioned parameters and provide successful ceramics samples production. This is very helpful in prediction and designing the micro-structure parameters important for advance microelectronic further miniaturization development. This is a quite original novelty for real micro-structure projecting especially on the phenomena within the thin films coating around the grains what opens new prospective in advance fractal microelectronics.
KEYWORDS
PAPER SUBMITTED: 2021-04-20
PAPER REVISED: 2021-06-15
PAPER ACCEPTED: 2021-06-22
PUBLISHED ONLINE: 2021-10-10
THERMAL SCIENCE YEAR
2022, VOLUME
26, ISSUE
Issue 1, PAGES [299 - 307]
- Farahani F., et al., Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning Elsevier 2018, Vol 149, pp. 48-68, Mathematics and Computers in Simulation Multilayer Perceptron
- Tien Bui, D., et al. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14, pp. 447-458 (2017). doi.org/10.1007/s10346-016-0711-9
- Wang Y., et al., A hybrid intelligent approach for constructing landslide displacement prediction intervals, Applied Soft Computing, Volume 81, 2019, 105506,ISSN 1568-4946 doi.org/10.1016/j.asoc.2019.105506
- Prakash S.,, et al., Neuro fuzzy hybrid intelligent approach for four -area load frequency control of interconnected power system, 2012 2nd International Conference on Power, Control and Embedded Systems, 2012, pp. 1-7, doi: 10.1109/ICPCES.2012.6508124.
- Fazeli H., et al. Experimental Study and Modeling of Ultrafiltration of Refinery Effluents Using a Hybrid Intelligent Approach, Energy & Fuels 2013 27 (6), 3523-3537 DOI: 10.1021/ef400179b
- Rumelhart, D., et al. Learning representations by back-propagation errors, Nature, 323, (1986) pp. 533-536
- da Silva I.N., et al. Artificial Neural Network Architectures and Training Processes, in: Artificial Neural Networks. Springer, Cham., 2017, pp.21-28
- Ribar S. et al. Neural Networks Application on Human Skin Biophysical Impedance Characterisations, Biophysical Reviews & Letters, 16,(2021), pp. 2021 - 19
- Hecht-Nielsen: Theory of the backpropagation neural network, International Joint Conference on Neural Networks, IJCNN, Washington, D. C.,USA, 1989. Vol. 1, pp. 593-605
- Mitic V. V., et al. The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination, Integrated Ferroelectics, 212 ,(2020)
- Mitic V. V., et al. Neural Networks and Microelectronics Parameters Distribution Measurements Depending on Sintering Temperature and Applied Voltage, Modern Physics Letters B, 34, (2020)
- Mitic V. V., et al. Ceramics, Materials, Microelectronics and Graph Theory New Frontiers Modern Physics Letters B, 34, (2020)
- Mitic V. V., et al. Entropy and fractal nature, Thermal Science, 24 (2020) 3B pp. 2203-2212.
- Mitic V. V., et al., Butler-Volmer Current Equation and Fractal Nature Correction in Electrochemical Energy, Thermal Science, (2020), p. 232-232,
- Randjelovic B. Graph Theory Approach in Synthetized Diamonds Electrophysical Parameters Defining, in: Bioceramics, Biomimetic and other Compatible Materials Features for Medical Applications, (Ed. Najman S., Mitic V., Groth T., Barbeck M., Yu Chen P., Sun Z.), Springer Nature, Cham, Switzerland, 2021, accepted for publication
- Ribar S., Neural Networks from Biophysical Applications in Microelectronics Parameters Measurements, in: Bioceramics, Biomimetic and other Compatible Materials Features for Medical Applications, (Ed. Najman S., Mitic V., Groth T., Barbeck M., Yu Chen P., Sun Z.), Springer Nature, Cham, Switzerland, 2021, accepted for publication
- Mitic V.V., Structure and electrical properties of BaTiO3 ceramics, (in serbian), Zaduzbina Andrejevic, Belgrade, Serbia, 2001
- Ilić I., On tail index estimation using a sample with missing observations, Statistics & Probability Letters,82, (5), (2012) pp. 949-958
- Ilić I., Veličković V., Simple tail index estimation for dependent and heterogeneous data with missing values, Brazilian Journal of Probability and Statistics,33, (1), (2019) pp. 192-203
- Ilić I., Veličković V., A process of project quality improvement, Filomat 33 (6), (2019) pp. 1833-1844
