MétaCan
Menu
Back to cohort
Record W2115896125 · doi:10.1088/0964-1726/21/4/045018

Enhanced thermomechanical functionality of a laser processed hybrid NiTi–NiTiCu shape memory alloy

2012· article· en· W2115896125 on OpenAlexaff
Matthew Daly, A. Pequegnat, Y. Zhou, Muhammad Khan

Bibliographic record

VenueSmart Materials and Structures · 2012
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsShape-memory alloyNickel titaniumMaterials scienceLaserThermomechanical processingAlloyComposite materialMetallurgyMechanical engineeringOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

The exciting thermomechanical behavior of NiTi shape memory alloys (SMAs) has sparked significant research effort seeking to exploit their exotic properties. The performance capabilities of conventional NiTi offerings are limited, however, by current fabrication technologies. In this study, a high power density laser source was implemented to locally alloy Cu into a conventional NiTi material. The effects of laser processing created a localized NiTiCu ternary material domain which possessed a set of unique thermomechanical properties. The combined active responses of the laser processed hybrid NiTi‐NiTiCu SMA represent an enhanced material functionality, which permits a multi-stage thermomechanical recovery and allows for unprecedented novel applications to be realized. (Some figures may appear in colour only in the online journal)

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 head0.017
GPT teacher head0.246
Teacher spread0.228 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSmart Materials and StructuresSame topicShape Memory Alloy TransformationsFrench-language works237,207