Using Artificial Neural Networks to Refine and Analyze the Results of Atomic Force Microscopy of Dispersed Filled Elastomers (Contact and Semi-Contact Modes)

Author details

Oleg K. Garishin
Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science, Perm, Russian Federation
Aleksandr K. Sokolov
Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science, Perm, Russian Federation

https://doi.org/10.15593/perm.mech/2025.3.06

The paper proposes two new applications of artificial neural networks for decoding and analyzing results of the surface of elastomer nanocomposite scanning by means of atomic force microscopy (contact and semi-contact modes). The main advantage of this approach is that it enables studying local mechanical properties of the material not only on the sample surface but also in the near-surface layer. In case of the contact operation mode of the atomic force microscope, the artificial neural network was created and "trained" using the database with the pressing models of the probe of the atomic force microscope into a nonlinear hyperelastic medium with rigid spherical inclusions ("contact neural network"). This database contained the results of calculations of indentation curves for various values of the filler particle sizes and their localization in the near-surface layer of the material (depth and horizontal distance from the tip of the probe). The use of such a neural network allows speeding up the construction of indentation curves by several orders of magnitude compared to conventional methods based on the numerical solution of the corresponding boundary value problems for each specific case. As a result, computing time is also significantly reduced, therefore, with an already constructed and "trained" neural network, powerful and high-speed computers are not needed. In the semi-contact mode, the neural network was built using real scans of the relief and phase shift of the probe cantilever oscillations obtained on samples made of a dispersed filled elastomer ("semi-contact neural network"). It was shown that with its help it is possible to quite accurately predict what the results of the semi-contact scanning will look like with an increase in the maximum value of probe indentation (i.e. with a deeper study of the near-surface layer). Further, these modified scans are supposed to be used as a basis for analyzing near-surface layers using a contact neural network.

Keywords

artificial neural networkscontact and semi-contact methods of atomic force microscopyelastomer nanocompositenonlinear elastic materialsfinite deformations.

ИСПОЛЬЗОВАНИЕ ИСКУССТВЕННЫХ НЕЙРОСЕТЕЙ ДЛЯ УТОЧНЕНИЯ И АНАЛИЗА РЕЗУЛЬТАТОВ АТОМНО-СИЛОВОЙ МИКРОСКОПИИ ДИСПЕРСНО НАПОЛНЕННЫХ ЭЛАСТОМЕРОВ (КОНТАКТНЫЙ И ПОЛУКОНТАКТНЫЙ РЕЖИМЫ)

Предложено два новых варианта применения искусственных нейронных сетей для расшифровки и углубленного анализа результатов сканирования поверхности эластомерных нанокомпозитов с помощью атомно-силовой микроскопии (контактный и полуконтактный режимы). Основное преимущество данного подхода состоит в том, что он позволяет исследовать локальные механические свойства материала не только на поверхности образца, но и в приповерхностном слое. В случае контактного режима работы атомно-силового микроскопа искусственная нейросеть создавалась и «обучалась» на основе компьютерной базы данных по численному моделированию процесса вдавливания зонда атомно-силового микроскопа в нелинейную гиперупругую среду с жесткими сферическими включениями («контактная нейросеть»). Эта база содержала результаты расчетов кривых индентирования при различных значениях размеров частиц наполнителя и их локализации в приповерхностном слое материала (глубина и горизонтальное удаление от вершины зонда). Использование такой нейросети позволило на несколько порядков ускорить процесс построения индентационных кривых по сравнению с обычными методами, основанными на численном решении соответствующих краевых задач для каждого конкретного случая. В результате значительно уменьшаются и компьютерные затраты, следовательно, при наличии уже построенной и «обученной» нейросети не нужны мощные и скоростные компьютеры. При полуконтактном режиме нейросеть строилась на основе реальных сканов рельефа и фазы сдвига колебаний кантилевера зонда, полученных на образцах из дисперсно наполненного эластомера («полуконтактная нейросеть»). Показано, что с ее помощью можно достаточно точно предсказать, как будут выглядеть результаты полуконтактного сканирования при увеличении максимальной величины вдавливания зонда (то есть при более глубоком исследовании приповерхностного слоя). Далее эти модифицированные сканы предполагается использовать как основу для анализа приповерхностных слоев с помощью контактной нейросети.

Ключевые слова

искусственные нейронные сетиконтактный и полуконтактный методы атомно-силовой микроскопииэластомерный нанокомпозитнелинейно упругие материалыконечные деформации.

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Article information

Published: 8 September 2025
Issue date: 8 September 2025
Issue: No. 3 (2025)

Using Artificial Neural Networks to Refine and Analyze the Results of Atomic Force Microscopy of Dispersed Filled Elastomers (Contact and Semi-Contact Modes)

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