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Record W2107246916 · doi:10.1243/09544119jeim110

The effect of microstructure on the wear of cobalt-based alloys used in metal-on-metal hip implants

2006· article· en· W2107246916 on OpenAlexaff
R. Varano, J Bobyn, John B. Medley, S. Yue

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of WaterlooMontreal General HospitalMcGill University
Fundersnot available
KeywordsMaterials scienceMicrostructureMetallurgyCobaltLubricantAlloyMolybdenumCarbideAnnealing (glass)Carbon fibersMetalComposite material

Abstract

fetched live from OpenAlex

The influence of microstructure on the wear of cobalt-based alloys used in metal-on-metal hip implants was investigated in a boundary lubrication regime designed to represent the conditions that occurred some of the time in vivo. These cobalt-chromium-molybdenum alloys were either wrought, with a total carbon content of 0.05 or 0.23 wt %, cast with a solution-annealing procedure or simply as-cast but not solution annealed. Bars of these different alloy grades were subjected to various heat treatments to develop different microstructures. The wear was evaluated in a linear-tracking reciprocating pin-on-plate apparatus with a 25 per cent bovine serum lubricant. The wear was found to be strongly affected by the dissolved carbon content of the alloys and mostly independent of grain size or the carbide characteristics. The increased carbon in solid solution caused reductions in volumetric wear because carbon helped to stabilize a face-centred cubic crystal structure, thus limiting the amount of strain-induced transformation to a hexagonal close-packed crystal structure. Based on the observed surface twining in and around the contact zone and the potentially detrimental effect of the hexagonal close-packed phase, it was postulated that the wear of cobalt-based alloys in the present study was controlled by a deformation mechanism.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.230
Teacher spread0.221 · 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 teacher head, 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

Citations62
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicOrthopaedic implants and arthroplastyFrench-language works237,207