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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">ntv</journal-id><journal-title-group><journal-title xml:lang="ru">Научно-технический вестник информационных технологий, механики и оптики</journal-title><trans-title-group xml:lang="en"><trans-title>Scientific and Technical Journal of Information Technologies, Mechanics and Optics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2226-1494</issn><issn pub-type="epub">2500-0373</issn><publisher><publisher-name>Университет ИТМО</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17586/2226-1494-2025-25-6-1197-1207</article-id><article-id custom-type="elpub" pub-id-type="custom">ntv-553</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МАТЕМАТИЧЕСКОЕ И КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MODELING AND SIMULATION</subject></subj-group></article-categories><title-group><article-title>Определение оптимального метода машинного обучения для построения прогнозных моделей микротвердости по Виккерсу керамического композитного материала гидроксиапатит-многостенные углеродные нанотрубки</article-title><trans-title-group xml:lang="en"><trans-title>Machine-learning method for the development of a Vickers microhardness predictive model of a hydroxyapatite-multi-walled carbon nanotube ceramic composite material</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7067-7979</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Резванова</surname><given-names>А. Е.</given-names></name><name name-style="western" xml:lang="en"><surname>Rezvanova</surname><given-names>A. E.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Анастасия Евгеньевна Резванова, младший научный сотрудник</p><p>634055; Томск</p><p>sc 57199302281</p></bio><bio xml:lang="en"><p>Anastasiya E. Rezvanova, Junior Researcher</p><p>634055; Tomsk</p><p>sc 57199302281</p></bio><email xlink:type="simple">ranast@ispms.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0000-5133-4893</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Кудряшов</surname><given-names>Б. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Kudryashov</surname><given-names>B. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Борис Сергеевич Кудряшов, аспирант, инженер-исследователь</p><p>634055; Томск</p><p>sc 57656690100</p></bio><bio xml:lang="en"><p>Boris S. Kudryashov, PhD Student, Engineer-Researcher</p><p>634055; Tomsk</p><p>sc 57656690100</p></bio><email xlink:type="simple">bsk3@ispms.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0002-7191-163X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Погудин</surname><given-names>В. Ю.</given-names></name><name name-style="western" xml:lang="en"><surname>Pogudin</surname><given-names>V. Yu.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Владимир Юрьевич Погудин, инженер,</p><p>634055; 634050; Томск</p></bio><bio xml:lang="en"><p>Vladimir Yu. Pogudin, Engineer</p><p>634055; 634050; Tomsk</p></bio><email xlink:type="simple">pogudin.vova@bk.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Институт физики прочности и материаловедения Сибирского отделения Российской академии наук</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Institute of Strength Physics and Materials Science of the Siberian Branch of the Russian Academy of Sciences</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Институт физики прочности и материаловедения Сибирского отделения Российской академии наук; Томский государственный университет систем управления и радиоэлектроники</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Institute of Strength Physics and Materials Science of the Siberian Branch of the Russian Academy of Sciences; Tomsk State University of Control Systems and Radioelectronics</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>23</day><month>12</month><year>2025</year></pub-date><volume>25</volume><issue>6</issue><fpage>1197</fpage><lpage>1207</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Резванова А.Е., Кудряшов Б.С., Погудин В.Ю., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Резванова А.Е., Кудряшов Б.С., Погудин В.Ю.</copyright-holder><copyright-holder xml:lang="en">Rezvanova A.E., Kudryashov B.S., Pogudin V.Y.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://ntv.elpub.ru/jour/article/view/553">https://ntv.elpub.ru/jour/article/view/553</self-uri><abstract><sec><title>   Введение</title><p>   Введение. Долговечность и износостойкость керамических имплантатов, используемых в условиях высоких эксплуатационных нагрузок, в значительной степени зависят от их механических характеристик. Керамический композит на основе гидроксиапатита рассматривается как перспективный биоматериал для реконструкции поврежденных костных тканей и замещения костных дефектов благодаря своей биосовместимости и способности обеспечивать остеоинтеграцию с костной тканью. Для повышения механической прочности предлагается армировать керамику на основе гидроксиапатита многостенными углеродными нанотрубками, обладающими высокими физико-механическими характеристиками. Данный подход направлен на применение материалов в зонах имплантации, испытывающих значительные механические нагрузки. Эффективность армирования нанотрубками во многом зависит от состава композита, технологии синтеза и условий испытаний, что приводит к высокой вариативности итоговых характеристик. Прямое экспериментальное определение свойств каждого образца требует значительных временных затрат. Для оптимизации процесса исследования механических характеристик композитных материалов представляется актуальным использование математических моделей, основанных на методах машинного обучения, что позволяет прогнозировать микротвердость по Виккерсу в зависимости от нагрузки индентирования.</p></sec><sec><title>   Метод</title><p>   Метод. Экспериментальные испытания на микротвердость по Виккерсу для шести серий керамических образцов проводились методом индентирования при нагрузках в диапазоне от 0,98 Н до 9,8 Н. Для прогнозирования полученных данных были применены три метода машинного обучения: нейронная сеть, метод случайного леса и градиентный бустинг.</p></sec><sec><title>   Основные результаты</title><p>   Основные результаты. В результате исследования после усреднения значений по всем нагрузкам индентирования для каждого образца определено, что с увеличением концентрации многостенных углеродных нанотрубок до 0,5 масс. % микротвердость композита по сравнению с гидроксиапатитом без добавок возрастает от 3,83 ± 0,39 ГПа до 4,71 ± 0,40 ГПа, а армирование становится эффективным на 19 %. Таким образом, наибольший вклад в повышение микротвердости композита внесли добавки с концентрацией 0,5 масс. %, при этом добавление 1 и 2 масс. % привело к значительному снижению микротвердости, что связано с возникновением агломерации нанотрубок в керамической матрице.</p></sec><sec><title>   Обсуждение</title><p>   Обсуждение. Результаты моделирования позволили на основе данных экспериментального исследования определить оптимальный метод машинного обучения для построения прогнозной модели микротвердости композитной керамики гидроксиапатит-многостенные углеродные нанотрубки в широком диапазоне нагрузок, а также установить взаимосвязь между составом композита и его механическими характеристиками, что открывает новые возможности для проектирования прочных и долговечных керамических имплантатов.</p></sec></abstract><trans-abstract xml:lang="en"><p>   The durability and wear resistance of ceramic implants, used under high operating loads largely, depend on their mechanical characteristics. Ceramic composite based on hydroxyapatite is considered as a promising biomaterial for reconstruction of damaged bone tissues and replacement bone defects due to its biocompatibility and ability to provide osseointegration with bone tissue. In this work, to increase mechanical strength, the hydroxyapatite ceramics was reinforced by multi-walled carbon nanotubes additives which have high physical and mechanical properties. The potential of such research lies in the use of these composites in implantation areas that experience significant mechanical loads. The effectiveness of nanotubes reinforcement depends on the ceramics composition, synthesis technology, and testing conditions, resulting in high variability in the final characteristics. At the same time, direct experimental study of the properties of each sample requires significant time-cost. The use of mathematical models based on machine learning methods to optimize the process of analysis the mechanical characteristics of composite materials is relevant study. This will allow us to predict Vickers microhardness depending on the indentation load. In this study, experimental microhardness tests were carried out by using Vickers method for six sets of ceramic materials which were exposed to indentation loads ranging from 0.98 N to 9.8 N. Three machine learning methods were used to predict the data obtained: neural network, random forest and gradient boosting. After averaging the values for all loads, it was determined that with the increasing the concentration of multi-walled carbon nanotubes to 0.5 wt. % the Vickers microhardness of the composite increases from 3.83 ± 0.39 GPa to 4.71 ± 0.40 GPa compared to hydroxyapatite without additives, and reinforcement becomes effective by 19 %. Thus, the greatest contribution to the increase in the microhardness of the composite was made by the additives of multi-walled carbon nanotubes with a concentration of 0.5 wt.%, while the addition of 1 and 2 wt.% led to a significant decrease in microhardness, which is associated with the appearance of multi-walled carbon nanotubes agglomeration in the ceramic hydroxyapatite matrix. The simulation results based on experimental data allowed us to determine the optimal machine learning method for constructing a predictive model of the microhardness of hydroxyapatite - multi-walled carbon nanotubes ceramic composite in a wide range of loads. In addition, it was possible to establish the relationship between the composition of the composite and its mechanical characteristics, which opens up new possibilities for designing strong and durable ceramic implants.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>гидроксиапатит</kwd><kwd>многостенные углеродные нанотрубки</kwd><kwd>микротвердость по Виккерсу</kwd><kwd>прогнозирование</kwd><kwd>методы регрессии</kwd><kwd>машинное обучение</kwd></kwd-group><kwd-group xml:lang="en"><kwd>hydroxyapatite</kwd><kwd>multi-walled carbon nanotubes</kwd><kwd>Vickers microhardness</kwd><kwd>prediction</kwd><kwd>regression methods</kwd><kwd>machine learning</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Исследование выполнено за счет гранта Российского научного фонда № 25-23-20233 (https://rscf.ru/ project/25-23-20233/) и гранта в форме субсидии, выделяемого Департаментом по научно-технологическому развитию и инновационной деятельности Томской области (Соглашение № 02/5/2025). Исследования выполнены с использованием оборудования Центра коллективного пользования «Нанотех» Института физики прочности и материаловедения Сибирского отделения РАН</funding-statement><funding-statement xml:lang="en">This research was carried out with support from the Russian Science Foundation grant No. 25-23-20233 (https://rscf.ru/ project/25-23-20233/) and a subsidy grant allocated by the Department of Scientific, Technological Development and Innovative Activities of the Tomsk Region (Agreement No. 02/5/2025). The authors like to express their gratitude towards the management of Core Facility Centre “Nanotech” of the Institute of Strength Physics and Materials Science of the SB RAS for equipment employed in these studies</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Fiume E., Magnaterra G., Rahdar A., Verné E., Baino F. 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