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Record W2357794234

Laser spot welding of SPCC steel and NdFeB magnet

2008· article· en· W2357794234 on OpenAlexaff
Y. Zhou

Bibliographic record

VenueJournal of Tsinghua University(Science and Technology) · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNeodymium magnetWeldingMetallurgyMagnetSpot weldingJoint (building)BrittlenessLaser beam weldingElectric resistance weldingHeat-affected zoneWelding jointComposite materialFracture (geology)Mechanical engineeringStructural engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

Mechanical fastening or adhesive bonding methods are generally used to join a NdFeB permanent magnet to a steel component, while no research has been reported on the welding process to join magnet to steel. In this paper, laser spot welding was used to join a NdFeB permanent magnet to an SPCC (steel plate cold commercial) steel in an effort to achieve joining of magnet/steel dissimilar materials with high speed and quality, with the joint formation mechanism, hardness, strength, and fracture behavior analyzed. The results show that during the welding process, the two base metals quickly melt, mix and then solidify to form the weld that joins the two specimens together, but that the hardness in the joint is not uniform with the heat affected zone having lower hardness than the NdFeB base metal. Within the nugget, the region adjacent to the fusion line has the highest hardness while the middle part of the nugget has the lowest in the joint. The maximum fracture stress of the joint is about 75% the strength of the magnet. Hot cracks tend to occur at the interface between the nugget and the magnet base metal, whereat the cracks propagate and lead to joint failure during shear tests. The fracture is intergrannular which is a typical brittle fracture.

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 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 score0.034
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
Teacher spread0.151 · 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.

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
Published2008
Admission routes1
Has abstractyes

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