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Dissimilar laser welding of NiTi shape memory alloy and copper

2015· article· en· W2272842848 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

VenueSmart Materials and Structures · 2015
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Centres of Excellence
KeywordsMaterials scienceWeldingNickel titaniumShape-memory alloyCopperCrackingMetallurgyLaser beam weldingComposite materialFracture (geology)Joint (building)Electric resistance weldingStructural engineering

Abstract

fetched live from OpenAlex

This work is the first investigation of joining NiTi and copper. The successful Nd:YAG laser welding of NiTi to copper achieved in this work enables new methods of connecting shape memory alloys to electro-mechanical systems. Joints made with an optimum peak power of 2.2 kW accommodated pseudoelastic deformation of NiTi, proving their use with high strength actuators. Fracture occurred through the cross section of these defect-free joints. A lower peak power of 1.8 kW created weak joints with limited weld penetration of the copper sheet. This lack of bonding resulted in fracture occurring across the small disconnected joint areas. Joints made with a higher peak power of 2.6 kW had significant cracking in the fusion zone. Two regions of distinct Cu composition were found in the fusion zone, and cracking occurred at the interface between these regions because of their different physical properties. Failure initiated at this cracking and propagated through the fusion zone that had been embrittled by mixing with over 20 at.% Cu.

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 categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score1.000

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)0.0010.000

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 head0.025
GPT teacher head0.253
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