Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys

Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a me...
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Autor*in:	Xingjun Liu [verfasserIn] Qinghua Peng [verfasserIn] Shaobin Pan [verfasserIn] Jingtao Du [verfasserIn] Shuiyuan Yang [verfasserIn] Jiajia Han [verfasserIn] Yong Lu [verfasserIn] Jinxin Yu [verfasserIn] Cuiping Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	biomedical titanium alloys machine learning Young’s modulus microstructures β-phase

Übergeordnetes Werk:	In: Metals - MDPI AG, 2012, 12(2022), 5, p 796
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:5, p 796

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/met12050796

Katalog-ID:	DOAJ031140343

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language	English
source	In Metals 12(2022), 5, p 796 volume:12 year:2022 number:5, p 796
sourceStr	In Metals 12(2022), 5, p 796 volume:12 year:2022 number:5, p 796
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	biomedical titanium alloys machine learning Young’s modulus microstructures β-phase Mining engineering. Metallurgy
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container_title	Metals
authorswithroles_txt_mv	Xingjun Liu @@aut@@ Qinghua Peng @@aut@@ Shaobin Pan @@aut@@ Jingtao Du @@aut@@ Shuiyuan Yang @@aut@@ Jiajia Han @@aut@@ Yong Lu @@aut@@ Jinxin Yu @@aut@@ Cuiping Wang @@aut@@
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callnumber-first	T - Technology
author	Xingjun Liu
spellingShingle	Xingjun Liu misc TN1-997 misc biomedical titanium alloys misc machine learning misc Young’s modulus misc microstructures misc β-phase misc Mining engineering. Metallurgy Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys
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topic_title	TN1-997 Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys biomedical titanium alloys machine learning Young’s modulus microstructures β-phase
topic	misc TN1-997 misc biomedical titanium alloys misc machine learning misc Young’s modulus misc microstructures misc β-phase misc Mining engineering. Metallurgy
topic_unstemmed	misc TN1-997 misc biomedical titanium alloys misc machine learning misc Young’s modulus misc microstructures misc β-phase misc Mining engineering. Metallurgy
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title	Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys
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title_full	Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys
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title_sort	machine learning assisted prediction of microstructures and young’s modulus of biomedical multi-component β-ti alloys
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title_auth	Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys
abstract	Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component β-Ti alloys with low moduli. Prediction models of microstructures and Young’s moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti–13Nb–12Ta–10Zr–4Sn (wt.%) alloy with a single β-phase microstructure and Young’s modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials.
abstractGer	Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component β-Ti alloys with low moduli. Prediction models of microstructures and Young’s moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti–13Nb–12Ta–10Zr–4Sn (wt.%) alloy with a single β-phase microstructure and Young’s modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials.
abstract_unstemmed	Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component β-Ti alloys with low moduli. Prediction models of microstructures and Young’s moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti–13Nb–12Ta–10Zr–4Sn (wt.%) alloy with a single β-phase microstructure and Young’s modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials.
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title_short	Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys
url	https://doi.org/10.3390/met12050796 https://doaj.org/article/ff36a07c95134594b3c3965aa4cd65fa https://www.mdpi.com/2075-4701/12/5/796 https://doaj.org/toc/2075-4701
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