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Record W4398235537 · doi:10.1177/13621718241253490

Micro-resistance welding of NiTi shape memory alloy wire and MP35N wire

2024· article· en· W4398235537 on OpenAlexafffund
Tetsuya OYAMADA, Kaiping Zhang, Peng Peng, Y. Zhou

Bibliographic record

VenueScience and Technology of Welding & Joining · 2024
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNippon Steel Corporation
KeywordsNickel titaniumShape-memory alloyMaterials scienceMetallurgyWeldingAlloyComposite material

Abstract

fetched live from OpenAlex

The micro-resistance welding between NiTi shape memory alloy and Co-35Ni-20Cr-10Mo alloy (MP35N) wires was investigated to gage the feasibility of their future applications. Welding current and electrode force significantly affected the joint breaking force. The highest joint breaking force (0.99 kgf) at the optimized welding parameter (0.51 kA, 7.500 kg) was accomplished by maximizing the solid-state bonded area and minimizing the heat-affected zone (HAZ). This highest joint breaking force was higher than that of the micro-resistance welded NiTi wire joint reported in previous studies. Furthermore, the cyclic tensile test revealed that the accumulated residual strain of NiTi wire was reduced after welding. This could be attributed to the reduction in the dislocation density through grain growth in the HAZ of NiTi wire during the welding process.

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 score0.002

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 science0.0010.000
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 head0.013
GPT teacher head0.252
Teacher spread0.240 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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