"Predicting the modulus of elasticity for biocompatible titanium alloys"
Апстракт
Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys of different composition, with lower modulus of elasticity, without the presence of toxic alloying elements such as Al and V [1]. Traditional methods used to detect dependencies between parameters, as well as alloy design, are often not particularly effective and usually require large investments of time and resources. The study introduces the machine learning technique Extra Tree Regression, which, through the analysis of data from 246 biocompatible titanium alloys, identifies factors associated with reduced elastic modulus [2]. The three most influential were: specific heat and mass fraction of titanium and mass fraction of titanium silicon. Using data on the most influential factors, four-component diagrams were designed where certain alloy compositions reach a modulus of up to 54 GPa. In addition, Monte... Carlo simulations were used to demonstrate the feasibility of modeling multicomponent alloys with elastic modulus below 70 GPa.
Кључне речи:
titanium alloys / extra tree regression / Monte Carlo simulationsИзвор:
9th Conference of Young Chemists of Serbia, 2023, 165-165Издавач:
- Belgrade : Serbian Chemical Society
- Belgrade : Serbian Young Chemists’ Club
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200023 (Институт за технологију нуклеарних и других минералних сировина - ИТНМС, Београд) (RS-MESTD-inst-2020-200023)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200135 (Универзитет у Београду, Технолошко-металуршки факултет) (RS-MESTD-inst-2020-200135)
Институција/група
Institut za tehnologiju nuklearnih i drugih mineralnih sirovinaTY - CONF AU - Marković, Gordana AU - Manojlović, Vaso AU - Ruzic, Jovana AU - Sokić, Miroslav PY - 2023 UR - https://ritnms.itnms.ac.rs/handle/123456789/1169 AB - Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys of different composition, with lower modulus of elasticity, without the presence of toxic alloying elements such as Al and V [1]. Traditional methods used to detect dependencies between parameters, as well as alloy design, are often not particularly effective and usually require large investments of time and resources. The study introduces the machine learning technique Extra Tree Regression, which, through the analysis of data from 246 biocompatible titanium alloys, identifies factors associated with reduced elastic modulus [2]. The three most influential were: specific heat and mass fraction of titanium and mass fraction of titanium silicon. Using data on the most influential factors, four-component diagrams were designed where certain alloy compositions reach a modulus of up to 54 GPa. In addition, Monte Carlo simulations were used to demonstrate the feasibility of modeling multicomponent alloys with elastic modulus below 70 GPa. PB - Belgrade : Serbian Chemical Society PB - Belgrade : Serbian Young Chemists’ Club C3 - 9th Conference of Young Chemists of Serbia T1 - "Predicting the modulus of elasticity for biocompatible titanium alloys" EP - 165 SP - 165 ER -
@conference{ author = "Marković, Gordana and Manojlović, Vaso and Ruzic, Jovana and Sokić, Miroslav", year = "2023", abstract = "Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys of different composition, with lower modulus of elasticity, without the presence of toxic alloying elements such as Al and V [1]. Traditional methods used to detect dependencies between parameters, as well as alloy design, are often not particularly effective and usually require large investments of time and resources. The study introduces the machine learning technique Extra Tree Regression, which, through the analysis of data from 246 biocompatible titanium alloys, identifies factors associated with reduced elastic modulus [2]. The three most influential were: specific heat and mass fraction of titanium and mass fraction of titanium silicon. Using data on the most influential factors, four-component diagrams were designed where certain alloy compositions reach a modulus of up to 54 GPa. In addition, Monte Carlo simulations were used to demonstrate the feasibility of modeling multicomponent alloys with elastic modulus below 70 GPa.", publisher = "Belgrade : Serbian Chemical Society, Belgrade : Serbian Young Chemists’ Club", journal = "9th Conference of Young Chemists of Serbia", title = ""Predicting the modulus of elasticity for biocompatible titanium alloys"", pages = "165-165" }
Marković, G., Manojlović, V., Ruzic, J.,& Sokić, M.. (2023). "Predicting the modulus of elasticity for biocompatible titanium alloys". in 9th Conference of Young Chemists of Serbia Belgrade : Serbian Chemical Society., 165-165.
Marković G, Manojlović V, Ruzic J, Sokić M. "Predicting the modulus of elasticity for biocompatible titanium alloys". in 9th Conference of Young Chemists of Serbia. 2023;:165-165..
Marković, Gordana, Manojlović, Vaso, Ruzic, Jovana, Sokić, Miroslav, ""Predicting the modulus of elasticity for biocompatible titanium alloys"" in 9th Conference of Young Chemists of Serbia (2023):165-165.