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Record W2028110202 · doi:10.1179/026708300101507730

Indentation behaviour and wear resistance of pseudoelastic Ti–Ni alloy

2000· article· en· W2028110202 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Science and Technology · 2000
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsUniversity of Alberta
FundersSyncrude
KeywordsPseudoelasticityMaterials scienceAlloyMetallurgyShape-memory alloyUltimate tensile strengthDiffusionless transformationIndentationTensile testingMartensiteThermoelastic dampingComposite materialMicrostructureThermalThermodynamics

Abstract

fetched live from OpenAlex

Recent studies demonstrate that near equiatomic Ti–Ni alloys possess high resistance to surface damage by wear. It is suggested that the high wear resistance of Ti–Ni alloys is closely correlated to their pseudoelasticity, which is usually evaluated by tensile testing. However, when a Ti–Ni alloy is under wear, its surface is in a complex stress state. Since the thermoelastic martensitic transformation of Ti–Ni alloys responds differently to different stresses, it may not be appropriate to evaluate the pseudoelasticity by tensile testing. The present paper reports recent work on pseudoelastic behaviour of a Ti–51 at.-%Ni alloy employing a microindentation technique as well as tensile testing methods. In the present work, the wear performances of Ti–51 at.-%Ni alloy specimens with different degrees of pseudoelasticity were also investigated, and efforts were made to explain the beneficial effect of pseudoelasticity on the wear resistance of Ti–Ni alloys.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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