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Carbides and their Role in Advanced Mechanical Properties of L605 Alloy: Implications for Medical Devices

2014· article· en· W2014536743 on OpenAlexaff
Fan Sun, Diego Mantovani, F. Prima

Bibliographic record

VenueMaterials science forum · 2014
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceCarbideAlloyMetallurgyNucleationAnnealing (glass)Grain boundaryGrain sizeRecrystallization (geology)Microstructure

Abstract

fetched live from OpenAlex

L605 (ASTM F90), a cobalt-chromium-tungsten alloy with excellent mechanical properties and high radiopacity, has been widely accepted as a suitable alloy for stent applications. The presence of carbides in this alloy, primary carbides and secondary carbides, leads to difficulties in controlling mechanical performances and therefore in optimizing stent size and performances. This work is thus to investigate the carbides and their role in advanced mechanical properties of L605 alloy for stent fabrication. Herein, the nature, nucleation, distribution and dissolution of the carbides were investigated in a series of recrystallized L605 tubes from hard-drawn (HD) state. The mechanical properties corresponding to each carbide state were examined by tensile tests and microhardness measurements. The results indicate important relationships among carbide precipitation, grain size and mechanical behaviors, as a function of annealing temperature and duration. The intergranular secondary carbides, induced at the onset of the recrystallization of L605 matrix, were preferentially precipitated at grain boundaries. The nucleation of such particulate phase leads to a pinning effect on grain coarsening, resulting in a strengthening effect of the material. However, the further growth of the secondary carbides brings about considerable reduction of ductility, which is inacceptable for stent application. Therefore, an optimization protocol on carbides controlling was developed to maintain the strengthening effect without losing ductility and small grain size.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2014
Admission routes1
Has abstractyes

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