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Record W876147556 · doi:10.1520/stp38289s

Development of Pseudoelastic TiNi Tribo Materials

2001· book-chapter· en· W876147556 on OpenAlexaff
Li Dy

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPseudoelasticityMaterials scienceMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

It has recently been demonstrated that TiNi shape memory alloy exhibits high resistance to wear and could be a superior tribo material. Excellent wear performance of this alloy benefits by its pseudoelasticity, resulting from a reversible martensitic transformation. Extensive research was conducted to investigate wear behavior of TiNi alloy during various wear processes, including sliding wear, erosion and corrosive wear. Friction of this alloy was also investigated with the emphasis on pseudoelastic effects on friction. The research manifests that this novel wear-resistant alloy is multi-functional and can be an excellent candidate for various tribological applications. Attempts have also been made to develop tribo composites using TiNi alloy as the matrix, reinforced by TiC, TiN and nano-TiN particles, respectively. The TiNi-matrix composites possess considerably improved wear resistance. This paper presents a brief review of our studies on the development of novel TiNi-based tribo materials.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.803
Threshold uncertainty score0.983

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.001

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 head0.021
GPT teacher head0.202
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2001
Admission routes1
Has abstractyes

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