Elastic Properties of As-Solidified Ti-Zr Binary Alloys for Biomedical Applications

Shiraishi, Takashi; Shishido, Toetsu; Shinozaki, Nobuya

doi:10.2320/matertrans.mi201501

Cited by 28 publications

(11 citation statements)

References 20 publications

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“…According to the authors, Ti-60Zr alloys present an elastic modulus of 89 GPa, with a relatively low shear modulus, 33 GPa. 39 Recently explored Ti-Zr-Ta ternary alloys close to this composition displayed an impressive yield strength, higher than 1 GPa. 40 The second region of interest is located at near 80% Ti.…”

Section: U C Cmentioning

confidence: 99%

“…In a recent assessment, Shiraishi et al identified a minimum Young modulus for the Ti-Zr binary system close to this region. According to the authors, Ti-60Zr alloys present an elastic modulus of 89 GPa, with a relatively low shear modulus, 33 GPa . Recently explored Ti-Zr-Ta ternary alloys close to this composition displayed an impressive yield strength, higher than 1 GPa .…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

Salvador

Zornio

Miranda

2020

ACS Appl. Mater. Interfaces

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The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus (K) and the shear modulus (G) of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for K and G based on compositional features. The models were then used to predict the resultant Young modulus (E) for all possible compositions in the Ti-Nb-Zr system, with variations in the composition of 2 at. %. Random forest (RF) predictions of E deviate from the NN predictions by less than 4 GPa, which is within the expected variance from the ML training phase. RF regressors seem to generate the most reliable models, given the selected target variables and descriptors. Optimal compositions identified by the ML models were later investigated with the aid of special quasi-random structures (SQSs) and density functional theory (DFT). According to a combined analysis, alloys with 22 Zr (at. %) are promising structural materials to the biomedical field, given their low elastic modulus and elevated beta-phase stability. In alloys with Nb content higher than 14.8 (at. %), the beta phase has lower energy than omega, which may be enough to avoid the formation of omega, a high-modulus phase, during manufacturing.

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Section: U C Cmentioning

confidence: 99%

Section: Machine Learning Modelsmentioning

confidence: 99%

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

Salvador

Zornio

Miranda

2020

ACS Appl. Mater. Interfaces

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“…The observed misfit shows an increase in the lattice parameter a α″ from the expected value of 0.288 nm to the observed value of 0.298 nm, and a decrease in both b α″ and c α″ lattice parameters from 0.526 nm to 0.492 nm and 0.473 nm to 0.463 nm, respectively. These kinds of differences are observed in many other titanium-based alloys containing Nb [ 69 ], Zr [ 70 , 71 ], Ta [ 72 , 73 ], V [ 74 , 75 ] and Mo [ 67 , 74 ], all of which show a continuous variation of the network parameters with increasing concentration of dissolved elements.…”

Section: Resultsmentioning

confidence: 99%

Evolution of Microstructural and Mechanical Properties during Cold-Rolling Deformation of a Biocompatible Ti-Nb-Zr-Ta Alloy

et al. 2022

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In this study, a Ti-32.9Nb-4.2Zr-7.5Ta (wt%) titanium alloy was produced by melting in a cold crucible induction in a levitation furnace, and then deforming by cold rolling, with progressive deformation degrees (thickness reduction), from 15% to 60%, in 15% increments. The microstructural characteristics of the specimens in as-received and cold-rolled conditions were determined by XRD and SEM microscopy, while the mechanical characteristics were obtained by tensile and microhardness testing. It was concluded that, in all cases, the Ti-32.9Nb-4.2Zr-7.5Ta (wt%) showed a bimodal microstructure consisting of Ti-β and Ti-α″ phases. Cold deformation induced significant changes in the microstructural and the mechanical properties, leading to grain-refinement, crystalline cell distortions and variations in the weight-fraction ratio of both Ti-β and Ti-α″ phases, as the applied degree of deformation increased from 15% to 60%. Changes in the mechanical properties were also observed: the strength properties (ultimate tensile strength, yield strength and microhardness) increased, while the ductility properties (fracture strain and elastic modulus) decreased, as a result of variations in the weight-fraction ratio, the crystallite size and the strain hardening induced by the progressive cold deformation in the Ti-β and Ti-α″ phases.

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“…For example, Zr-based Ti-60Zr (at.%) alloy exhibited the lowest Young's modulus in the Ti-Zr binary alloys. [68] Hisata et al [26d] investigated the microstructure of (Zr-Ti)-(0-8)Nb (at.%). The phase changes with increasing Nb content as follows: α′ → α″ + α′ + β → β + α″ → β.…”

Section: Dense Zr-alloysmentioning

confidence: 99%

Zirconium Alloys for Orthopaedic and Dental Applications

Mehjabeen

Song

et al. 2018

Adv Eng Mater

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Elastic Properties of As-Solidified Ti-Zr Binary Alloys for Biomedical Applications

Cited by 28 publications

References 20 publications

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

Evolution of Microstructural and Mechanical Properties during Cold-Rolling Deformation of a Biocompatible Ti-Nb-Zr-Ta Alloy

Zirconium Alloys for Orthopaedic and Dental Applications

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