Physical and chemical aspects of the study of clusters, nanostructures and nanomaterials
Founded at 2009


Pore percentage estimation of piezoelectric ceramics using CCANN and image made with SEM

D.V. Mamaev, S.A. Merkuryev, O.V. Malyshkina

Tver State University

DOI: 10.26456/pcascnn/2021.13.286

Original article

Abstract: The authors synthesized samples of piezoelectric potassium sodium niobate ceramics of 10,25 and 40 pore percentage by volume. Capsule convolutional artificial neural network has been developed for estimation of the pore percentage in images. Using the scanning electron microscopy, f learning massive of examples was formed (photographs of surface and edges of so-synthesized samples). Development and approbation of the capsule convolutional artificial neural network was completed in a few stages. During the first stage, the network was trained using a backpropagation method. Secondly, it was tested by a testing set. At the final stage we made a comparison of the acquired results with the results of the density comparing method. The article shows that this method can be used the pore percentage estimation in sodium niobate ceramics because the acquired results are comparable with the results of other methods. It was found that the samples where the pores were not made also have some pore percentage (about 5%).

Keywords: piezoelectric ceramics, capsule convolutional artificial neural network, artificial neural networks, pore percentage

  • Danila V. Mamaev – 2nd year postgraduate student, Tver State University
  • Sergey A. Merkuryev – 2nd year postgraduate student, Tver State University
  • Olga V. Malyshkina – Dr. Sc., Full Professor, Head of the Department of Dissertation Councils and Doctorate Studies, Scientific Research Department, Tver State University

Reference:

Mamaev, D.V. Pore percentage estimation of piezoelectric ceramics using CCANN and image made with SEM / D.V. Mamaev, S.A. Merkuryev, O.V. Malyshkina // Physical and chemical aspects of the study of clusters, nanostructures and nanomaterials. – Tver: TSU, 2021. — I. 13. — P. 286-293. DOI: 10.26456/pcascnn/2021.13.286. (In Russian).

Full article (in Russian): download PDF file

References:

1. Golovnin V.А., Kaplunov I.А., Ped'ko B.B., Malyshkina O.V., Movchikova А.А. Fizicheskie osnovy, metody issledovaniya i prakticheskoe primenenie p'ezomaterialov [Physical foundations, research methods and practical application of piezomaterials]. – Moscow, TEKHNOSFERА Publ., 2013. – 272 p. (In Russian).
2. Directive 2002/95/EC of the European Parliament and of the Council of 27 January 2003 on the restriction of the use of certain hazardous substances in electrical and electronic, Official Journal of the European Union L 37, 2003, vol. 46, pp. 19-23.
3. Ivanenko V.I, Lokshin E.P., Gromov O.G., Kalinnikov V.T. Sintez segnetoelektricheskikh i lyuminestsentnykh slozhnykh oksidov redkikh elementov [Synthesis of ferroelectric and luminescent complex oxides of rare elements]. Apatity: Kola Science Center of RAS Publ., 2009, 153 p. (In Russian).
4. Rödel, J., Webber K.G., Dittmer R. et al. Transferring lead-free piezoelectric ceramics into application, Journal of the European Ceramic Society, 2015, vol. 35, issue 6, pp. 1659-1681. DOI:10.1016/j.jeurceramsoc.2014.12.013.
5. Saito Y., Takao H., Tani T. et al. Lead-free piezoceramics, Nature, 2004, vol. 432, issue 7013, pp. 84-87. DOI: 10.1038/nature03028.
6. Bernard J., Benčan A., Rojac T. et al. Low-temperature sintering of K0,5Na0,5NbO3 ceramics, Journal of The American Ceramic Society, 2008, vol. 91, issue 7, pp. 2409-2411. DOI: 10.1111/j.1551-2916.2008.02447.x.
7. Sun W., Tagliasacchi A., Deng B. et al. Unsupervised capsules in canonical pose. Available at: https://arxiv.org/pdf/2012.04718.pdf (accessed 25.06.2021).
8. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet classification with deep convolutional neural networks advances in neural information processing, Communications of the ACM, 2017, vol. 60, issue 6, pp. 84-90. DOI: 10.1145/3065386.
9. Haykin S.S. Neural networks: A comprehensive foundation, 2nd ed. New-Jersey, Prentice Hall, 1999, XXI, 842 p.
10. Gorban' A.N. Obuchenie nejronnykh setej [Neural network training]. Moscow, ParaGraf Publ., 1990, 160 p. (In Russian).
11. Lillicrap T.P., Santoro A., Marris L., Akerman C.J., Hinton G. Backpropagation and the brain, Nature Reviews, Neuroscience, 2020, vol. 21, issue 6, pp.335-346. DOI: 10.1038/s41583-020-0277-3.

⇐ Prevoius journal article | Content | Next journal article ⇒