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Machine-learning method for the development of a Vickers microhardness predictive model of a hydroxyapatite-multi-walled carbon nanotube ceramic composite material

Анастасия Евгеньевна Резванова,Борис Сергеевич Кудряшов,Владимир Юрьевич Погудин,Anastasiya E. Rezvanova,B. S. Kudryashov,V. Y. Pogudin

Published 2025 in Scientific and Technical Journal of Information Technologies, Mechanics and Optics

ABSTRACT

   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.

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