Affiliation:
1. National Research Center “Kurchatov Institute” – Federal State Unitary Enterprise “All-Russian Scientific Research Institute of Aviation Materials”
2. Institute of Chemistry of New Materials of the National Academy of Sciences of Belarus
Abstract
Based on the analysis of the literature on the possibility of using neural networks to create new materials with high functional properties, the article considers a solution to the problem of determining the operational stability of polymeric composite materials by creating physical and chemically sound mathematical prediction models. Epoxy resins of the UP-637 and EA brands with an isophorone diamine hardener were chosen as the matrix of the model composite material, and oligobutadiene rubber of the SKN-10 KTR brand was chosen as the modifier. It justifies directions of work necessary for development of new materials creation methodology with optimal characteristics, building a model for changing the properties of materials at variation of composition and implementation of full-scale mathematical modeling of physical and chemical processes of polymer composite materials aging at changing level and time of climatic factors influence. Verification of the obtained dependence of service characteristics on the composition of the material and the level of influencing climatic factors was carried out on the basis of data from full-scale tests in a temperate climate. The proposed methodology for modelling the properties of polymer composite materials will reduce the development time of new materials and allow creation of polymer composites based on epoxy resin containing fillers of various natures (carbon, mineral and polymer) with high performance parameters.
Publisher
Publishing House Belorusskaya Nauka
Reference15 articles.
1. Lyukshin B. A., Shil’ko S. V., Panin S. V., Mashkov Yu. K., Kornienko L. A., Lyukshin P. A., Pleskachevskii Yu. M. [et al.]. Dispersed-filled polymer composites for technical and medical purposes. Novosibirsk, Publ. House of the Siberian Branch of the Russian Academy of Sciences, 2017. 311 p. (in Russian).
2. Vdovin D., Abramochkin A., Borodulin A., Nelyub V. Method of Predicting the Polymer Composites’ Properties Using Neural Network Modeling. MATEC Web of Conferences (ICMTMTE 2021), 2021, vol. 346, no. 2, art. ID 02015. https://doi.org/10.1051/matecconf/202134602015
3. Kumar J. N., Qianxiao Li, Tang K. Y. T., Buonassisi T., Gonzalez-Oyarce A. L., Jun Ye. Machine learning enables polymer cloud-point engineering via inverse design. npj Computational Materials, 2019, vol. 5, art. ID 73. https://doi.org/10.1038/s41524-019-0209-9
4. Kumar J. N., Qianxiao Li, Ye Jun. Challenges and opportunities of polymer design with machine learning and high throughput experimentation, Communications, 2019, vol. 9, pp. 537–544. https://doi.org/10.1557/mrc.2019.54
5. Xie T., Grossman J. C. Hierarchical visualization of materials space with graph convolutional neural networks. The Journal of Chemical Physics, 2018, vol. 149, no. 17, art. ID 174111. https://doi.org/10.1063/1.5047803